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ISSUE 3 2026

THE MAGAZINE FOR THE TECH PROFESSIONAL ARTIFICIAL INTELLIGENCE

BIG DATA

PRODUCTIVITY

WHERE AI IS WORKING

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ITNOW | Issue 3 2026

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CONTENTS Where AI is working Artificial intelligence has moved beyond experimentation and hype to become a practical tool delivering measurable value across organisations of every size. From streamlining operations and improving decision making to enhancing customer experiences and accelerating innovation, AI is increasingly proving its worth in the real world. The conversation is no longer centred on what AI might achieve in the future, but where it is already making a difference today.

Image credit: Adobe Stock - AI generated

In this edition of ITNOW, we explore the places where AI is creating tangible outcomes across industries, professions and public services. We examine how organisations are embedding AI into everyday processes, unlocking efficiencies, augmenting human expertise and solving complex challenges at scale. Speaking with leaders, practitioners and innovators, we uncover the lessons learned from deployment and the practical realities of implementation.

AI Assurance Stakeholder Consortium launched

The reality of autonomous AI cyber attacks

Why AI assurance must go beyond software testing

Developing business-critical AI safely

Public safety and the challenges for AI systems

25 years of BCSWomen: Past, present and future

Why most AI projects fail after the demo

How AI is reshaping threats, and the steps to reduce risk

The changing face of cyber security in the public sector

Turning innovation into better care for children

Taking a learning-orientated approach to cyber security

SMEs and the AI adoption gap

ITNOW | Issue 3 2026

IMPACT

GOVERNMENT LAUNCHES NEW CONSORTIUM TO BUILD TRUST IN AI AND GROW UK ASSURANCE MARKET

The government has launched a new expert consortium to strengthen trust in AI and help the UK seize the economic opportunities of responsible AI adoption.

he AI Assurance Stakeholder Consortium, convened by the Department for Science, Innovation and Technology and led by BCS, will bring together leading voices from across the UK’s AI assurance ecosystem to support the development of a recognised AI assurance profession.

doi:10.1093/itnow/bwag072

WHY AI ASSURANCE MATTERS AI assurance is the process of measuring, evaluating and communicating whether AI systems are trustworthy, safe, secure and working as intended. As AI becomes increasingly embedded across the economy and public services, independent and highquality assurance will be critical to giving the public, small and medium sized businesses, and private investors confidence in the technology. The UK already has strong foundations in AI assurance, with a growing market worth an estimated £1.01 billion in 2024 and which government analysis suggests could grow to £18.8 billion by 2035 if barriers to AI adoption are addressed. The consortium will support this growth by developing practical building blocks for a future AI assurance profession, including: • A voluntary professional code of ethics

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• A skills and competencies framework • Working to map information access requirements for AI assurance providers • Collaboration across sectors to raise the visibility, credibility and quality of AI assurance services The launch follows the government’s 2025 Roadmap to trusted third-party AI assurance, which sets out how the UK can build a world-leading AI assurance market, boost responsible AI adoption and support economic growth. Kanishka Narayan MP, Parliamentary Under-Secretary of State (Minister for AI and Online Safety), said: ‘If we want Britons from every walk of life to seize the benefits AI offers, they need to be able to trust it. AI assurance is going to be a massive part of our efforts to put AI to work boosting businesses, improving public services, and creating opportunities for people. ‘The UK already has all the ingredients needed to build a world-class AI assurance sector. This new consortium will spearhead those efforts. Together we can make the UK the most trusted place in the world to develop, deploy and assure AI.’ Emma McGuigan FBCS, Chair of the AI

Assurance Stakeholder Consortium, added: ‘AI assurance is no longer a niche issue, but is becoming essential infrastructure for an economy that wants to adopt AI confidently, responsibly and at scale. The consortium will focus on practical steps that help organisations understand what good AI assurance looks like, from professional ethics and skills, to the information needed to assess AI systems properly. ‘Our ambition is to support a trusted, credible and globally respected AI assurance profession that helps unlock innovation while protecting people, organisations and society.’ Sharron Gunn MBCS FCA, BCS CEO and Vice Chair of the AI Assurance Stakeholder Consortium, also said: ‘As a group, our duty is to create an AI assurance profession which is proud to adhere to a code of ethics and, ultimately, to hold world-class registrations and certifications. ‘Professional standards matter because they give the public and employers confidence that the people making critical decisions about technology have the right skills, judgement and accountability. This consortium is an important step towards building a

community of AI practice across the UK that is trustworthy, inclusive and ready to lead at a global level.’ Susan Taylor Martin, CEO of British Standards Institution (BSI), a member of the consortium, added: ‘As the AI transformation gathers pace, it’s critical that there is a way to differentiate credible AI governance from unchecked claims, supporting responsible innovation and paving the way for AI to be a force for good. BSI is delighted to be joining this consortium to build the AI assurance skills and competencies framework in a robust, coherent and consistent manner. It is through the use of international standards, assurance and the infrastructure around them that we can build much-needed confidence in a safe, secure AI ecosystem.’ Dame Wendy Hall, Regius Professor of Computer Science at the University of Southampton and member of the consortium said: ‘Trust is the foundation on which successful AI adoption will be built. The UK has world-leading expertise in computer science, standards, accreditation and professional ethics, but we need to bring that expertise together in a way that gives people confidence that AI systems are being developed and used responsibly. ‘This consortium is an important step towards making AI assurance a recognised and respected profession. By setting clear expectations for skills, conduct and accountability, we can help organisations innovate with confidence while ensuring that safety, transparency and public trust remain at the heart of AI deployment.’ Adam Leon Smith FBCS, Independent expert member of the consortium added: ‘As AI assurance continues to develop, it is vital that we build the skills and capacity needed to support safe, trustworthy and responsible adoption. The UK has a real opportunity to lead this work across Europe, and internationally... AI is a global technology, and effective assurance will depend on strong collaboration across borders.’ Peter Thompson CBE, CEO of the National Physical Laboratory, which

Image source: Adobe Stock

is establishing the UK’s Centre for AI Measurement, said: ‘AI assurance depends on trusted, scientifically robust ways of measuring whether systems are safe, secure and performing as intended… By embedding measurement science into AI assurance from the outset, we can help ensure that claims about AI safety, robustness and performance are tested consistently, independently and in ways that stand up to scrutiny. That will be essential if the UK is to move from broad principles around responsible AI to assurance methods that organisations, regulators and the public can rely on in practice.’ Sue Daley OBE, Director of Tech and Innovation at techUK, which heads the consortium’s industry advisory group added: ‘The UK's AI assurance ecosystem… is a market with the potential to grow six-fold over the next decade if market barriers are addressed… by working together through the consortium, we can build an AI assurance profession that is rigorous, inclusive and fit for the pace at which AI is developing.’

LOOKING AHEAD The consortium will be convened for an initial period of one year and will work closely with government and

stakeholders across the AI assurance ecosystem. Its work will support the government’s wider mission to boost AI adoption, drive economic growth and ensure the UK remains at the forefront of safe, responsible and innovative AI.

WHO ARE THE FOUNDING CONSORTIUM MEMBERS? BCS, The Chartered Institute for IT UK Accreditation Service (UKAS) British Standards Institution (BSI) National Physical Laboratory (NPL) Ada Lovelace Institute Chartered Quality Institute (CQI) Adam Leon Smith FBCS — independent expert Professor Dame Wendy Hall, DBE, FRS, FREng, DistFBCS, Regius Professor of Computer Science, University of Southampton — independent expert In addition to the Core consortium members there are a range of Observers and an Industry Advisory Group, led by techUK. A fuller version of this article is available on the BCS website. For further enquiries please get in touch with us at aiassurance@bcs.uk

ITNOW | Issue 3 2026

IMPACT

CELEBRATING THE LIFE OF MICHAEL GRANT FBCS Michael Grant FBCS, who passed away recently, is remembered dearly as a successful and pioneering IT professional and, more importantly, as a man of huge kindness and generosity.

ichael was a former BCS President, a highly respected tech professional, and, above all, a man universally remembered for his warmth and altruism.

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BCS extend our sincere condolences and good wishes to his family and friends as we remember his extraordinary and inspirational life. BCS President Sarah Winmill FRSA CITP CEng FBCS said: ‘Michael was deeply respected across our community, not just as a past president, but because he was unfailingly warm, good-humoured, and generous with his time and spirit. He had a way of making people feel welcome and valued, whether you knew him well or only in passing, and while we feel his loss keenly, we’re also celebrating his life and work. ‘What stays with me most is his unwavering commitment to making IT good for society. Only a couple of weeks ago, he reached out to invite me to an event with the WCIT Ethical and Spiritual Panel — a gesture entirely in keeping with his thoughtful and purposeful nature. I think he would have taken quiet satisfaction in knowing that he was contributing to something meaningful until his final days, and that is a legacy that will endure.’ Sharon Gunn MBCS BFP FCA, Group CEO of BCS, wrote: ‘So many of our members were inspired by Michael’s

ITNOW | Issue 3 2026

dedication and energy, as both BCS President and as Master of the Worshipful Company of Information Technologists. We were very fortunate that he chose to give his time to our community we'll be honouring his life and work in the right way.’

A CAREER WELL LIVED Michael Grant first entered the technology field in the flight simulation industry. He then spent 35 years working in a variety of international marketing and corporate communications roles at director and vice-president level within six IT companies: Gateway, Lotus Development, Commodore, ICL, Prime Computer and Wang. In March 2019, Michael was inaugurated as BCS President. By his own admission, however, Michael’s successful career was far from preordained. In an interview with ITNOW magazine in 2019, Michael described himself as a ‘child of charity’ and, recalling his early years, he said: ‘I lived for much of my life in a council house. My mother was a secretary and a widow. My father had been a corporal in the Royal Air Force. Really, my prospects were not too great until the Royal Air Force Benevolent Fund stepped in when I was seven-and-ahalf and I was sent away to a cathedral boarding school for the next 10 years.’

LESSONS IN LIFE AND BUSINESS Before information technology, Michael began his working career as a management trainee for Heinz. The experience, in his own words, was extraordinary. One of his early jobs was helping the Heinz family organise an annual event for 2,000 guests at Ascot. He got to meet the founder of Tesco, Jack Cohen, as well as Paul Getty, Princess Margaret and more. Perhaps more revealing of his true character, however, Michael recalled: ‘Each year, I’d get back to my desk and, as a very junior management trainee on my £12 a week, Mr Henry J Heinz would write me a personal handwritten thank you letter from Pittsburgh, the company’s global headquarters. It’s an attitude to reward and recognition which has stuck with me throughout my life. It was a great lesson.’

A REVOLUTION’S FOUNDATIONAL DAYS From there, as today’s IT industry began to take shape, Michael’s career grew in scope and became international. As it did, he rubbed shoulders with many of today’s household names. Speaking to ITNOW, for example, he recalled setting up an exhibition stand for his thenemployer, Prime Computer — the

inventors of super-minicomputers (the supercomputers of the day). ‘Steve Jobs was struggling to put his exhibition stand together’, Michael recalled. ‘I was quite good at that by then. So, I wandered across the corridor to shovel a couple of bits together for him, and at the end of the show, he came up to me and said: ‘I’d like you to come to my launch party for Apple, my company is Apple’, which, of course, I knew because I’d been helping put the Apple signs up.’ Looking back across his career, he said: ‘I had such an amazing time in the IT industry — far better than any dream I could ever have come up with. I just loved the international aspect of it and the fact that it didn’t matter two hoots where you came from.’ Michael’s wasn’t, however, a life untouched by reality or difficulty. Speaking in 2019, he said: ‘My son, James, has Down’s syndrome and I have a grandchild in the United States with a dual diagnosis for Down's syndrome and autism. Because I see, and I work with, a lot of people with disabilities, I’m very pleased that [the IT profession] is beginning to focus on designing products in a way that, right from the word go, are made for people from all age groups, from all types of mental ability or disability. That, to me, is the exciting change that has happened.’

MICHAEL’S IMPACT IN THE INDUSTRY Along with becoming BCS President in 2019, Michael also enjoyed prominent roles in many other volunteer-led organisations. Chief among these was the Worshipful Company of Information Technologists (WCIT). The WCIT is one of the City of London’s modern livery companies, which are historic membership organisations that support professional standards, charity and the civic life of the City of London. WCIT focuses on promoting excellence and ethics in information technology through education, professional development, and charitable work that benefits society. Michael was a Freeman and then a Liveryman of the WCIT. Later, he served

as the Company’s Clerk (the equivalent of CEO) from 2005 to 2010, and later as Master in 2012-13.

and generosity, I am saddened by his passing and honoured to have known him.’

Speaking about an industry he clearly loved, Michael said: ‘There’s a great pride in being a professional, knowing you’re a professional and knowing that you’ve earned your professionalism.’

Julia Adamson MBCS FBCS, Executive Director (Education & Public Benefit), said: ‘Michael was such a kind and thoughtful leader, always curious, always encouraging. He will be deeply missed.’

OTHER TRIBUTES

Chris Rees, a former BCS president, said: ‘Michael was the soul of courtesy, always supportive of me when I was president, a great enthusiast for BCS, as well as the Worshipful Company of Information Technologists. He was surprised and delighted to be elected deputy president and then president, roles in which he served with distinction. He was a true friend, and will be sorely missed.’

As we published this celebration, BCS members continued to come forward with their own tributes to Michael Grant: David Sutton wrote: ‘I first met Michael at a technology conference in Brighton in 2002. Michael was a truly remarkable man. His career included many of the world’s most successful IT organisations, arranging events with an incredible flair, imagination and with budgets to match. In 2024, he began writing The Life of Grant, documenting his career with great candour and humour — a life lived fully (https://tinyurl.com/4bu9pva3). I shall always remember him for his kindness

Trying to sum up a life in just one word is generally a fruitless task. Lives are too big, and his certainly was. In Michael Grant’s case, it may, however, just be possible: Michael was a gentleman.

ITNOW | Issue 3 2026

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WHAT IS ANTHROPIC’S CLAUDE MYTHOS? THE REALITY OF AUTONOMOUS AI CYBER ATTACKS Cyber expert Richard Benham FBCS speaks to Martin Cooper MBCS to break down the security risks of autonomous AI vulnerability testing and Project Glasswing.

nthropic has released its muchpublicised Claude Mythos, a highly advanced frontier AI model capable of autonomously discovering and testing software vulnerabilities at an unprecedented scale. And it is reportedly worryingly effective. Because of the model’s potential as a tool for good and also for cybercrime, Anthropic also launched Project Glasswing — a defensive initiative granting major tech firms and governments early access to Mythos. The move was made to allow them time to consider and react.

doi:10.1093/itnow/bwag074

Why don't you introduce yourself and tell us a bit about your career. I've been around for decades in cyber and now AI; it seems the world's just catching up with me. Until 2008, I worked in banking and spent over a decade advising the police. I found

academia at the back end of that, coming up with an idea in 2010; this word ‘cyber’ kept popping up, and businesses kept asking me, ‘do you have anything to help our IT departments and boards understand what this cyber attack thing is?’

and attack systems? Yes, and it's currently the preferred method of attack. Is it good enough to break systems? Of course, because global legacy vulnerabilities will always be there. It’s as good as the weakness on the other side.

Thinking about autonomous AI cyber attacks. What's your take on Anthropic’s Mythos?

While Anthropic appeared to act honourably by offering this to defend against it, our adversaries and criminals won't do that. They are almost certainly evolving versions to attack us. We just need to be prepared and have our defences in place.

I think it's just the evolution in what's coming. A few years ago, I explored how AI could be used as a cyber offensive tool. The big challenge is that you can get AI to write AI, which can then go off and do its own thing. This worries people because you effectively lose control and trust. All that Anthropic has done is productise that capability, give it a name, and use clever marketing. Others will follow, just as ChatGPT and Grok have. Can AI be used to find vulnerabilities

'THE BALANCE OF POWER IS ALWAYS WITH THE CRIMINALS FOR ONE SIMPLE REASON: THEY DON'T HAVE TO OBEY LAWS OR RULES; THEY JUST HAVE THE OBJECTIVE IN MIND.'

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And how about Anthropic’s Project Glasswing? Well, I think there are other things at play here; [governments and big tech firms] weren't surprised. It’s commercial interest versus public good. Anthropic made a commercial decision because, as much as it finds vulnerabilities, it can be used for good reasons, and they know people will copy it. Years ago, I came up with this ethos of AI angels and AI devils in cyberspace. That's where we're going: AI devils creating mayhem and AI angels battling

Image source: Adobe Stock

them in a virtual war accelerated on both sides. On the face of it, Anthropic have been honourable, but many others are working on the same thing.

So, who holds the advantage, the attackers or the defenders? You only need to be breached once for a meltdown, but you have to defend all the time. The balance of power is always with the criminals for one simple reason: they don't have to obey laws or rules. They just have the objective in mind. When we put regulations in place, our opponents don't follow them. I've been slightly critical of recent reports stating ‘we will act in an honourable, lawful way’. While absolutely right, our adversaries don't play by our rules, giving them a constant advantage.

When it comes to autonomous AI vulnerability testing, Mythos is apparently very good at exploiting poorly defended systems. Does good

cyber hygiene still have a place in the AI world? For sure. If we look at the NHS, banks, or government organisations, their legacy IT infrastructure is so old that gaps are inevitable. This new capability highlights the need for fast investment and protection, so making it public is a very good thing. You have to ask why the whole system hasn't collapsed yet, and the answer is just the pace of change. The other massive threat is human vulnerability — fake news, phone calls, and videos. As humans, we want to trust people, but with current systems, how do you verify who you're talking to? The exploitation and manipulation of people is only going to get worse.

What keeps you awake at night? What do you really hope the bad guys won't do? Well, it's not just for cyber experts — our dependency on technology is so deep now that if the whole system

'MANY BOARDS STILL SEE IT AS AN IT RISK RATHER THAN A BUSINESS RISK, THINKING IT BELONGS TO THE IT GUY DOWN THE HALL... TREAT IT AS A BUSINESS RISK, NOT AN IT RISK.'

fails, we would go down in a matter of weeks. If the banks and electricity stop working, we will be almost back to the Middle Ages. What scares me most is that the government doesn't have a contingency plan for the entire system collapsing at once. The only positive example is COVID, where civilisation was put on hold, and we coped really well. But without banks or food, it's a different story. My biggest worry is a fatal cyber attack on the United Kingdom that we just can't recover from, forcing us to start all over again.

Finally, you advise lots of boards. If they could only remember one thing you’ve said, what would you want it to be? Well, for boards, it's about realising this is your problem. Many boards still see [AI] as an IT risk rather than a business risk, thinking it belongs to the IT guy down the hall. We’ve been pumping this message out across the sector for years: treat it as a business risk, not an IT risk. I’m still forced to say it because for most people on the board, it feels like a risk they can't control or don't fully understand, so it's too much to grasp.

ITNOW | Issue 3 2026

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AGENTIC AI IS HERE: HOW TO GET YOUR DATABASE READY Chaitanya Kulkarni FBCS explores how reasoning systems place different requirements on databases, and how we can prepare our information storage systems to be ready for agentic workloads.

doi:10.1093/itnow/bwag075

ver the past year, I’ve noticed a fundamental shift in the conversations I have with software architects, developers and IT leaders. A year ago, almost every discussion about artiﬁcial intelligence revolved entirely around large language models. The questions were predictable and focused heavily on capability: which model performs best on benchmarks? How accurate is it for speciﬁc tasks? Which provider should we choose for our upcoming project? Today, the questions landing on my desk are entirely different. While organisations still care about the underlying models, they are increasingly preoccupied with a much more practical challenge: how those models connect to the operational systems that run their business. They want to know how an AI agent can retrieve live, dynamic information, interact safely with enterprise applications and make decisions using trusted internal data rather than static training material. In other words, the conversation has moved rapidly from ‘which model?’ to ‘how does it work in the real world?’ From my perspective, that’s precisely where the truly interesting architectural challenges begin.

AI AGENTS BEHAVE DIFFERENTLY To understand why this shift is happening, we have to look at how our data infrastructure was built. Most

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enterprise databases were designed to support traditional applications with highly predictable behaviour. These applications issue specific requests, receive structured responses and complete well-defined transactions. Even analytical workloads, while heavier, generally follow wellunderstood queries and patterns.

making. We have seen this cycle play out multiple times over the last few decades. Virtualisation changed how we designed storage networks, the cloud completely transformed how we thought about systemic resilience, and containers forced us to reinvent our entire approach to software deployment.

AI agents don’t behave this way at all. An agent might retrieve a piece of information, evaluate it against a goal, ask a follow-up question based on that evaluation, compare the two answers, call an external business tool, and then return to the database several more times before finally deciding what to do next. This iterative looping isn’t a flaw in the system; it is simply how computational reasoning systems operate. The consequence for IT leaders is significant: databases are no longer supporting only human users and predictable applications. They are increasingly supporting autonomous software capable of making its own independent decisions about what information it needs next. This represents a profound shift in data demand.

I believe agentic AI is beginning to do something very similar for enterprise data platforms. For years, the industry focus has been on making databases faster, more resilient and easier to operate at scale. While those fundamental objectives remain critically important, AI introduces additional, complex considerations into the mix. Architects must now ask themselves: can the database provide enough rich context for an agent to make sensible, autonomous decisions? Can multiple independent agents safely access and lock information at the same time without causing deadlocks? Perhaps most crucially, can we audit the data path to understand why an agent performed a particular action? These questions rarely arose in database design discussions a few years ago, but increasingly, they are becoming central to modern architecture planning.

THE DATABASE IS BECOMING PART OF THE AI CONVERSATION One lesson I’ve learned throughout my career in technology is that infrastructure rarely attracts attention until a new type of workload exposes assumptions we didn’t know we were

CONNECTIVITY MATTERS MORE THAN EVER When it comes to implementation, one of the biggest practical hurdles isn’t the sophisticated model itself, but the messy reality of connecting that

model to legacy enterprise systems. To solve this, many organisations are currently experimenting with the model context protocol (MCP), an emerging open standard that gives AI agents a consistent, secure way to discover available tools and access underlying data. The technical protocol itself isn’t the most interesting part of this trend; what’s fascinating is what it enables. Instead of building a bespoke, custom integration for every single AI application or use case, organisations can begin exposing their data capabilities in a unified, standard way. This dramatically reduces architectural complexity and makes it significantly easier to develop advanced systems that combine multiple disparate data sources and business services. Whether MCP ultimately becomes the dominant industry standard is less important than the broader direction of travel it represents. Enterprise AI fundamentally needs standard, secure and highly observable ways to interact with operational data systems.

PERFORMANCE IS ONLY PART OF THE STORY When people think about databases, performance is usually the first topic that comes to mind, and it certainly matters here. If an agent performs dozens of micro-interactions with a data layer while completing a single human task, latency issues will compound quickly. However, I suspect that data governance will ultimately become equally, if not more, important than raw speed. As AI agents gain more autonomy to act on behalf of the business, organisations will need absolute confidence that they can answer straightforward operational questions. If a system fails or behaves unexpectedly, leaders will demand to know why an agent made that specific recommendation. What

Image source: Adobe Stock

exact information influenced its final decision? Which data or systems did it access? Could the same decision be reliably reproduced tomorrow given the same data state? These aren’t really AI questions; they are timeless questions about operational trust, compliance and enterprise accountability.

SO HOW DO YOU PREPARE? The good news is that I don't believe organisations need to panic and replace perfectly good, stable databases just because they are experimenting with agentic AI. Most of the preparation work is likely to involve asking different questions of your existing setup rather than buying entirely different technology. As you audit your readiness, focus the conversation on core data management principles: can our current data platform expose trusted information securely to non-human actors? Do we have visibility to understand exactly what data our AI systems are accessing in real time? Can we observe and log the complex interactions between

'AS AI AGENTS GAIN MORE AUTONOMY TO ACT ON BEHALF OF THE BUSINESS, ORGANISATIONS WILL NEED ABSOLUTE CONFIDENCE THAT THEY CAN ANSWER STRAIGHTFORWARD OPERATIONAL QUESTIONS.'

autonomous agents and enterprise systems? And finally, are governance, access control and auditing built into the fabric of the architecture from the very beginning, rather than treated as an afterthought to be added later? These are conversations worth having right now, well before these AI workloads become business critical.

LOOKING AHEAD Technology rarely changes as abruptly or dramatically as tomorrow's headlines suggest. In my experience, lasting change usually happens much more quietly. Architectures gradually evolve, operational practices improve, new standards emerge, and eventually, those hard-fought changes simply become the new normal. We are at the very beginning of that normalisation process with agentic AI. The initial wave of excitement around large language models naturally focused public attention on what the models themselves could do. Over the next few years, I believe we will spend just as much time thinking about the unglamorous infrastructure that allows those models to work. For database professionals, this shift isn't something to worry about. It is a remarkable opportunity to rethink how one of the most important parts of enterprise IT continues to evolve.

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WHEN YOUR NEWEST ANALYST IS AN AGENT Reena Pathak, Director of Cyber Security, shares the leadership lessons from guiding a security team through the arrival of generative and agentic AI — and explains why real transformation is messy, iterative and human.

ike most security teams, we began with an abundance of everything except clarity: tools, detections, alerts and noise. Our architecture was fragmented, procurement decisions were disconnected, and we had no clear view of how our controls interconnected. The security operations centre (SOC) carried a high false-positive rate, which translated directly into analyst burnout and skilled people doing low-value work. I was convinced that time saved here would pay back across the whole of cyber defence — freeing capacity for proactive threat hunting and the strategic work only humans can do.

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That conviction set us on the road to generative and, eventually, agentic AI. What follows is not a technical blueprint for building agents; those resources already exist. It is a leadership account of the planning, enablement and cultural challenges that vendor blogs rarely mention, distilled into a handful of lessons for security leaders who are just beginning, midway through, or planning their own AI journey.

TOOLS WITHOUT WORKFLOWS ARE JUST NOISE Our first attempt was humbling. We took a security-specific generative AI assistant and dropped it into the environment to help with repetitive

tasks: summarising incident tickets, drafting reports, analysing threat intelligence. The results were, frankly, ‘meh’. We had introduced the tool in a stand-alone fashion, without embedding it in workflows, without baselines and without clear guidelines. Usage became inconsistent and entirely dependent on individual analyst preference. Vendors market these tools as plugand-play. In practice they demand a deep understanding of your data flows, process gaps, data quality and organisational context — none of which comes in the box. The lesson I now repeat to anyone who will listen: start by asking what problem AI is solving, not what technology is available. The turning point came when we aimed the assistant at a specific, painful target: our detection rule base. Rules were inconsistently named, poorly governed, hard to search and barely mapped to MITRE ATT&CK. Using the assistant, we accelerated our migration to detection-as-code, improving both efficiency and code quality far faster than manual effort alone could have. A vague tool became a valuable one the moment it had a real problem to solve.

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Agents perform poorly in environments with poor data, broken processes or missing context. Before we built our detection engineering agent, we used the assistant to clean and standardise the rule base — an unglamorous prerequisite that made everything afterwards possible. The richer the context we gave an agent, the more accurate and useful its outputs became.

FOUNDATIONS BEFORE FRONTIER

The single biggest success factor, though, was something simpler: protected time. Rather than treating agent development as a side-of-desk activity squeezed between operational duties, we dedicated two people fulltime to designing and building our custom systems. Freed from competing priorities, they could focus on the design, the outcomes and the relentless iteration that quality demands. That decision, more than any tool, is what produced agents we could trust.

We talked a lot about becoming a

AGENTS ARE TEAM MATES

'THE MOST PROFOUND SHIFT IN OUR THINKING CAME WHEN WE STOPPED TREATING AGENTS AS TOOLS AND STARTED TREATING THEM AS TEAM MEMBERS.'

‘frontier organisation’ — one that moves beyond basic AI tools to embed advanced, agentic systems deeply. My view has not changed: frontier status is an aspiration, not a switch you flip. You cannot leap from a traditional model straight to the frontier. You first have to build the foundation of strategic vision, clean data, mature processes and human readiness.

The most profound shift in our thinking came when we stopped treating agents as tools and started treating them as team members. They generate content, make decisions, execute logic continuously and interact with one another — much like humans on shift.

Image source: Adobe Stock

Once we saw them that way, everything changed. We now give our agents ‘job descriptions’ with clear objectives, defined collaboration points and access controls. We onboard them the way we onboard a new hire: coaching, feeding context, correcting mistakes and building trust gradually rather than assuming it from day one. When we upgrade a model, it feels like a promotion — the agent suddenly has new capabilities, which prompts us to revisit its permissions, role and place in the architecture. We built three systems on this principle, each designed around human-in-the-loop oversight rather than full autonomy. An incident analysis system performs initial triage, deep analysis across data sources, limited threat hunting and structured recommendations for human analysts. A detection engineering agent manages the full rule lifecycle — validation, optimisation, overlap checks, ATT&CK mapping and threat-intelligence integration — and analysts can simply converse with it. A threat intelligence agent ingests and operationalises intelligence, generates hypotheses, runs targeted hunts and creates tasks for our proactive teams, linking directly

to the other two. The improvements are not theoretical: less burnout, faster response and more time for strategic work.

HOW WE BUILT IT People often ask which model we used and how long we spent training it — and the honest answer tends to surprise them. We took a capable, generalpurpose large language model with strong agentic and tool-use abilities and shaped it: a tightly defined job, a small set of tools, and a clear set of rules. The intelligence was, in effect, off the shelf; the value we added was context and constraint. For most security teams that is the real unlock — you are not hiring data scientists to train algorithms, you are configuring and guiding a capable general system to reason the way your best analysts do. That clarity also drove the buildversus-buy decision. The market is full of vendors promising an ‘AI SOC analyst’ in a box, but a bought product gives you someone else’s judgement. We built our own because an agent that sits inside your defence team has to understand your environment, your playbooks and your risk appetite — and, as the rule-based work taught us, that context is the whole point.

So ‘training’ meant something closer to coaching, just analysts walking each agent through real, sanitised scenarios, correcting its reasoning and tightening its instructions until its judgement held up. The timescale is worth stating plainly for anyone planning their own: we had useful output within a few weeks, but earning the team’s confidence took a couple of months of running the agents in parallel with humans and watching the gap close. Like any good hire, an agent embedded in a live team is a relationship to maintain, not a project to close.

GETTING COMFORTABLE The elephant in the room is job anxiety. Even as a leader, I felt the tension: if agents handle the routine work, what happens to roles? My honest answer to the team was that AI will not take your job — but someone who uses AI well might. The work is evolving towards the more nuanced, complex and collaborative, including industry-wide threat collaboration and societal-level defence. We addressed this head-on with a dedicated ‘People Workstream’ covering governance, assurance, job-description updates and timereallocation planning. But comfort was

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Image source: Adobe Stock

not only a people problem; it was an engineering one too. As the accountable leader, I had to be personally confident the agents were safe, auditable and properly controlled before they could make decisions in live workflows. That meant multi-layered controls: authentication — especially for agent APIs — identity management for interagent and agent-to-tool communication, governance of secrets and tokens, content filtering to mitigate prompt injection and jailbreaks, and defence-in-depth authorisation backed by role-based access controls. We added independent verification and validation: deterministic pre-checks before any AI-driven decision, confidence scoring on machinegenerated detections, conservative downgrade logic, and purple-teaming to test that the controls held against real threat behaviours. For high-risk actions, such as call-out recommendations, we required documented rationale, parallel running with humans and shift-handover summaries of agent decisions. We treated trust like onboarding: gradual, evidence-based and never assumed.

PROVING THE VALUE AI enablement has to deliver a measurable return. We kept our traditional SOC metrics — triage time, mean time to respond, false-positive rates — and added agent-specific ones: detection coverage gaps closed, intelligence-toaction time, manual hours saved, and unnecessary call-outs avoided, meaning the times the system correctly decided not to wake a human for a false positive. We also track employee engagement and workload, because reducing burnout was the whole point. Costs — prompt usage, tooling, engineering time — are tracked against those gains so we can see return on investment holistically rather than as a vanity metric.

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WHAT COMES NEXT We are still early. Agent-to-agent interactions, long-term role evolution and — soberingly — the parallel development of adversarial agents by threat actors all remain open questions. In regulated, critical national infrastructure environments, assurance expectations around transparency, auditability and accountability are only tightening. We will need new layers of purple-teaming for an AI-versus-AI world, continuous career-path redesign so that people are not deskilled, and mature cost governance so that savings are reinvested in strategy rather than quietly absorbed as headcount cuts. The principles, I suspect, will stay constant across this and the next technology revolution: start with the problem, build the foundation, treat your agents as team mates you remain accountable for, and care for your people first. This is my ‘ithihaasa’ — a Sanskrit word meaning ‘that is how it happened’. Every journey will be different, but I hope ours helps other leaders navigate theirs with a little more clarity, confidence and humanity. Reena Pathak, Director of Cyber Security, works in a critical national infrastructure environment. The opinions expressed are entirely the author’s own and do not reflect the views, policies or practices of any current or former employer. All examples, metrics and technical details have been generalised and anonymised for illustrative purposes.

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BEYOND DEEPFAKES: WHY CONTENT PROVENANCE IS THE NEW INFRASTRUCTURE FOR DIGITAL TRUST John Collomosse FBCS, Senior Principal Scientist at Adobe, tells Martin Cooper MBCS about C2PA, a tamper-evident metadata standard, that’s looking to embed an image’s origin story into the picture itself.

eep fakes, misinformation, synthetic media, counterfeits — whatever you choose to call what’s not real, digital deception has reached crisis point online. And as AI tools become more powerful, prevalent and easier to use the problem only gets worse.

attribution and fairer compensation across the internet.

Why don’t you introduce yourself and tell us a little about your career?

My career began in computer vision and AI research, but has increasingly focused on trust in the digital supply chain: how we know where data and media came from, how they may have changed, and how rightsholders can

'GENERATIVE AI IS EMPOWERING CREATORS, BUT ALSO BRINGS RISKS. FAKE NEWS AND MISINFORMATION ARE MAJOR SOCIETAL CHALLENGES, AND TRAINING AI USING CREATIVE WORK HAS RAISED URGENT QUESTIONS AROUND CONSENT AND COMPENSATION.'

Generative AI is empowering creators, but also brings risks. Fake news and misinformation are major societal challenges, and training AI using creative work has raised urgent questions around consent and compensation. We believe content provenance can help address both of these risks. Content provenance is the story of how a piece of digital content was made. A photographer might take a snap, retouch it, and pass it to an editor who crops it for social media before a publisher releases it online. Provenance is a way of recording that history so that people and systems can understand where the content came from and what has happened to it as it passes through such a digital supply chain.

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doi:10.1093/itnow/bwag077

Far beyond just fighting fake news, this decentralised infrastructure can also support creator consent, attribution and licensing. In the future, it could provide a shared foundation for online trust, whilst also helping creators gain better

The solution may reside in content provenance: a tamper-evident digital ‘nutrition label’ inserted directly into media files. By fusing secure metadata with invisible watermarking and cryptographic fingerprinting, this technology survives social media stripping to map an unalterable history of how content was created.

I am Professor of AI at the University of Surrey and Senior Principal Scientist at Adobe Research, where I lead Adobe’s content authenticity research programme. At Surrey, I founded and direct DECaDE, the UKRI Centre for the Decentralised Digital Economy (https://tinyurl.com/8666yb8d), where we research how emerging technologies can improve trust, data agency and value exchange in the creative industries.

retain agency as their work moves through online platforms and AI systems. This matters not only for the creative industries, but for data governance and integrity in AI more generally.

You can think of it like a digital nutrition label. We care about the provenance of our food: where it came from, and how it has been processed — why not our news? Knowing whether an image came from a camera, was edited or generated by AI, or re-used from an earlier source gives us context to make informed trust decisions about that content.

Surely spotting deepfakes is a task that AI should excel at? A deepfake is AI content that has been created with intent to deceive, but a computer can’t detect intent — it can only detect AI use. AI detection tools are valuable in some instances, but their results can be unreliable and will always be in an arms race against bad actors. Detection itself is becoming a weaker signal because most content is now touched by AI. Even basic image operations such as upscaling and filling use AI! There is also nothing inherently deceptive about AI-generated media. Many people use AI to create and tell authentic stories. Simply labelling content as ‘AI’ tells us less and less. Conversely, human rights organisations like WITNESS have reported (https://tinyurl.com/yedp93fz) that most visual misinformation is not AI, or even edited, at all. It often involves unaltered content that has been misattributed to tell a false narrative. That is why provenance is so important. Instead of trying to classify content simply as ‘real’ or ‘fake’, or ‘AI’, provenance describes how content was made, edited and shared. It gives people the context they need to make up their own minds.

How did you get interested in

provenance? Was there a moment when you thought: ‘I have to solve this’? 10 years ago, I led an academic project called ARCHANGEL (https://tinyurl.com/4vpw4x8b) with The National Archives and the Open Data Institute. It used provenance to underwrite the integrity of born-digital media, such as video records from the UK Supreme Court. The project recorded provenance information on a blockchain: a shared, tamperevident database maintained by multiple archives: the UK, Estonian, Norwegian, Australian and US National Archives (NARA) were involved in the trial. The aim was to enable members of the public to verify the provenance of digital records when they were released. We later extended the prototype into the news domain through the open-source ‘Angel’s Wing’ browser extension, which searched for provenance records associated with photographs on the web. That work made it clear that provenance was going to be important. Digital media no longer comes only from institutional sources such as broadcasters or archives. It now comes from a decentralised ecosystem of creators. Trust can no longer rely only on the reputation of the institution that publishes something. We need technologies to help people understand the history of the content itself. When I later joined Adobe, several of us saw provenance as a key part of the answer. That thinking led us to create the Content Authenticity Initiative (CAI) in 2019, which today has more than 6,000 members working hard to make content provenance practical at internet scale. A major part of the CAI’s work has been to found and advocate for the Coalition for Content Provenance and

'THE IDEA IS THAT C2PA ALLOWS GOOD ACTORS TO PROVE THE AUTHENTICITY OF THEIR CONTENT. SIGNING CONTENT IS A WAY FOR CREATORS TO ASSERT AUTHORSHIP AND GAIN ATTRIBUTION AS THEIR WORK MOVES THROUGH THE DIGITAL SUPPLY CHAIN.'

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Authenticity (C2PA) open standard. In simple terms, C2PA records provenance information in the metadata of digital content, so that the history of a file can travel with it.

Take us under the bonnet; how does C2PA work? How is it different from, say, EXIF metadata? What is a ‘manifest’? A manifest is the metadata structure that C2PA uses to store provenance information. It contains provenance facts called assertions. EXIF can record information such as camera settings or location, but it is easy to modify. C2PA is designed to be tamper-evident. Its manifest is signed and sealed using public key infrastructure, or PKI, so people and systems can check who or what is attesting to those facts and whether they have been altered. Assertions can record who created or published content, what actions were performed, and which other assets were used in its creation. Those source assets, called ingredients, can have their own manifests too, so forming a graph describing the history of the asset. The manifests are bound together with hashes, including a hash of the content itself, so tampering becomes evident. The idea is that C2PA allows good actors to prove the authenticity of their content. If a politician, celebrity or news organisation routinely signs their content, then fake content purporting to be from them will not carry their signature. Signing content is also a way for creators to assert authorship and gain attribution as their work moves through the digital supply chain.

Who is using C2PA? Where is it making a difference right now? Adoption is growing quickly. Many generative AI platforms write C2PA into generated content to show that AI was used, partly driven by legislation in jurisdictions such as the EU and California. Several camera manufacturers, including Leica, Sony

Image source: Adobe Stock

and Canon, have adopted C2PA in models aimed at photojournalism, and recent Google Pixel phones also support it. This lets users show their photographs and footage are real. Software products are also starting to read and write C2PA metadata, including Adobe’s creative tools, such as Photoshop, Lightroom and Premiere. Much early adoption has focused on still images, but there are good examples in video too, including France Télévisions signing daily news output and the BBC’s prototype at the IBC conference. LinkedIn surfaces provenance information when C2PA is present in posts, and Google recently announced plans to bring C2PA into search and browsers. That matters because provenance becomes more useful when people can see it where they encounter content. Still, many platforms, particularly social media, strip away

C2PA metadata and break the content supply chain.

How can this scale up if platforms strip metadata? The solution is to embed an invisible watermark in the content to make provenance metadata more ‘sticky’. The watermark uniquely identifies the content, enabling systems to recover stripped metadata from the cloud. We developed a watermark called TrustMark, designed to survive transformations applied by social platforms. We open-sourced TrustMark to promote interoperability and adoption, although this brings security challenges: someone might transfer (spoof) a watermark onto different content. That is why watermarking is combined with content fingerprinting, which helps verify that the watermarked image really matches the metadata recovered for it. One of

'BCS FELLOWSHIP IS A RECOGNISED MARK OF PROFESSIONALISM, BUT IT IS ALSO ABOUT BELONGING TO A WIDER COMMUNITY... I HAVE FOUND THAT PROFESSIONAL STANDING PARTICULARLY VALUABLE AS MY WORK HAS INCREASINGLY INVOLVED POLICYMAKERS AND STANDARDS ORGANISATIONS.'

our research breakthroughs was to combine three pillars of provenance — metadata, watermarking and fingerprinting — together to improve the durability of provenance. The threepillar approach is now widely adopted, including in the Code of Practice for the EU AI Act (https://tinyurl.com/mwa5avve).

Finally, you’re a BCS Fellow — what advice do you have for somebody thinking about applying? BCS Fellowship is a recognised mark of professionalism, but it is also about belonging to a wider community. I joined BCS as a computer science undergraduate, later became a Chartered Engineer through BCS, and then became a Fellow. I have found that professional standing particularly valuable as my work has increasingly involved policymakers and standards organisations. The regular computer vision meetings held at BCS with the British Machine Vision Association have also helped maintain the visibility of the UK AI research community. I would certainly encourage people to apply when they feel they have built a strong case: it recognises your professional contribution while supporting the discipline more broadly.

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WHY AI ASSURANCE MUST GO DEEPER THAN SOFTWARE TESTING Emma McGuigan FBCS and Martin Cooper MBCS explore why continuous assurance is the only way to keep AI safe and stop tech creators marking their own homework.

So, let's start with making AI that’s transparent and explainable. Why are these things so important and so hard to do?

belong in comic books, but now we are seriously looking at what it takes to get there — combining data, quantum capabilities and biological assessments.

Beyond the industry buzzwords, securing fundamental pillars like explainability, fairness, safety and privacy is far more challenging than it first appears.

AI has been used in medicine for decades, but what is exciting now is its impact on diagnosis. While a top consultant relies on their training and hundreds of past cases, an AI engine can analyse tens of thousands of similar cases, delivering a highly accurate, well-documented diagnosis.

But how will we ever have faith in that future when we cannot even trust how a credit limit was set on [credit] cards? If we cannot build confidence in something that could exist within a decision tree, how will we ever trust richer, more complex solutions that analyse far greater complexities to find their patterns?

n this edition of Insight Exchange (https://tinyurl.com/nxn9ak55), BCS Editor-in-Chief Martin Cooper sits down with Emma McGuigan, the BCS’s AI programme lead, to untangle the complex reality of responsible AI (https://tinyurl.com/29hbj84d).

As a passionate proponent of the technology, Emma argues that robust, independent AI assurance is now the only way to ensure these powerful systems genuinely do no harm.

doi:10.1093/itnow/bwag078

Why don’t you introduce yourself? I’m a 30-year-plus veteran in the IT sector. I spent many years with Accenture as a developer and architect before moving into global leadership roles. More recently, I've been working as a consultant with BCS to help shape and support our AI program, which is incredibly exciting. I also act as an independent advisor for several other organisations.

But as humans, we do not just want the diagnosis… we want to know it is fair, right, and leads to the best health outcome. Building that confidence is difficult. Traditional IT systems were deterministic and rule-based; we rigorously tested them and understood their logic. With deep learning models, there are no rules, only statistical patterns across millions of parameters. This is not human-readable logic. How do we build confidence when there are no clear, linear interactions?

And how about AI explainability? Think about where our ‘tech bro’ colleagues are taking us: towards super-intelligence. This term used to

'IF A TEACHER INTRODUCES A BIAS TO A CLASSROOM, ONLY THOSE STUDENTS ARE AFFECTED; IF YOU ADD BIAS TO AN AI ENGINE HANDLING THOUSANDS OF DAILY QUERIES, THE IMPACT IS MASSIVE.’

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Let's talk about fairness and inclusivity. I think we can all agree we want AI to be fair and inclusive… If a teacher introduces a bias to a classroom, only those students are affected; if you add bias to an AI engine handling thousands of daily queries, the impact is massive. A classic example is Amazon's 2018 hiring campaign. The algorithm was trained on their existing, predominantly male workforce, meaning CVs from different profiles were filtered out. Without focusing on the training data, the engine becomes a bias magnifier. These fairness issues are hard to detect because we often only realise the lack of inclusivity when the engine is used on a group omitted from the training data. How do we know what questions to ask to ensure a dataset is fair? Realworld data naturally carries real-world biases.

Image source: Adobe Stock

I mean, the AI engine can only be as good as the data it's been trained on. It's like all of us. We can only be as good at something as the people who have trained us to do it.

Where in an AI lifecycle are these issues hardest to detect?

that underlying bias is still at work.

Let's move on to safety and reliability. What's special or different about AI? Why is it hard to make reliable AI systems?

It comes back to that force magnifier. As an engine matures and operates for longer, it builds a certain credibility over time, but those biases remain baked in.

Traditional software is rule-based and deterministic. AI, however, is probabilistic and data-dependent; small input changes can produce unpredictable outputs, and models naturally degrade over time due to distribution shifts.

With traditional systems, the longer they existed, the more we came to believe they were correct and became dependent on them — which is why COBOL and Fortran systems still exist today. It is the same mentality: with age comes increased confidence in a solution, but the magnifying force of

This fundamentally changes the development life cycle. Traditional IT gave us confidence through rigorous, structured testing (unit, integration, system, performance, and user testing) against defined requirements. With AI, there is no finite test suite. The engine is constantly deployed into a

'DATA PRIVACY IN AI IS ENTIRELY DIFFERENT FROM THAT IN TRADITIONAL SYSTEMS. YOU CANNOT JUST TICK GDPR BOXES, PERFORM A TEST, AND CONSIDER THE JOB DONE, BECAUSE DATA IS THE CONTINUOUS FUEL FEEDING THE ENGINE, AND PRIVACY IS A CONSTANT, MAJOR OVERHEAD.'

live environment, where it continues to learn from new data, leading to small changes that can yield highly believable but completely incorrect answers. This is why having a human in the loop is so vital. Unlike traditional systems, where we just went back to a test cycle for new releases, AI requires human oversight. We need AI professionals who understand both the engine and the context in which it operates. In pharmacology, for instance, experts must verify that AI-generated drug designs are consistent with expectations, whilst remaining open to unexpected, positive breakthroughs that the AI's pattern-recognition has uncovered.

Who should be accountable? The human or the machine? Who should be accountable? Look at the Post Office Horizon scandal. It was a rule-based accounting system that had passed its tests and was assumed to be infallible. Yet, it took a devastating miscarriage of justice to remind us that people, not machines, are always accountable. Thirteen people lost their lives, and hundreds of families were ruined

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your personal data into a system without permission, but how often do we actually read the small print when accepting cookies or installing retail apps? We must decide whether reduced functionality is a price worth paying for privacy, whilst remaining mindful of the value of what we are giving up. Furthermore, major large language models are only being developed in the US and China. Even if a UK organisation uses its own customer data, if it is leveraging an overseas model, it faces entirely new challenges. How do you keep the model up to date whilst protecting your proprietary data, and how do you guarantee a user's right to exit? Navigating these jurisdictional and privacy issues represents a massive shift from traditional IT, and as professionals, we must have the confidence to answer them.

because of an IT system. We must never allow decision makers to hide behind the excuse of ‘the computer said so’. It is humans who sign off on these systems and make the final decisions. We use tools to help us, but the responsibility ultimately stops with us.

Why, where, and when should our AI makers consider privacy?

engine, you feed it more data, and these models can memorise that personal information. When Copilot or Claude recalls a question that you asked three days ago to answer you today, you get a clear insight into why privacy is a completely different beast now — especially if someone else has access to your device.

Data privacy in AI is entirely different from that in traditional systems. You cannot just tick GDPR boxes, perform a test, and consider the job done, because data is the continuous fuel feeding the engine, and privacy is a constant, major overhead.

Since UK privacy law guarantees the right to be forgotten, how can an individual realistically withdraw consent and demand their data back when that information is already woven into the very weights, nodes, and parameters of the trained AI model itself?

Crucially, privacy concerns do not stop at deployment. Every time you use the

These are the questions AI designers must answer. They cannot embed

‘I AM A BIG PROPONENT OF AI, BUT IT CANNOT BE LEFT UNPOLICED — ESPECIALLY BY THE PEOPLE WHO MAKE IT. WE CANNOT HAVE THE CREATORS MARKING THEIR OWN HOMEWORK. CONSUMERS MUST HAVE CONFIDENCE THAT THE ORGANISATIONS INTRODUCING THESE TOOLS ARE ACTIVELY MANAGING AND POLICING THEM.’

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Given that AI models are probabilistic and constantly learning, should we move away from day-one certifications and instead adopt a model of continuous assurance to ensure they behave responsibly and safely over time? Let us be clear: I am a big proponent of AI, but it cannot be left unpoliced — especially by the people who make it. We cannot have the creators marking their own homework. Consumers must have confidence that the organisations introducing these tools are actively managing and policing them. This is why AI assurance is key, and it must address three areas. First, AI professionals must be accredited and willing to sign their names to declare they are responsible practitioners. Second, we need continuous checks and assurance to ensure training data is thoughtfully collated and monitored as the system learns. Finally, the core engines themselves must be accredited. Thinking about assurance across these three pillars of people, data, and engines is absolutely vital.

Professional growth at every level Practical, business-focused learning that helps you advance r. and future-proof your career.

In partnership with the University of Cambridge, BCS brings world‑class professional learning to members through Cambridge Advance Online. Courses include: Generative AI in Business Leveraging Big Data for Business Intelligence Strategic Risk Management in the Age of AI BCS members receive an exclusive 20% discount. Visit bcs.org/cambridge

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HOW TO DEVELOP BUSINESS-CRITICAL AI SAFELY Andrew Leigh MBCS CITP, Software Development Group Head, and Dr Amie Vella, Head of Research with MASS, explore a framework for deploying mission critical AI.

ince the emergence of LLMs, the world has witnessed signiﬁcant hype about the potential for this kind of AI. Chatbots powered by LLMs are increasingly ubiquitous as they become embedded in everyday systems such as search engines and word processors. In the defence and national security domains, it’s no different; as general awareness of LLMs increases, customers, users and investors frequently ask how AI can be used in products and services. The added complexity, of course, is the highly secure, business- and mission-critical systems the industry requires.

MISSION-CRITICAL AI METHODOLOGY At MASS, we started by adopting an existing robust design methodology, such as the Design Council’s Double Diamond. This methodology includes four principle stages:

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DEFINE

DISCOVER

During this phase, we invited our customers to a hackathon to further explore and validate our initial ideas about the problems that AI could help solve. Whilst we began with a technology-agnostic exploration of user needs, we found that exposing users to working prototypes helped reveal additional requirements that would otherwise remain unarticulated. This is because people tend to think in terms of the constraints they already know due to confirmation and belief biases. When new technology comes along offering new possibilities, they need to see it to overcome those biases and understand the art of the possible.

The discovery phase recognises that we should start with the problem, not the solution. The MASS user experience (UX) team conducted an intensive, AI-agnostic study to identify pain points and opportunities, using techniques such as contextual enquiry (observation), desk research, workshops, surveys and interviews. We found user research to be an essential method for validating our initial ideas about the kinds of problems that AI might help to solve and the constraints that need to be taken into account when

To facilitate the hackathon, we built a pluggable architecture with a simple chat interface that allowed different models and inference engineers to be easily swapped in and out. This meant we could experiment with different combinations to see which worked best for each problem. Having a secure local LLM enabled us to experiment with real customer data, which helped participants engage more easily with the hackathon because they could validate results using recognised data. More significantly, allowing users

•

• It’s paramount that the output of the systems they work with can be trusted, given the unique challenges the defence industry faces — operating with sensitive data in highly secure environments being the main one. This presents a problem for the application of LLMs, because they always say yes confidently, which can be misleading when model confidence is not always a measure of information accuracy — independent research has found that the hallucination rate of some leading LLMs falls within a range of between 26% and 93% (https://artificialanalysis.ai/models) — and in business- and mission-critical systems, confident wrong answers are highly problematic. So, how can highly secure industries like defence apply this promising yet tricky technology in a trustworthy and safe way?

designing solutions. For example, the need for locally hosted, secure LLMs rather than cloud-based solutions stems from the highly sensitive nature of our customers’ data.

•

‘Discover’ explores the problem space to ensure all dimensions of the problem have been considered ‘Define’ converges on a clear definition of the problem to be solved and considers what success looks like ‘Develop’ explores the design and solution options that could be used to solve the problem ‘Deliver’ implements the solution that best solves the well-defined problem within the known constraints.

to be hands-on with the technology revealed that much of the risk is not just technical. System and software engineering are socio-technical disciplines because their success depends on the joint optimisation of systems and people. The nondeterministic and hallucinatory nature of LLMs amplifies this risk, a fact clearly on our customers' minds. AI changes not only system behaviour but also human behaviour; users may begin to over-rely on automated outputs or discount their own expertise, creating new operational risks that cannot be mitigated solely through technical controls. Image source: Adobe Stock

DEVELOP Drawing on insights from the discover and define stages, we gained the confidence to ideate on how LLMs could solve several problems our customers are facing. During the development stage, we returned to the discipline of user experience/user-centred design to explore options for LLM-enabled features through wireframes and prototypes. Users took part in usability testing of the candidate wireframes and prototypes. This offered further opportunity to surface the kinds of guardrails and affordances that users felt would be needed for AI-enabled features to be trusted, such as AI explainability and where to put humans in the loop.

CONCLUSION In summary, there are seven things to remember when looking at using AI for business and mission-critical systems:

Build confidence by experimenting with real data — deploy the technology into a system that can host real data so stakeholders can more easily validate results

Start with the problem, not with the technology — focus on user research

Confidence is a hidden danger of AI technology

Robust governance is needed to deliver AI solutions

Double down on approaching AI technologies as a socio-technical discipline — because the nondeterministic and hallucinating nature of AI amplifies sociotechnical risk

Collectively, these principles help ensure that AI is not only technically capable but also delivers safe solutions that are trusted, governable, and fit for use in business and mission-critical environments.

Discipline unlocks efficiency — stick to long-proven design methodologies such as the double diamond whilst practising user experience/user centred design, system/software architecture, and agile development in combination with data science and machine learning

DELIVERY Following the double diamond approach and conducting disciplined discovery, define, and develop stages has given us a clear vision for leveraging this promising technology. It has done so in ways that will solve customer problems whilst fostering trust in what is also a risky technology, owing to its nondeterministic and hallucinating nature, which can amplify the socio-technical risks. It has also revealed the skill sets, governance structures, human oversight, validation methodologies and quality procedures needed to deliver trustworthy, safe implementations in mission- and business-critical environments.

guardrails and affordances needed to foster trust in the AI-enabled system

Build an experimental system with a pluggable architecture so that different models can be evaluated for solving different problems and further elicit the necessary

Dr Andrew Leigh CITP MBCS is Software Development Group Head at MASS, leading software, product and research teams. A PhD-qualified software engineering leader with over 25 years' experience, he is an awardwinning innovator and Open University Associate Lecturer. Dr Amie Vella holds a PhD in Social Science and combines evidence-based research with industry expertise to understand how people, organisations and technology interact across defence and security.

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CAN BEHAVIOURAL AI IDENTIFY RISK WITHOUT IDENTIFYING PEOPLE? AI systems can identify individuals from images and video with a level of accuracy that would have seemed remarkable only a decade ago. Despite these advances, public safety remains one of the most difﬁcult challenges for AI. Preeti Patel, Professor and Co-Director of the AI and Data Science Research Group, London Metropolitan University, explores.

hether at festivals, sporting events, transport hubs, public demonstrations or large-scale religious pilgrimages, risks often emerge not because of who is present, but because of how people behave collectively. This raises an interesting question. What if we've been focusing on the wrong problem? What if public safety is not always about identifying individuals, but about understanding behaviour? As discussions around AI surveillance continue to evolve, there is growing interest in approaches that can improve public safety while reducing reliance on personal identification (https://tinyurl.com/2jpt86x2).

FROM WATCHING TO UNDERSTANDING

doi:10.1093/itnow/bwag080

The evolution of surveillance technology can be viewed in stages. The first stage was largely passive. CCTV systems provided evidence after something had happened. They were useful, but fundamentally reactive. The second introduced AI-driven analytics. Cameras became capable of counting people, monitoring occupancy levels and detecting congestion. Today, crowd analytics systems are already being used in transport hubs and large venues to understand how people move through spaces — for example, companies such as CrowdVision have developed systems that analyse passenger flow and crowd density in airports, helping operators identify bottlenecks and improve operational efficiency. These systems answer questions such as: • • •

How many people are present? Where are they moving? Which areas are becoming congested?

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Useful as these capabilities are, they only tell part of the story.

THE RISE OF BEHAVIOURAL AI The next challenge is understanding behaviour. Recent developments in computer vision and machine learning are making it possible to analyse not just where people are, but how they are behaving. Researchers increasingly refer to this as behavioural AI: systems designed to identify patterns, interactions and behavioural changes within groups. Think of a busy railway station. Most of the time, crowd movement follows predictable patterns. People enter, leave, queue and disperse in familiar ways. But sometimes behaviour changes. People suddenly stop moving. Groups begin clustering unexpectedly. Movement becomes erratic. Individuals start moving against the flow of the crowd. Density increases rapidly in a particular location. Human observers are often surprisingly good at recognising these signals. Experienced event managers and security professionals frequently describe situations where they sensed that something was wrong before an incident actually occurred. The ambition of behavioural AI is not to replace human judgement, but to augment it by identifying patterns that may otherwise go unnoticed. This is reflected in wider research trends. Studies in crowd analysis have increasingly moved beyond simple counting towards behaviour understanding, anomaly detection and predictive modelling (https://tinyurl.com/58jaybrp). Several developments in AI are helping to accelerate this shift. Modern computer vision systems no longer analyse images frame by frame, but model behaviour over time using temporal and transformer-based architectures. Visionlanguage models (VLMs), such as GPT-4o and Gemini, have

also demonstrated an increasing ability to interpret complex visual scenes and relationships, suggesting future systems may become better at understanding context rather than simply recognising objects. The technology is advancing rapidly. But recognising patterns in a video stream is one thing; identifying meaningful indicators of risk is another. Can AI really distinguish between normal crowd behaviour and the early signs of congestion, panic or aggression?

CAN IT WORK IN PRACTICE? This is the question that underpins our current research. The work sits at the intersection of computer vision, behavioural modelling and privacy-preserving AI. Most biometric systems rely on what are often called ‘hard biometrics[GS1.1]’ — characteristics such as faces, fingerprints or iris patterns that are directly linked to identity. Our research focuses on behavioural characteristics, sometimes referred to as ‘soft biometrics’, including movement trajectories, posture, group interactions and changes in collective behaviour over time. These behavioural representations are then used to train deep learning models that learn patterns associated with different crowd activities such as gathering, dispersing, queueing or exhibiting unusual movement patterns. By analysing sequences of behaviour rather than individual images, the aim is to determine whether emerging risks can be identified before they become obvious to human observers. A particular focus of the project is determining whether useful behavioural information can be retained while reducing the potential for identity disclosure. In other words, can AI learn enough about what people are doing without needing to know who they are? The research remains at an early stage. The goal is not to predict specific incidents with certainty, but to investigate whether behavioural signals can provide earlier warnings of elevated risk.

THE CHALLENGES AHEAD As with many emerging AI technologies, the opportunities are accompanied by important challenges. One challenge is context. Human behaviour varies enormously between environments, systems that perform well in one setting may not generalise to another. False positives are another concern. Excited celebrations, spontaneous gatherings or unusual but harmless behaviour may be incorrectly interpreted as signs of risk. Data remains a major challenge. Real crowd incidents are rare, sensitive and difficult to capture, meaning that building reliable datasets may be just as important as developing the AI itself. As a result, researchers are increasingly exploring self-supervised learning to identify unusual crowd behaviour.

Image source: Adobe Stock

There are also broader questions around transparency and accountability. If an AI system flags a crowd as potentially high risk, how should that decision be interpreted? What evidence supports the assessment? How should human operators respond? These issues are becoming increasingly important as regulators develop guidance for the responsible use of AI. Frameworks such as the UK Information Commissioner's Office AI and Data Protection Guidance (https://tinyurl.com/3srwxs76) and the NIST AI Risk Management Framework (https://tinyurl.com/2wd4mcsz) emphasise the importance of governance and risk management when deploying AI systems in real-world environments.

LOOKING AHEAD Looking further ahead, behavioural AI is unlikely to rely on video alone. Future systems may combine video, audio, sensor feeds, location data and environmental information to build a more complete picture of what is happening in a space. In this sense, the future may be less about smarter cameras and more about multimodal AI which builds a richer understanding of how people, spaces and events interact in real time. Whether this leads to safer public spaces remains an open question. The technology is still evolving, and many ethical and governance challenges remain. However, the direction of travel is becoming clearer. As AI moves beyond simple recognition towards understanding behaviour and context, the focus may shift from identifying individuals to understanding situations. With thanks to Dr Bilal Hassan and Mr Sonjoy Das for their contributions to our current research project, which informed this article.

ITNOW | Issue 3 2026

FOCUS

FRANK LAND: BRITISH COMPUTER PIONEER TURNED ACADEMIC Frank Land OBE FBCS, who died aged 97 on May 16 2026, was a prime mover in the story that took the world’s ﬁrst business computer, Lyons’ legendary LEO, to early commercial success. He went on to become a leading academic, pioneering research and teaching in the new discipline of systems analysis at the London School of Economics from the late 1960s through to the present day. Frank achieved all this as a young man despite the difficulties he faced as a Jewish refugee from Hitler’s Germany, arriving in England as a German-speaking 10 year old in 1939. Frank started working with computers in 1952. He had joined J Lyons & Co, the well-known and enterprising catering company, as a trainee cost accountant in the firm’s statistics office after graduating from the London School of Economics (LSE) in 1950 with a degree in economics. Lyons needed programmers to work on its new business systems and they sought to recruit them from within their own offices. Land was selected to join the handful of people working on the LEO (Lyons Electronic Office) project. At the time, he had no idea that the company was pioneering the use of computers for business — but he and his colleagues in the small LEO team developed the world’s very first operational computer designed to handle business data.

doi:10.1093/itnow/bwag082

This proved a fascinating, never-to-be forgotten time where the team broke fresh ground each day in bringing the LEO computer to work. Land worked as a programmer and systems analyst and, as the LEO enterprise grew, he became involved in projects for a growing list of companies who were buying time on the Lyons computers, or acquiring their own LEOs – the likes of Ford, Smith & Nephew, Renolds Chains, Standard Motors, ICI, Durlachers and British Oxygen. By the mid 1960s he was one of the more senior members of the LEO team in charge of regional offices and subsequently acting as chief consultant.

MOVE INTO EDUCATION Two events then combined to change Land’s career direction.

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First, in 1963, LEO Computers Limited — the fully-owned J Lyons subsidiary — was merged with the computer interests of the English Electric Company. Second, in 1967 the National Computing Centre offered grants to two universities to establish research and teaching facilities in the new discipline of systems analysis. The London School of Economics, one of the successful bidders for the grants, offered Land the chance to head up the new activity and at the same time to manage their new computer services department. Frank jumped at the opportunity, particularly since the LSE already employed his wife Ailsa as a lecturer in operational research. Frank’s work with systems at Lyons and with customers of LEO Computers had already convinced him of the importance of getting the understanding, knowledge and consent of the intended computer users if computers were to be deployed successfully. Now, he discovered the work of the sociotechnical organisational behaviour specialists — in particular, the work of Enid Mumford at the Manchester Business School. Working with Mumford and John Hawgood, he helped to bring the notions of socio-technical design into wider acceptance. It became the basis of much of the teaching and research at LSE. LSE became one of the first universities to offer graduate courses in information systems analysis and to initiate research into information systems and their impact on organisations. Frank became deeply involved in helping to define the nature of the subject and in setting down curricula for professional, undergraduate and graduate levels, working with organisations such as BCS and the Council for National Academic Awards, and helping to establish an international curriculum as a part of a project to establish standards by the International Federation for Information Processing (IFIP).

In 1975, Frank spent a year at the Wharton School, University of Pennsylvania, as a visiting professor in its Department of Decision Science in what was for him a very productive year. In 1982 he became the first professor in information systems in the UK. He then spent more time abroad as a visiting professor in several Australian universities (Sydney University, NSW, Curtin University in Perth, Western University and, twice, at Bond University in Gold Coast Queensland), as well as at the Indian Institute for Management in Ahmedebad, India. In the meantime, together with Barbara Farbey and David Targett, he carried out a series of research projects studying the value added to an organisation from the use of computer-based information systems. In 1986, Frank took up the chair of information management at the London Business School, retiring from full-time academic work in 1991. He returned to the LSE as emeritus professor in its Information Systems and Innovation Group.

INDUSTRY RECOGNITION INTERNATIONALLY During his career, Frank, a fellow of the British Computer Society, was twice appointed technical advisor to select committees of the House of Commons investigating the UK computer industry. Between 1984 and 1988 he was chairman of IFIP WG 8.2. In 1991, he received an honorary doctorate of science from what is now the University of East London and in 1992 he received the IFIP Outstanding Service award. In 2000 he was awarded a fellowship of the Association for Information Systems (AIS) and in 2003 the AIS LEO award for distinguished service to the discipline of information systems.

Frank always said that his career in computers owed everything to his early experience with LEO. During his retirement he collaborated with fellow ex-LEO personnel David Caminer, Peter Hermon and John Aris in writing a book on the LEO story. Subsequently, he worked tirelessly through the LEO Computers Society to keep the memory of LEO alive. His principal achievement in this enterprise has been the establishment of a comprehensive online database of LEO material that he called LEOpedia, developed in partnership with the Centre for Computing History in Cambridge.

PERSONAL LIFE Frank, one of distinguished twin brothers, was born in Berlin on October 24 1928. He married Ailsa Dicken, who went on to become the LSE’s professor of operations research, in 1953. They had three children and seven grandchildren. Upon his and Ailsa’s retirement they moved to Totnes, Devon and Frank became chairman of his local parish in the Dartmoor National Park. Ailsa pre-deceased him in 2021, coincidentally on May 16 of that year. Frank is survived by his twin brother, Ralph, his son Richard, and daughters Frances Place and Margi Knight along with seven grandchildren and four great-grandchildren with another on the way. This obituary was kindly provided to BCS by LEO Computers Society and proofread for consistency by our in house team. Images also kindly provided for use, including Frank taking part in a charity skydive in his 80s.

Latterly, in recognition of his services to the information systems industry, he was awarded an OBE in the late Queen’s Birthday Honours list of June, 2019.

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IMPACT

25 YEARS OF BCSWOMEN: PAST, PRESENT AND FUTURE As BCSWomen marks its 25th anniversary, Claire Penketh MBCS FRSA, Head of Policy Development at BCS, looks back at the group’s history, its impact on gender diversity in tech, and the current data on the progress made and what needs to be done to achieve lasting change.

oo often overlooked is the extraordinary contribution women have made to computing. While the popular image of technology has long been dominated by men, the real story is very different. From the earliest theories of programming to today’s AI and quantum computing, women have been at the forefront. The journey has not been straightforward, and it is a reminder that the problem is not one of capability, but of visibility, opportunity and inclusion.

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Now, Bletchley Park exists as a museum which explicitly celebrates the lives and legacies of the women who worked there. The founder of BCSWomen, Professor Sue Black, was one of the driving forces behind the successful campaign to transform it into a thriving centre after decades of neglect, during which time the indispensable contributions of women remained largely invisible.

Long before software engineering became a recognised profession, women were doing the work that laid the foundations for modern computing.

In the Second World War, women formed the majority of the workforce at Britain's codebreaking headquarters. Around three-quarters were women, serving in roles ranging from cryptanalysis and statistical analysis to machine operation, translation, and intelligence processing.

Ada Lovelace is often described as the world's first computer programmer. Working alongside Charles Babbage in the 19th century, she recognised that machines could do more than perform calculations. Her ideas anticipated many of the concepts that underpin computing today.

Some names have become widely recognised. Joan Clarke was one of the most renowned cryptanalysts at Bletchley Park and became Alan Turing's deputy in Hut 8, the section responsible for breaking German Naval Enigma ciphers used by U-boats targeting Allied shipping.

Lovelace was not alone. Throughout the 19th and early 20th centuries, women worked as ‘human computers’, carrying out complex calculations for astronomy, navigation, engineering and military planning. Though often seen as clerical tasks, these skills became central to the rise of the computing industry.

Mavis Batey was another leading codebreaker there, and she contributed to both the British victory at Cape Matapan in 1941 and the success of D-Day in 1944.

BEFORE ‘TECH’ WAS TECH

THE WOMEN OF BLETCHLEY PARK

But these women represented only a fraction of the thousands whose work remained anonymous for decades.

Image source: Adobe Stock

Many operated the Bombe machines used to decipher Enigma messages; others worked with Colossus, the world's first electronic computer, which helped decipher the Lorenz-encrypted (Tunny) messages between Hitler and his generals; still more processed intelligence, analysed patterns, and ensured information reached decision-makers. Part of the reason for their invisibility was secrecy; those who worked at Bletchley were bound by the Official Secrets Act and often spoke little about their roles, even to family members. However, secrecy alone does not explain their absence from the historical record. Like many women in technical fields, their work was frequently characterised as support rather than expertise, despite requiring exceptional skill, judgment and technical capability.

WHEN COMPUTING STOPPED LOOKING LIKE WOMEN'S WORK The decades following the war reveal one of the most significant paradoxes in the history of technology: as computing became more prestigious, women's participation declined. After the war, many women who had contributed to science and computing were encouraged to return to domestic roles. As computing’s status rose, it shifted from a clerical occupation to a prestigious profession, increasingly seen as a male domain. This reshaped public perceptions of who belonged in tech, and its effects are still felt today.

BRITISH PIONEERS WHO CHANGED THE INDUSTRY Britain has produced some of the most

'AS COMPUTING’S STATUS ROSE, IT SHIFTED FROM A CLERICAL OCCUPATION TO A PRESTIGIOUS PROFESSION, INCREASINGLY SEEN AS A MALE DOMAIN. THIS RESHAPED PUBLIC PERCEPTIONS OF WHO BELONGED IN TECH, AND ITS EFFECTS ARE STILL FELT TODAY.'

influential women in computing history. Dame Stephanie Shirley OBE CH, a Distinguished Fellow of BCS, was a pioneering entrepreneur and philanthropist who founded Freelance Programmers in 1962, a software firm that employed exclusively women and championed flexible working practices and gender equality. It grew into a major software enterprise and helped establish professional standards that shaped the emerging software industry. She joined BCS as a student member in 1957 and became its first female president between 1989-90, before being awarded a Distinguished Fellowship in 2021. When she died in 2025, tributes poured in from across the BCS community. Other British pioneers made equally significant contributions. Karen Spärck Jones transformed information retrieval through work that underpins modern search engines and natural language processing. Dame Wendy Hall DBE FRS FREng, Regius Professor of Computer Science at the University of Southampton, pioneered work in hypermedia, contributing to many of the concepts

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IMPACT

computer science and attending academic conferences, she found that women were typically represented only as a fraction of attendees, creating environments that could feel isolating and unwelcoming. A turning point came when she attended a Women in Science conference in Brussels. Unlike the computing conferences she was used to, the event was filled with women who readily introduced themselves, shared experiences and offered support. The experience demonstrated the power of a professional community and helped her realise that the challenge was not a lack of individual confidence, but a lack of networks and visibility for women in technology.

Image source: Sue Black

that underpin today's web and digital information systems. She served as BCS’ president between 2003 and 2004 and was awarded a Distinguished Fellowship in 2016. BCS Fellow, Professor Ursula Martin CBE, Professor of Computer Science at the University of Oxford, has combined excellence in computer science research with influential work on policy, ethics and widening participation in technology. These women did not simply succeed within the technology sector; they helped define it.

THE BEGINNINGS OF BCSWOMEN Against this backdrop, BCSWomen was established in 2001 by Professor Sue Black. Sue founded BCSWomen after experiencing first-hand what it felt like to be a woman in a predominantly male environment. In the late 1990s, while undertaking doctoral research in

With support from BCS, she established London BCSWomen in 1998, creating what became the UK's first online network for women in technology. Demand soon extended far beyond London, prompting her to pursue a national organisation. In October 2001, she formally launched BCSWomen as a UKwide specialist group. Former Chairs of BCSWomen have included Professor Karen Petrie, Vice Principal of the Faculty of Science, Engineering and Business at the University of Dundee; Gillian Arnold FBCS, who went on to become a BCS President and is widely recognised as a pioneering technology leader whose career has spanned industry, professional practice and diversity advocacy; Sarah Burnett FBCS, a respected technology industry analyst and leading specialist in intelligent automation; and Andrea Palmer FBCS, a technology leader and champion of inclusion who has dedicated her career to helping more women enter, progress and thrive in the technology profession. From the outset, BCSWomen offered something many women in the profession had struggled to find: a sense of belonging. Through email discussion groups, professional networking, technical conversations, mentoring, and shared experiences, members connected with peers facing similar opportunities and challenges. The network helped create confidence, career resilience and professional visibility at a time when such support structures were relatively rare.

BCSWOMEN TODAY 'A TURNING POINT CAME WHEN SHE ATTENDED A WOMEN IN SCIENCE CONFERENCE IN BRUSSELS. UNLIKE THE COMPUTING CONFERENCES SHE WAS USED TO, THE EVENT WAS FILLED WITH WOMEN WHO READILY INTRODUCED THEMSELVES, SHARED EXPERIENCES AND OFFERED SUPPORT.'

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25 years on, BCSWomen has evolved from a small online network into one of the UK’s most vibrant and influential tech communities, championing gender diversity and providing support, connection, and change across the digital profession. Speaking about the landmark anniversary, Tristi Tanaka MBCS, Chair, BCSWomen, said: ‘As we celebrate 25 years, we honour the pioneers who created a community where women

in technology can connect, thrive and lead. Our progress demonstrates the power of belonging and collective action. The next chapter is about building a profession that reflects society and ensures technology delivers positive outcomes for everyone.’ Jo Stansfield, Deputy Chair, BCSWomen, said: ‘BCSWomen’s 25-year journey shows that innovation is strongest when diverse voices are heard, valued and empowered. As technology increasingly shapes every aspect of society, creating inclusive cultures is not optional — it is essential. We celebrate the community that has driven change so far and look forward to building a more equitable, responsible and innovative future together.’

BCSWOMEN’S IMPACT That emphasis on community remains central to BCSWomen's identity. Professor Sue Black has frequently described bringing women together as one of the group's most important achievements. In a sector where informal networks and personal connections can often shape careers, creating trusted spaces for collaboration and support has proved invaluable. A flagship contribution is the BCSWomen Lovelace Colloquium, founded in 2008 by Dr Hannah Dee MBE. This national conference for women and non-binary computing students offers opportunities to present research, engage with employers, and build networks. The 19th annual event took place at the University of Bath this year and was the largest to date, showing continuing support for professional communities and visible role models in computing. Dr Dee was awarded her MBE for services to technology and women in the IT sector, and was recognised for founding the colloquium.

and competitiveness. Technology shapes society, and many industry leaders argue that the people designing digital systems must better reflect the communities they serve. Through advocacy, community-building, and contributions to industry-wide discussions on diversity, the organisation has become an important voice in the tech community at large, shaping the conversation around inclusion in technology.

WHAT’S NEXT FOR BCSWOMEN? At a time when digital skills are central to the UK's economic ambitions and the development of emerging technologies from AI to quantum, the organisation's mission remains as relevant as ever. Gender diversity is still a critical challenge across tech and STEM fields, and this gap has profound implications for the profession's future. Looking ahead, BCSWomen enters its next quarter-century with no shortage of ambitions: increasing representation in technical and leadership roles, strengthening support for women in tech and addressing the intersectional dimensions of inclusion. Success will require collaboration across education, industry and government, but the organisation's history demonstrates the impact that committed communities can have over time. Emerging technologies like AI are reshaping markets, roles, and professional boundaries at an unprecedented pace. In this moment of rapid change, women's voices and perspectives risk being excluded and overlooked, while careers and progression in the tech sector may feel limited

CELEBRATION MEETS REALITY The anniversary is also a reminder that significant challenges remain. According to BCS' Gender Diversity in the Tech Sector Report 2025, women still account for just 22% of IT specialists. At this rate, it will take 283 years before women make up an equal share of the tech workforce. The report highlights further disparities across the profession. Women IT specialists represent just 6% of IT engineering roles, while Black women account for only 0.6%. A pay gap also persists, with female IT specialists earning around 12% less per hour than their male counterparts. As the UK seeks to strengthen its digital economy and leadership in areas such as artificial intelligence, failing to attract and retain diverse talent risks limiting innovation

Image source: Web Science Trust / Wikipedia

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BUILDING A MORE INCLUSIVE PROFESSION IS NOT SIMPLY THE RIGHT THING TO DO; IT IS ESSENTIAL IF WE WANT TECHNOLOGY TO REACH ITS FULL POTENTIAL AND WORK FOR EVERYONE SHARRON GUNN, CEO OF BCS

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with immediate career value such as skills frameworks, trusted training and certification, platforms for knowledge-sharing, and contributions to the professional body's evidence base.

or inaccessible. The need for visible, vocal advocates and allies has never been clearer. Marking the quarter-century milestone, Sharron Gunn, CEO of BCS, said: ‘Over the past 25 years, we have seen the extraordinary contribution women make to technology when barriers are broken down and opportunities are opened up. ‘As BCS approaches its 70th anniversary next year, and AI reshapes every part of our lives, it is more important than ever that women are at the heart of that progress. Building a more inclusive profession is not simply the right thing to do; it is essential if we want technology to reach its full potential and work for everyone.’ BCSWomen’s mission is to close the gender gap by 2050, focusing on four main areas: •

Inclusive design in the evolving membership: Building a multigenerational network that spans diverse specialisms and acknowledges the reality that many women’s careers are nonlinear, impacted by life phases and responsibilities which create structural barriers to recruitment, retention, and progression.

•

Prioritising practical opportunities: Focusing on concrete initiatives

•

Strengthening policy advocacy: Identifying known and emerging barriers and linking practical interventions to scaled initiatives that drive systemic change.

•

25 more years: BCSWomen are committed to lifting one another up while staying grounded in collaboration, integrity, and evidence-based action. We're building a community that is resilient, inclusive, and ready to act.

•

25 years after a small network was transformed into a national movement, BCSWomen stands as evidence of what collective action can achieve. As BCSWomen celebrates its 25th anniversary, it does so from a position of strength, with a generation of women in technology benefiting from the foundations built over the past quarter-century.

ABOUT BCS WOMEN BCSWomen is a volunteer-led specialist group which exists to create tangible pathways for women to develop, progress, and thrive in technology careers. Their work spans four interconnected pillars: 1.

Education and career entry

Early career, career pivots, and returns to work

Retention and professional staying power

'THE ANNIVERSARY IS ALSO A REMINDER THAT SIGNIFICANT CHALLENGES REMAIN. ACCORDING TO BCS' GENDER DIVERSITY IN THE TECH SECTOR REPORT 2025, WOMEN STILL ACCOUNT FOR JUST 22% OF IT SPECIALISTS. AT THIS RATE, IT WILL TAKE 283 YEARS BEFORE WOMEN MAKE UP AN EQUAL SHARE OF THE TECH WORKFORCE.'

Women’s progression and senior leadership

As well as practical support, BCSWomen offers members community, providing a space where women can explore their careers, share experiences and find genuine support across specialisms and sectors. BCSWomen is here to ensure that women shape the technology profession and welcomes advocates and allies. Read more about the 25th anniversary of BCSWomen: https://tinyurl.com/2hf24bp8

ITNOW | Issue 3 2026

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CELEBRATING THE LIFE OF ROB NEIL FBCS CITP

BCS was sad to hear the news of the passing of Rob Neil CITP CEng FBCS. Our thoughts and well wishes are with his colleagues and friends.

ob will be remembered as a man who combined a sharp intellect for technology with an instinctive understanding of people. He became a trusted digital transformation adviser to local government, and a generous mentor. He was also an energetic BCS volunteer, holding inﬂuential positions on the Academy of Computing Board and the Registrations and Standards Committee. Sharron Gunn, Group Chief Executive of BCS, the Chartered Institute for IT, said: ‘Rob gave so much to BCS over many years and was a hugely valued, active member of our community. Most recently, he served on our Registrations & Standards Committee, continuing to contribute with dedication and generosity even while facing illness… He will be greatly missed, and very warmly remembered.’

EARLY YEARS

TRIBUTES

PERSONAL LIFE Rob married his wife in June 2000 and became a devoted stepfather to her daughter, Merissa, whom he treated as his own. He took great pride when she followed in his footsteps to the University of East Anglia in 2020. In recent years, he found particular joy in becoming a grandfather, relishing regular visits to Leeds to spend time with his grandchildren and sharing stories of their milestones with colleagues and friends. Despite undergoing treatment for cancer over the past year, he remained engaged with his work and community for as long as possible. He died on Thursday, 21 May at Pilgrims Hospice in Ashford, where, his family said, staff ensured his final days were as comfortable as possible. Rob’s career spanned the evolution of public sector IT from its early, more fragmented forms to today’s integrated, citizenfocused services. Throughout, he remained guided by a simple principle: that technology should serve people, not the other way around. It was a belief he lived out not only in his professional work, but in the generosity, humour and humanity he showed to those around him. His wife, daughter and grandchildren survive him.

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doi:10.1093/itnow/bwag084

Rob’s influence extended far beyond formal roles. As we wrote, tributes poured in via LinkedIn. Many saw him as a mentor figure whose encouragement helped shape careers: colleagues spoke of his willingness to offer honest advice, open doors and support others in starting their own businesses. Others emphasise his professional qualities — knowledgeability, pragmatism and thoughtfulness, and a rare ability to see both sides of a problem and bring people together around a solution. Several note his passion for improving public services through technology and the lasting impact of his work across local government. Yet it was his character and personal connections that came through most vividly. Rob is described as a ‘gentleman’, ‘a true professional’, and ‘one of the good guys’, but also as someone with a dry or sometimes mischievous sense of humour, whose presence could lift a meeting or lighten a difficult moment.

He is remembered as generous with his time, warm in his relationships and deeply loyal to colleagues and friends. He was, in the words of those who knew him, ‘great company’ — equally at home discussing system architecture or sharing a beer or a curry. A confessed foodie, he earned a reputation for knowing the best menu choices wherever he went, and his enthusiasm for music — particularly Nena and Toyah — was well known among friends.

Born in Dover in 1968, Rob was educated at Dover Grammar School for Boys before studying computing science at the University of East Anglia, where he gained a BSc (Hons). He later completed an MSc in intelligent knowledge-based systems — an early form of artificial intelligence — at the University of Essex. This field would foreshadow both his professional interests and the wider transformation he helped public services to navigate. After graduating in 1991, he worked as a software engineer and consultant before moving into academia. Between 1992 and 1998, he was a senior lecturer in computing at Canterbury Christ Church University, where colleagues recall both his depth of knowledge and his ability to make complex ideas accessible. He later returned to industry as an IT manager before joining Ashford Borough Council in 2001 as head of ICT and customer

services. In 2010, he became the council’s chief information officer, a role in which he developed a reputation for combining technical expertise with pragmatism and a focus on outcomes. It was, however, with the founding of Accordio Ltd in 2015 that Rob found what those closest to him described as his ‘true vocation’. Through the consultancy, he advised local authorities, including Tower Hamlets, Kent County Council, West Lancashire Borough Council, and Norfolk County Council, helping them navigate the challenges of digital transformation at a time of financial constraint and rising citizen expectations. His work was marked by a practical, collaborative approach, building consensus across organisations and translating strategy into delivery.

#POPCOMPSCI

AI ETHICS AND GOVERNANCE: MOVING FROM THEORY TO PRACTICE From micro-businesses to large enterprises, Darren Winter, AI Ethics Specialist, Trainer, Consultant and Speaker, explores how ethical thinking can be built into everyday decisions, shared responsibility and governance.

I ethics and governance is often treated as an afterthought: the system is built, the use case is launched, and only then do the risks, gaps and unintended consequences become visible. But in a rush to adopt AI, organisations can easily miss a more basic question: do we need AI here, and if so, what safeguards should be in place before we use it? That is why AI ethics matters. The UK Government’s evolving approach to AI regulation, the ICO’s guidance on AI and data protection and the EU AI Act’s phased rollout through 2026 and 2027 all point the same way: organisations are expected to build ethical thinking into how they design, buy and deploy AI, not review it after launch.

This article shows three practical routes: start small in micro-businesses with user-focused checks; build capability in SMEs through champions, cross-functional practice and training; and scale with structured governance and independent oversight in large organisations.

AI ETHICS AND GOVERNANCE IN MICRO BUSINESSES

doi:10.1093/itnow/bwag085

AI ethics starts with two simple questions asked at every decision point: who is affected by this, and what could go wrong for them? Those questions keep attention on people rather than just performance, and they help teams

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pause before adopting a tool simply because it looks efficient or fashionable. A useful next step is to walk through the user journey and ask where someone might be excluded, misrepresented or disadvantaged by an assumption built into the product. That might be a customer whose needs, circumstances or language are not reflected in the data, or a user who is given a result they cannot understand, challenge or act on. The ICO’s guidance is clear that fairness and transparency need to be considered across the AI lifecycle, including problem formulation and deployment. In a micro-business, ethics should be embedded into everyday decisionmaking. There may be no board, committee or formal review process, but there should still be a habit of asking whether AI is needed at all, whether the decision is proportionate, and whether the impact on people has been properly understood before launch.

BUILD CAPABILITY: ETHICS IN SMES As organisations grow, AI ethics and governance needs to become a shared responsibility. This is where ‘AI Champions’ can play a genuinely useful role. I am a big fan of a specific, focused champion role, ideally held by someone exposed to several parts of the

Image source: Adobe Stock

organisation, because they can see both the frustrations and successes of the process as well as customer feedback and operational reality.

together helps challenge assumptions and increases the chance that hidden risks are identified before they are built into the design.

A good AI Champion is not there to block innovation. They are there to notice patterns, ask structured questions and provide feedback on where AI is helping, where it is causing friction and where it is creating risk. That role works best when it is tied to real conversations about operations, procurement, service quality and user experience, rather than being left as a side project with no authority or access.

SCALE WITH STRUCTURE: ETHICS IN LARGE ORGANISATIONS

This stage also demands investment in skills and training. AI literacy should be accessible for managers, procurement teams and front-line staff so they can recognise bias, question outputs and understand when human oversight is required. Practical upskilling through short courses, role-based scenarios and workshops turns good intentions into day-to-day capability, supports compliance, reduces the risk of data breaches or unsafe use of non-anonymised data, and helps organisations recognise and reward best practice. Diverse perspectives matter because bias is often invisible from inside the role that created it. Technical, commercial and operational teams will each see the system differently, but none will see the whole picture on their own. Bringing those viewpoints

In larger organisations, AI ethics needs structure and formal oversight. That usually means an independent AI or ethics board with mixed membership that reflects the key stakeholders in the organisation: technical, legal, operational, customer-facing and senior leadership perspectives. If the membership is too narrow, the board risks reflecting one part of the business instead of challenging it. It is important to remember that the board’s recommendations should be independent of organisational goals, even though that can be difficult to manage in practice. The point of the board is not to repeat commercial ambition in a different room; it is to test whether a system is fair, transparent, explainable and defensible, even when it is attractive from a business point of view. BCS’ work in this area is relevant here. Its report on AI ethics and professional registrations found strong public support for independent standards in high-impact AI roles, reinforcing the idea that accountability should not sit entirely inside the organisation

that benefits from the technology (https://tinyurl.com/2nuemu63). At scale, AI ethics and governance is not about slowing everything down; it is about creating the discipline and independence needed to make better decisions.

CONCLUSION: ETHICS AS A MATURING PRACTICE Technology is moving quickly, and we do not have all the answers yet. But we do know what good practice looks like: protect people, data, privacy, rights, and customers, and ensure AI is used in ways that are proportionate, explainable and responsible. Getting AI ethics right is not about slowing progress; it is about securing a future that organisations can trust. The organisations most likely to succeed will start simple, then scale. They will ask the right questions early, build capability through champions and cross-functional discussion, and introduce structure and independence when the organisation becomes large enough to need it. Darren Winter is Director of Duco Digital Training, a BCS-accredited training organisation specialising in AI, business and technology training, and recently completed three years of doctoral PhD research in AI Ethics at the University of Córdoba.

ITNOW | Issue 3 2026

TODAY

WHY MOST ENTERPRISE AI PROJECTS FAIL AFTER THE DEMO Building an AI demo is easy. Turning it into a trusted, scalable business capability is where the real challenge begins, as Danny Major MBCS explains.

f you've spent any time around AI over the past couple of years, you've probably seen the same pattern. Someone builds an impressive demo in a few days. Everyone in the room is amazed. The business starts talking about transforming customer service, automating internal processes or reinventing the employee experience.

— the model is the easy bit. That might sound strange given all the attention paid to the latest models, but choosing an LLM has become one of the simpler decisions you'll make. The harder questions are the ones that don't make the headlines: •

Then... progress slows. The pilot takes longer than expected. Security raises concerns. Compliance wants answers. The knowledge isn't as clean as everyone thought it was. Users don't quite trust the responses. Six months later, the AI is still being described as a ‘pilot’. I've seen this play out time and again. The technology is rarely the thing that gets in the way. Usually, it's everything around it.

doi:10.1093/itnow/bwag086

KNOWLEDGE IS POWER FOR AI TOO The truth is that building an AI demo and deploying AI into a real business are two completely different challenges

• • • •

Where does the AI get its information from? Who owns that information? How do you know it's up to date? What happens when the AI isn't confident? How do you improve it over time?

Those questions don't make for exciting demos, but they're the ones that determine whether an AI project succeeds. AI is only as good as the knowledge behind it. One thing I've learnt is that most organisations don't have an AI problem, they have a knowledge problem. Information lives everywhere: SharePoint, PDFs, internal wikis, product documents, emails, Teams chats… different departments often have different versions of the same

'MOST ORGANISATIONS DON'T HAVE AN AI PROBLEM, THEY HAVE A KNOWLEDGE PROBLEM. INFORMATION LIVES EVERYWHERE... DIFFERENT DEPARTMENTS OFTEN HAVE DIFFERENT VERSIONS OF THE SAME ANSWER, AND NOBODY IS ENTIRELY SURE WHICH ONE CURRENT — AI SIMPLY EXPOSES THAT PROBLEM.'

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answer, and nobody is entirely sure which one current — AI simply exposes that problem. If your knowledge is fragmented, inconsistent or out of date, the AI will struggle too. It doesn't matter how capable the underlying model is. Ironically, some of the most successful AI programmes I've worked on have spent far more time organising knowledge than experimenting with prompts. It isn't glamorous work, but it's where the real value is created — trust beats intelligence.

TRUST IS PARAMOUNT When people use consumer AI, they'll often forgive the occasional mistake. It's very different in an enterprise environment. If you're helping someone understand an insurance policy, supporting a patient with healthcare information or assisting a customer with a financial product, ‘probably right’ isn't good enough. The organisations making AI work aren't necessarily trying to answer every question. They're building systems that know when to answer, when to ask for clarification and when to hand over to a human. In other words, they're designing for trust. I've found that users are surprisingly accepting when an AI says, ‘I don't know’ or ‘you need to speak to

Image source: Adobe Stock

someone’. What frustrates them is an AI that sounds confident while giving the wrong answer — AI projects are organisational projects.

many questions it answered without human intervention. Those numbers are interesting, but they don't tell you whether the project is succeeding.

of hours. That's fantastic. It lowers the barrier to innovation and allows organisations to explore ideas much more quickly than ever before.

Another lesson I've taken away is that AI isn't just a technology implementation. Introducing AI changes how people work.

The measures that really matter are the ones the business already cares about:

However, the projects delivering real value aren't succeeding because they picked the latest model or wrote the perfect prompt. They're succeeding because they invested in the things that are much harder to demonstrate: good knowledge, strong governance, thoughtful engineering and clear measures of success.

Someone now needs to own the knowledge. Someone must review content. Teams need to understand how the AI behaves. Governance processes must evolve. Success measures need to be agreed before the first customer ever uses it. The organisations that recognise this early tend to move much faster than those treating AI as another software deployment.

HOW TO MEASURE SUCCESS Stop measuring conversations! It's tempting to judge success by how many conversations an AI has handled or how

• • • • •

Did customer satisfaction improve? Did quote conversion increase? Did call volumes reduce? Are colleagues spending less time searching for information? Has the customer journey become easier?

If the answer to those questions is yes, then the AI is working. If not, it doesn't really matter how impressive the demo looked — the demo was never the difficult part. Generative AI has reached the point where almost anyone can build something impressive in a matter

'THE ORGANISATIONS MAKING AI WORK AREN'T NECESSARILY TRYING TO ANSWER EVERY QUESTION. THEY'RE BUILDING SYSTEMS THAT KNOW WHEN TO ANSWER, WHEN TO ASK FOR CLARIFICATION AND WHEN TO HAND OVER TO A HUMAN. IN OTHER WORDS, THEY'RE DESIGNING FOR TRUST.'

The irony is that the best enterprise AI projects often look less magical than the demos; they're predictable, they're reliable, they know their limits… and that's exactly why people trust them. Perhaps that's the biggest lesson of all. The future of enterprise AI won't be won by the systems that sound the smartest. It'll be won by the ones that organisations and their customers can depend on every single day. Danny Major FBCS is an advisor, focused on helping tech businesses on the journey from scale-up to enterprise. LinkedIn: https://tinyurl.com/ccnpw334

ITNOW | Issue 3 2026

TODAY

HOW AI IS RESHAPING THREATS AND THE STEPS NEEDED TO REDUCE RISK As artiﬁcial intelligence increasingly becomes part of the attack surface, Katerina Tasiopoulou, CEO and Founder of Threatscene, explores the options for protecting today’s businesses.

or many years, security teams have understood the attack surface in familiar terms: endpoints, networks, applications, identities and third-party suppliers. Artiﬁcial intelligence is now reshaping that model. AI introduces new assets that must be secured, new techniques that attackers can exploit, and new forms of failure that existing controls were never designed to address. Beneath this technical shift lies an economic reality: many AI tools are inexpensive or free for attackers to use, while deploying them responsibly as a defender requires investment, governance and scarce expertise. That imbalance is already influencing how organisations need to think about cyber resilience.

doi:10.1093/itnow/bwag087

WHAT DOES ‘ATTACK SURFACE’ MEAN IN THE AI ERA? Traditionally, the attack surface has been the collection of points where an attacker could gain access to, disrupt or influence an organisation. In practice, this meant vulnerable software, exposed services, weak credentials, misconfigurations and supplier risk. AI does not replace these concerns. It adds to them.

• • • • • •

training and inference data prompts and user interactions APIs connected to AI services vector databases and retrieval systems GPUs and specialised infrastructure automated workflows driven by AI outputs

Many of these components sit outside conventional security processes, creating blind spots for governance and assurance teams.

HOW AI IS EXPANDING RISK FOR ORGANISATIONS

foundation models and ﬁne-tuned models

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HOW THREAT ACTORS ARE LEVERAGING AI Cybercriminals are rapidly adopting AI, often at a lower cost than defenders. Open-source models, free-tier services and criminally adapted tools such as WormGPT and FraudGPT have lowered the barrier to entry for less-skilled actors. In practical terms, AI is being used to:

In many cases, AI risk is being created by the speed of adoption rather than by the technology itself. Business units are deploying AI tools faster than security teams can assess them.

•

One clear example is shadow AI: employees using unauthorised tools to summarise documents, generate code or draft communications. In doing so, sensitive information may be entered into public services without legal, contractual or security review.

•

Third-party AI providers also introduce a new form of supply-chain dependency. Organisations are no longer outsourcing only storage or software capability; they may be outsourcing decision support, reasoning processes and data handling to vendors whose internal controls are not always transparent.

Today’s risk footprint may include: •

often created quickly, sometimes outside established identity and access management controls.

• •

•

The result is not necessarily a new category of attack, but a significant increase in speed, volume and believability.

EMERGING AI-NATIVE THREATS Alongside traditional attacks enhanced by AI, there are now threats that target AI systems directly. These include: •

At the same time, internal complexity grows. New service accounts, API keys, plug-ins and integrations are

generate more convincing phishing emails in multiple languages create deepfake voice and video for fraud or impersonation automate reconnaissance of exposed systems improve malware variation to evade detection scale scams and social engineering campaigns

•

Prompt injection: manipulating a model through crafted inputs Indirect prompt injection: malicious instructions hidden

Image source: Adobe Stock

• •

in files, webpages or emails Data poisoning: corrupting training or retrieval data Model extraction: recreating proprietary models through repeated querying Sensitive data leakage: exposure through prompts, logs or outputs Hallucination risk: confident but inaccurate outputs used in business processes

you would to any other data flow 4. Apply least privilege to models, datasets, APIs and admin tools: identity is now the primary control plane for AI, as it is for cloud 5. Vet AI vendors as critical suppliers: ask about data retention, training practices, subprocessors, and incident handling

Frameworks such as OWASP Top 10 for LLM Applications, MITRE ATLAS, and the NIST AI Risk Management Framework provide useful starting points.

6. Monitor prompts, outputs, and anomalous behaviour: logging is the foundation for both detection and accountability

THE STEPS NEEDED TO REDUCE RISK

7. Red-team AI systems: test for prompt injection, data leakage and misuse as part of normal offensive security work

Reducing AI risk is not about a single tool or a blanket ban. It is about bringing AI into the security disciplines that already apply to the rest of the business and extending those disciplines where AI is required. A practical starting set of steps: 1. Establish AI governance: define approved tools, acceptable use and data-handling rules, and make them enforceable rather than aspirational 2. Discover and inventory AI usage: you cannot secure what you cannot see. Shadow AI needs to be found, not assumed away 3. Classify data before it enters AI systems: apply the same sensitivity rules to prompts and training data as

8. Train staff on AI-specific risks: focus on AI-enabled phishing, deepfakes and safe usage, not just generic awareness content 9. Align with established frameworks: NIST AI RMF, ISO/IEC 42001, and the NCSC's Guidelines for Secure AI System Development provide structure and defensibility None of this is free. The asymmetry with attackers will not disappear, but it can be narrowed by investing in fundamentals rather than chasing point solutions.

FUTURE OUTLOOK

and scrutiny continue to increase. Measures such as the European Union AI Act, sector guidance and growing insurer expectations mean boards are being asked sharper questions: where is AI used, what data does it access, and who is accountable when it fails? The organisations most likely to succeed will not be those that moved fastest, nor those that delayed longest. They will be those that treated AI security as part of normal business management: governed centrally, monitored continuously and improved over time. AI should not be feared, but it must be secured. The attack surface has changed, attackers have gained an economic head start, and traditional controls need to stretch further than before. The response is not exotic technology. It is governance, visibility, identity, supplier discipline, testing, training and sustained investment. With extensive experience in global incident response units, Katerina has overseen high-profile cyber incidents and advises top organisations and government officials on cybersecurity. Katerina has received various awards, including the 'BCS Young IT Professional of the Year Award' in 2018, 'Top 100 Women in the UK, sponsored by the Sunday Times' in 2019, 'WeAreTheCity Rising Star Winner 2019,' and the 'FDM EveryWoman Rising Star Award' in 2020.

AI is becoming embedded in everyday business operations, while regulation

ITNOW | Issue 3 2026

TODAY

WHY LAW FIRMS MUST TREAT SECURITY AS A CORE PROFESSIONAL DUTY

Ian Jeffery, CEO of the Law Society of England and Wales, talks to Grant Powell MBCS about how cyber threats, regulation and collaboration are reshaping what it means to safeguard clients in a digital legal economy.

he legal profession needs to be understood from two perspectives. Firstly, it is an industry in its own right, handling highly sensitive client information, financial data and confidential communications. Secondly, it is an adviser to organisations across the wider economy, supporting them with everything from compliance and contracting to incident response. Ian Jeffery, CEO of the Law Society of England and Wales, explains why cyber security preparedness has never been more critical for today’s law firms.

doi:10.1093/itnow/bwag088

How is the legal profession positioned to reduce cybersecurity risks in an increasingly digital and cross-border economy? In terms of its own operations, the profession is already subject to frameworks and legislation such as GDPR, but it also faces additional obligations. Solicitors are regulated by the Solicitors Regulation Authority (SRA), which imposes clear duties regarding the safeguarding of client money, confidential information, and other assets. Alongside those regulatory requirements, there is a longstanding professional ethical responsibility to act in clients' best interests. That combination is important. It raises the bar for how seriously cyber risk must be treated and embeds security

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into the professional culture. But the profession’s role doesn’t stop there; legal practitioners also play a vital role in helping other sectors manage cyber risk — whether through advising on data protection, drafting commercial contracts, supporting IT procurement or responding to breaches and disputes. So, while law firms are part of a wider ecosystem and face many of the same risks as others, they also have a broader influence through the advice they provide across the economy.

What are the most significant cyber threats currently facing law firms and their clients, and where should they focus first? Many of the core threats will be familiar: phishing attacks, weak password practices and other forms of human error remain among the most common. These are not unique to the legal sector, but they affect it just as much as any other. Where things become more distinctive is in how those threats play out. A good example is fraudulent fund redirection, particularly in areas like conveyancing. These transactions often involve significant sums of money and operate under time pressure. If an email account is compromised, attackers may send last-minute changes to bank details, redirecting funds to fraudulent accounts. It’s a very targeted use of

otherwise familiar tactics. There is also the nature of the information law firms hold. Legal data can be highly sensitive, commercially valuable or politically significant. That creates additional incentives for attackers and exposes firms to risks such as hacktivism, as malicious actors target them for the clients they represent or the cases they are involved in. While the underlying threats are broadly consistent, their consequences in the legal context can be particularly serious. Firms need to be vigilant not only about the threats themselves, but also about how those threats intersect with their specific areas of practice.

As law firms adopt more advanced technologies, what does strong cyber resilience look like in practice? At its core, cyber resilience is about getting the fundamentals right. This includes technical controls, strong password practices, system maintenance, supplier assurance and, critically, staff training. But there has been a shift in mindset. Firms now need to assume that an attempted breach is a realistic scenario rather than a distant possibility. It’s vitally important that they focus not just on prevention, but also on preparedness and response, with the

processes in place to detect, contain and recover from an incident. What ‘good’ looks like will vary from firm to firm. A large commercial practice working with major financial institutions may face stringent client requirements around audits, contractual safeguards and supply chain security. Smaller firms will have different expectations, but the same principles apply. At the Law Society, we provide guidance to help firms get started and point them towards expert providers where necessary. Ultimately, though, resilience is context-dependent. It depends on the nature of the work being done, the clients being served and the sensitivity of the data involved.

What common cybersecurity mistakes do law firms tend to make? Many of the issues we see are predictable human mistakes, whether that’s falling victim to phishing attempts or failing to follow basic security practices. From a technical perspective, problems often arise when systems are not properly maintained. Failing to apply updates, leaving software unpatched or keeping redundant systems live can all create vulnerabilities. There have been cases where breaches occurred simply because an unnecessary legacy system was still active and accessible. More recently, we’ve also seen new risks emerging around artificial intelligence. One issue is the use of public AI tools. If confidential information is entered into these platforms, it can potentially be exposed. There is also the risk of inaccurate or misleading outputs, which must be carefully managed through human oversight. While many firms are now implementing secure, internal versions of these tools, that doesn’t remove the need for caution. At the same time, AI is increasing the sophistication of attacks. Deepfakes and advanced phishing techniques can make it much harder to distinguish legitimate communications from fraudulent ones. While the fundamentals remain critical, the landscape is evolving, and law firms need to adapt accordingly.

How important is collaboration with bodies like the National Cyber Security Centre in maintaining global trust in English law? Collaboration is essential. A good example is the 2023 report produced in partnership with the National Cyber Security Centre (NCSC), which combined the Law Society’s understanding of legal practice with the NCSC’s expertise on the threat environment (https://tinyurl.com/2fahk54x). This kind of collaboration produces guidance that is both technically robust and highly relevant to the profession. It also helps ensure that intelligence about emerging threats is shared more quickly and widely. While the largest firms may have access to cutting-edge information, it can take time for that knowledge to reach the rest of the profession. Working with organisations like the NCSC helps bridge that gap. We also work closely with bodies such as the Information Commissioner’s Office and the Solicitors Regulation Authority. Together, these relationships help maintain high standards and reinforce trust in the legal system, both in the UK and internationally. How is the evolving regulatory landscape shaping law firms’ cybersecurity obligations, and what should firms be doing now to stay ahead of compliance requirements? Law firms operate within a framework that includes GDPR, guidance from the Information Commissioner’s Office, and the Solicitors Regulation Authority's rules. The SRA takes a principlesbased approach rather than prescribing detailed technical standards. The central obligation is clear: firms must protect client money, confidentiality and assets. An important aspect of this is accountability. Firms are not judged solely on whether an incident occurs, because cyber threats are widespread and ongoing. Instead, the focus is on preparedness and response. Were appropriate measures in place? Was the incident handled properly?

Image source: Adobe Stock

To stay ahead, firms need to understand their obligations and embed them into day-to-day operations, ensuring that governance, systems, and training all align with those expectations.

For smaller firms with limited resources, what are the most impactful steps they can take to improve their cybersecurity today? For smaller firms, the most practical approach is to start with established guidance. The Law Society provides resources that walk through the key steps, from staff training to leadership awareness and the use of external expertise. In addition, frameworks such as the NCSC’s Cyber Essentials offer a clear, structured pathway to establishing a solid baseline level of security. By working through these materials, firms can build a strong foundation and identify any areas that require further attention based on their specific risks. It’s about prioritisation — focusing on the areas of most risk first, and then building from there. Cybersecurity is now a core part of professional responsibility in law. It cannot simply be outsourced. The challenge is growing, and the responsibility for operating securely and taking ownership of risk mitigation must sit firmly within each practice.

ITNOW | Issue 3 2026

TODAY

THE CHANGING FACE OF CYBER SECURITY IN THE UK PUBLIC SECTOR Conﬁdentiality, integrity and availability — Craig Shaw, Head of Cyber Security at Sopra Steria, explores how ensuring public sector IT systems are safe is a rewarding challenge.

doi:10.1093/itnow/bwag089

very industry faces its own cybersecurity pressures, but the public sector operates on a fundamentally different plane. When essential services, critical national infrastructure and sensitive citizen data are at stake, the margin for error narrows dramatically. Cybersecurity in government is not simply a technical challenge. It’s a matter of national resilience and public trust. Over the past decade, the UK public sector has been forced to evolve at pace. As recently as 2016, cyber responsibility was fragmented across government departments and primarily framed as a national security issue, rather than a strategic imperative embedded in day to day operations. That began to change with the formation of the National Cyber Security Centre (NCSC), which provided a clearer focal point for national guidance and coordination. The impact of the WannaCry ransomware attack soon after proved to be a watershed moment, accelerating the shift towards centralised standards, regulation and accountability. Between 2022 and 2024, this trajectory hardened further. The launch of the Government Cyber Security Strategy 2022-2030, combined with heightened geopolitical tension, drove a marked increase in both the volume and sophistication of threats facing public bodies. Today, that challenge is being reshaped once again. The arrival of AI has dramatically accelerated attackers' capabilities through scale, speed and

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automation. At the same time, it offers defenders powerful new tools for vulnerability discovery, threat detection and response. How the public sector balances those forces will define the next era of cyber resilience.

WHY CYBER RISK LOOKS DIFFERENT IN GOVERNMENT Today, public sector cybersecurity remains shaped by confidentiality, integrity and availability. For example, protecting citizen data held by organisations such as HMRC, ensuring systems that store national insurance and pension data cannot be manipulated or corrupted, and keeping critical national infrastructure — including NHS services — available at all times. As a result, cybersecurity requirements in government are more stringent and specialised. Public bodies must operate under heightened regulatory scrutiny and defend systems that cannot simply be taken offline without real world consequences. Security programmes, therefore, must balance resilience, accountability and continuity at scale and be prepared for high levels of public exposure. Consequently, modern public sector cyber security programmes operate under a set of rigorous and highly specific requirements. Secure by design procurement is now a baseline expectation, with vendors required to meet defined security standards and demonstrate that controls are embedded from the outset, rather than added retrospectively. Data

sovereignty and classification rules add further complexity; for example, sensitive defence or law enforcement data often cannot leave UK borders or approved environments. This dictates how information is stored, accessed and shared. Identity and access management are equally critical. With multi agency collaboration and third party contractors commonplace, access controls must be granular, auditable and continuously enforced to prevent misuse or privilege creep. These requirements are rooted in a heightened focus on sovereignty and national security, which most clearly distinguishes public sector cybersecurity from private sector programmes. However, important operational differences also come into play. Public bodies typically rely far more heavily on complex legacy systems that support essential services and cannot be easily replaced or upgraded without significant risk. Slower, more rigid procurement cycles further constrain the pace at which new technologies can be adopted. At the same time, the responsibility for protecting sensitive citizen data and critical national infrastructure increases exposure to nation state and politically motivated attacks, as adversaries seek to steal state secrets, undermine public trust or disrupt economic stability.

AI AND THE NEW CYBER ARMS RACE As with every sector, AI is reshaping the cybersecurity landscape, and the

Image source: Adobe Stock

public sector is no exception. What makes cybersecurity distinctive in this context is its dual role: AI significantly strengthens defensive capabilities while simultaneously enabling more sophisticated and scalable attacks. For public bodies responsible for essential services and national infrastructure, that tension is particularly acute. On the defensive side, AI is helping government security teams detect threats earlier, improve accuracy, and reduce pressure on already-stretched resources. AI driven analytics can identify anomalies across vast, complex estates, including legacy systems, far faster than human analysts alone, while automated response capabilities reduce dwell time and help contain incidents before they disrupt public services. AI has also reshaped security testing itself. Where penetration testing was once a point in time, scoped exercise, effective assurance now requires continuous, automated testing that assesses systemic risk across entire environments and dependencies. With time to exploit reducing from days to hours, regular scanning and AI driven assurance are essential in order to keep pace with adaptive attacks and free up specialists to focus on higher value, strategic work. However, the same technologies are equally available to adversaries. AI now enables the rapid generation of highly convincing phishing campaigns, automated reconnaissance at scale, and faster adaptation to defensive controls.

Attackers can identify vulnerabilities across public facing systems, personalise social engineering efforts, and even exploit deepfake technologies to impersonate trusted officials or suppliers.

WHAT COMES NEXT FOR PUBLIC SECTOR CYBERSECURITY Against this backdrop, the combination of AI enabled threats and sustained attacker focus on critical national infrastructure and defence systems is making public sector cyber security more complex, interconnected and unpredictable. Looking ahead, several forces will shape the next phase of public sector cyber risk. Chief among them is the continued rise in state sponsored activity as demonstrated by the NCSC, which dealt with 204 ‘nationally significant’ cyber attacks against the UK in the 12 months to August 2025 (https://tinyurl.com/ycya32k5). This was up from 89 in the previous year, reflecting growing geopolitical tension and a willingness by hostile states to use cyber operations to pursue strategic objectives. At the same time, space and satellite systems are emerging as prime targets for adversaries. Modern defence operations rely heavily on space based assets for secure communications, navigation, intelligence and the coordination of military, emergency

and transport services. Disrupting or degrading satellite infrastructure can therefore have cascading effects on everything from defence readiness to economic stability, making it an attractive target in both cyber and hybrid conflict. Further ahead, quantum computing poses a long term yet potentially profound challenge. While still emerging, advances in quantum capability could eventually undermine today’s cryptographic standards. For public sector organisations responsible for long lived data and systems, this demands early planning, investment and a clear roadmap towards post quantum resilience.

CYBERSECURITY AS A FOUNDATION OF MODERN GOVERNMENT Cybersecurity in the public sector has moved far beyond perimeter defence or compliance checklists. It now sits at the intersection of national resilience, public trust and geopolitical reality. The challenge ahead is not simply adopting new technology, but embedding security as a strategic, continuous capability, spanning people, policy and platforms. Public bodies must plan for disruption, design for resilience and treat cybersecurity as a foundational enabler of modern government rather than an afterthought.

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TODAY

TURNING INNOVATION INTO BETTER CARE FOR CHILDREN AND FAMILIES

doi:10.1093/itnow/bwag090

Kate Warriner, Chief Transformation and Digital Ofﬁcer, Alder Hey Children's NHS Foundation Trust, talks to Grant Powell MBCS about how AI is being used to reduce clinician workloads, improve patient experiences, and shape the future of children's healthcare while maintaining the highest standards of safety, governance, and trust.

he successful use of artiﬁcial intelligence within healthcare requires much more than simply implementing new technology. It demands strong clinical leadership, robust governance and a clear focus on improving care. At Alder Hey, AI is already delivering tangible beneﬁts through Ambient Voice Technology (AVT), while the Trust's longer-term ambitions span diagnostics, predictive analytics, and digitally enabled support for families. Kate Warriner explains how Alder Hey is translating AI ambition into practical outcomes for children, young people, families and staff.

How has technology, including robotics, improved the hospital experience at Alder Hey? Alder Hey uses a range of technologies to improve patient care and hospital efficiency. These include telemedicine robots, which allow children to communicate remotely with family, friends and healthcare staff via mobile video-screen devices; logistics robots that transport supplies, laundry and waste around the hospital; and a robotic pharmacy system that helps dispense medicines safely and efficiently. The Trust is also exploring advanced

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surgical robots that enable surgeons to perform highly precise, delicate procedures on children. Together, these technologies could help to reduce staff workload, streamline operations and enhance the overall patient experience while supporting innovative healthcare delivery.

Alder Hey has seen encouraging results from Ambient Voice Technology. What have been the biggest benefits so far, and have any outcomes surprised you? Ambient Voice Technology (AVT) has been one of our most successful AI deployments. We began rolling it out in 2024, and now just under 95% of our clinical letters are produced using the technology, with around 1,500 clinicians using it. Previously, much of the documentation process involved typing notes or dictating letters, which were then transcribed, whereas now the AI tool handles much of that work during the consultation itself. For families, the consultation feels more personal because clinicians spend less time looking at screens and more time engaging directly with children, young people and parents. This means more eye contact, better conversations

Image source: Adobe Stock

and a stronger connection during appointments. One of the key benefits for clinicians that we didn't fully anticipate was the reduction in cognitive load, where not having to remember every detail and document it afterwards has significantly reduced the mental burden of consultations. This has been a hugely significant and very positive outcome.

How important have clinicians and leaders been in turning AI from a strategy into solutions that make a real difference? They've been critical. We have a strong culture of innovation at Alder Hey and some fantastic clinical leaders who help drive the adoption of new technologies.

'SAFETY ALWAYS COMES FIRST. WE'RE EXCITED ABOUT THE OPPORTUNITIES AI PRESENTS, BUT WE ONLY DEPLOY TECHNOLOGIES ONCE WE'VE DEMONSTRATED THAT THEY'RE SAFE, EFFECTIVE AND CLINICALLY APPROPRIATE.'

One of our greatest strengths is that our clinicians are not just willing to use new tools; they actively lead change and shape how those tools are implemented. We have chief clinical information officers, a chief nursing information officer and a chief admin and operational officer working together as a multidisciplinary team. Clinical leadership helps ensure that digital innovation aligns with the needs of patients and staff.

Safety always comes first. We're excited about the opportunities AI presents, but we only deploy technologies once we've demonstrated that they're safe, effective and clinically appropriate. Every solution goes through rigorous governance processes, including information governance reviews, data protection impact assessments and clinical safety assessments. We also maintain an AI register and ensure appropriate oversight of everything we deploy.

We're also fortunate to have strong support from our board and executive team. There's a real ambition across the organisation to be at the forefront of digital innovation, which helps create a culture where people feel encouraged to embrace new technologies.

Families are central to the process. We have active forums involving children and young people, and we engage with them whenever we're looking at introducing new technologies. We want to understand their concerns and make sure their voices are heard.

Being a children's hospital also influences our approach. Children and young people have grown up with technology all around them, and they expect organisations like ours to be forward-thinking. That expectation helps reinforce our commitment to innovation.

Consent is also essential. For example, whenever AVT is used, families are asked for permission beforehand. If they don't want it used, we don't use it. By involving families early and being transparent about what we're doing, we've been able to build trust while continuing to innovate.

How do you balance AI innovation with ensuring it is safe, effective and trusted by families?

How is Alder Hey ensuring that patient data is protected and that AI outputs can be trusted?

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Protecting patient data is completely non-negotiable for us. Any technology we deploy must meet the same rigorous standards we apply to all healthcare systems. That includes strong information governance, cybersecurity and privacy requirements. We work closely with information governance specialists, our data protection officer and our clinical safety officers whenever we assess new technologies. Those experts are involved from the very beginning to ensure privacy and safety are built into the process rather than added later. We have strong expertise within the organisation and robust governance arrangements that enable us to assess innovative technologies before they enter clinical use. For us, trust is fundamental. Innovation only succeeds if patients, families and staff have confidence in the systems being used.

How do you assess AI’s potential return on investment? Every investment goes through a structured business case process where we assess expected benefits and track them throughout implementation. In some cases, there are clear financial savings because new technology replaces older systems or removes inefficient processes. In other cases, the benefits are less tangible and can be harder to quantify. Again, our use of AVT provides a good example. Previously, a clinician would dictate a letter, a transcription team would type it, it would be reviewed and then processed before being sent out. Now the letter can be generated, reviewed with the family, and signed off during the consultation itself. This removes a significant amount of administrative work. We don't just look at financial benefits. We assess technologies across safety, quality, productivity, efficiency and experience. Some solutions help identify deterioration in patients more quickly, creating important clinical and safety benefits alongside any cost savings. Healthcare organisations must consider the full value of innovation rather than focusing solely on financial return.

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Which areas of AI do you believe have the greatest potential to transform children's healthcare over the next five years, and why? Several areas are particularly exciting, such as the opportunity to use AI to improve flow and efficiency. As a digitally mature organisation, we have strong data foundations, and AI can help us better utilise that information to improve the way care is delivered. Predictive and personalised care is another major opportunity. AI has the potential to help us identify risks earlier, better understand population health needs and tailor care more effectively to individual patients. Diagnostics is also an area with enormous potential. In paediatrics, there has generally been less progress in diagnostic AI than in adult healthcare, so there's a significant opportunity to improve outcomes for children and young people. Alongside that, we're seeing exciting opportunities emerge through collaboration. Earlier this year, we signed a memorandum of understanding with five other UK paediatric centres and a world-leading paediatric hospital, SickKids, based in Toronto, and launched collaborations with various government partners to share learning on AI. Those partnerships will play an important role in shaping future innovation. Finally, I think AI has huge potential to empower families by helping them

access services more easily and become more active care participants.

What does success look like for Alder Hey’s AI strategy, and what are the next major milestones? Our strategy is centred on empowering everyday lives through AI. For colleagues, success means AI becoming part of everyday work and helping people do their jobs more effectively so they can focus on delivering excellent care. For children, young people and families, success means putting a digital version of the care we provide into their hands and enabling them to access support in new and more convenient ways. Partnerships will be central to that vision. We already work with a range of organisations through programmes such as the Paediatric Open Innovation Zone, and we're continuing to develop new collaborations. Our next major milestones involve progressing several new international partnerships and exploring opportunities across urgent and emergency care, wider hospital services and community-based care pathways. Ultimately, success is about bringing together the expertise of many different partners and using AI to create meaningful improvements for children, young people, families and staff. That's the future we're working towards at Alder Hey.

Image source: Adobe Stock

TODAY

MEMBER SPOTLIGHT: JOHN KEEGAN FBCS

John Keegan FBCS discusses the journey to Fellowship and the leadership principles that have deﬁned his career across government and industry.

rom building capability to shaping culture, John Keegan FBCS, Deputy Director Department for Work and Pensions (DWP), talks to Grant Powell MBCS about leadership, public impact and the people-ﬁrst foundations of modern cybersecurity. He also shares his insights on Fellowship of BCS, what this means and the responsibility that comes with it.

What aspects of your career and professional contribution do you feel most strongly demonstrate the standards expected of a Fellow? I’ve built my career over many years around enabling teams to succeed — helping people develop their skills, grow their careers, and ultimately deliver meaningful outcomes. For a significant portion of my career I worked in the private sector, developing and delivering services for government. That work was both challenging and rewarding, and gave me a strong foundation of knowledge and skills.

Throughout your career, you’ve managed a variety of programmes. What lessons have you learned

Resilience comes from strong foundations: clear structures, robust planning, and the right skills around you. It’s also about creating environments where issues can be identified and addressed early, with fallback options in place. Ultimately, sustainable capability is built on a combination of trust, challenge, and disciplined execution.

How do you believe experienced leaders should contribute back to the profession and help shape the future of cybersecurity and ICT? There are a few areas where experienced leaders can make a real difference. First, sharing knowledge and experience is vital — whether through mentoring, coaching, or simply being open about what you’ve learned over time. Supporting those early in their careers, or those developing into new roles, is an important responsibility. Second, I strongly believe in the value of both mentoring and reverse mentoring. Learning should never stop, and engaging with people from different backgrounds, generations, and perspectives helps challenge your own thinking and identify blind spots.

Reflecting on your journey to Fellowship, what advice would you offer to experienced practitioners who are aspiring to senior recognition but may underestimate the value of their impact and leadership? My first piece of advice is: don’t rush. You’ll know when the time is right to apply, and that’s when you can clearly evidence your impact and reflect confidently on your experience. That said, don’t hesitate to test the waters. Speak to existing Fellows, seek feedback, and explore where you stand. The application process itself is valuable, even if you’re not immediately successful. It helps you understand the expectations and identify any gaps. I’ve supported a number of colleagues through Fellowship and also been involved in assessment, and one consistent message is that people often underestimate their own impact. So, don’t be afraid to put yourself forward. Embrace the process, learn from the feedback, and treat it like any other professional challenge. If you’re not quite ready, you’ll gain clarity on what to focus on next. That, in itself, is progress. Read the full article and find out more about becoming a BCS Fellow: https://tinyurl.com/t3ak2x95

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doi:10.1093/itnow/bwag091

To me, Fellowship is not just about individual achievement; it’s about impact. It’s about contributing to the public good while also investing in the next generation of talent who will lead both government and industry in the future.

When it comes to the technology, one of the most important lessons is simple: check, test, and check again! In terms of people, you need to build teams you trust - teams that can deliver! You also need to challenge assumptions and validate decisions. Challenging constructively is essential to avoiding failure or, at the very least, ensuring that when things do go wrong, they do so in a controlled and recoverable way.

Finally, contributing to the profession is about more than technical expertise. It’s about fostering inclusivity, encouraging diversity of thought, and recognising that no one has all the answers. Strong teams, and strong professions, are built on collaboration and continuous learning.

Over the last decade, I’ve transitioned to working client-side within DWP, where my focus has been on building internal capability. That shift has been particularly important to me — creating sustainable, in-house expertise that directly supports people, whether that’s through helping them back into work or ensuring timely payments.

about building resilient, sustainable technology and security capabilities at scale?

Congratulations to our recent BCS Fellows! Andrew Roden Jeremy Chapeau Balaji Salem Balasundram Abdul Muqtadir Mohammed Balaji Thadagam Kandavel Jitender Jain Abu Saleh Mohammed Mahbubul Alam Balakumar Kunthu Joe Kelly Adam Ennamli Balkrishna Patil John Anand Lourdusamy Adedoyin Williams Owajoba Bhagavathi Sathya Satish Kadiyala Josson Paul Kalapparambath Aditi Mallesh Bhanu Kiran Kaithe Joyal Shah Aditya Vikram Kashyap Bhaskar Gurram Joyjit Roy Ahmed Mohamed Bhaskara Reddy Udaru Jun Liu Ajay Guyyala Bhavesh Jaisinghani Justin Davis Akhilesh Bollam Brajesh Kumar Shrivastava Karan Alang Kartheek Medhavi Penagamuri Shriram Akhilesh Kota Brian Roche Karthikeyan Thirumalaisamy Akinniyi Samuel Dr Carla Di Cairano-Gilfedder Kiran Kumar Jaghni Akshat Vaid Chaitanya Kulkarni Kranti Kumar Appari Akshay Nagpal Chris Whitehead Krishna Anumula Amaan Javed Daniel Liam Broomhead Kulbir Singh Dr Amir Aly Danish Khan Kuldeep Deshwal Amit Kumar Padhy Deepak Kole Kumar Rajiv Nayan Anat Heilper Dhanveer Singh Kunal Parekh Andrew Csizmadia Dharmateja Priyadarshi Uddandarao Lakshit Arora Anil Kumar Anusuru Dharnisha Narasappa Dr Lakshmi Manasa Gaduputi Anish Kumar Jain Diganta Sengupta Larry Tampkins Anjan Kumar Ojha Dileep Rai Latha Ramamoorthy Anjan Kumar Ayyadapu Dinesh Kumar Garg Long Cheng Ankur Khare Dmitriy Malygin Louw Fouche Ankur Aggarwal Dr Emmanuel OGU Dr Lushan Weerasooriya Ankur Partap Kotwal Femi Bamisaiye Manikandan Sampathkumar Ankush Sanjay Mahajan Gabriel Tosin Ayodele Manoj Kumar Vunnava Anna Kovalova Gaurang Deshpande Mansoor Ahmad Khan Dr Anne Marie Cunningham Gerard Rego Manushi Manish Sheth Anoop Kunjuraman Pillai Saraswathy Amma Girish Wali Anthony Simon Croudass Dr Mark Thompson Gowdhaman Sadhasivam Anton Kosiakin Mark Quentin Lewis Dr Hewa Balisane Aravind Nuthalapati Mark Stuart Smith Hrishikesh Paranjape Aravind Narayanan Matt Mason Hritesh Yadav Archana Rajendran Dr Matthew Stewart Ifan Evans Michael Lafferty Artem Kuznetsov Ilia Sergunin Arun Chaudhary Dr Indrajit Sen Arun Sugumar Jagadeesh Vasanthada Arun Kumar Palathumpattu Thankappan James Bore Dr Aryendra Dalal Jason Scott-Taggart Ashif Anwar Jay Shah Ashish Bhardwaj Jay Prakash Thakur Ashvini Kumar Jindal Jaykumar Ambadas Maheshkar Avishkar Nikum Jeevan Shanbhag

ITNOW | Issue 3 2026

Mohamad El Abiad Mohammed Misbahul Khair Mohammed Safwanullah Shareef Mohit Sachdeva Mohit Bharti Mona Arora Monu Sharma Mrinal Ahlawat

Mrinmoy Aich Ravindra Gogineni Sridhar Korimilli Muhammad Amir Quraishi Riaz Ahmed Mohammed Sait Srinivas Murri Muzeeb Mohammad Ripunjaya Pattnaik Srinivasan Narayanan Nagmani Lnu Rishi Nareshbhai Lad Srishti Mukherjee Narayanaswamy Ramajayam Rohit Nimmala Stanislav Yermolov Naresh Dulam Rudra Sinha Sudeep Annappa Shanubhog Natarajan Ravikumar Sudhahar Jayapalan Dr Sabesan Sithamparanathan Nikhila Pothukuchi Sujan Das Sachin Gadiyar Nirupam Samanta Suman Chandra Sharma Sahil Agarwal Nishanth Prakash Sundeep Bobba Sai Chaithanya Pallaprolu Ohm Hareesh Kundurthy Sunit Prakash Sai Kalyan Reddy Pentaparthi Oleg Samoylov Suraj Kath Sairam Vangapally Oleg Fleitman Suvodeep Pyne Sairohith Thummarakoti Colonel Oliver Bartels Syed Ahmad Sampath Kumar Mucherla Olumide Abiola Tannu Jiwnani Sandeep Dommari Omorinsola Goriola Tarun Mathur Sandeep Voona Omotayo Omoyemi Dr Tatiana Habruseva Sanjay Paul Parth Saxena Tom Natt Santosh Nazare Petre Cristian Ilie Tosin Ojo Santosh Praneeth Banda Piyush Sagdeo Vadym Semeniuk Sateesh Rao Pala Pooja Jhunjhunwala Vasdev Gullapalli Saumen Biswas Pooja Chitrakar Venkata Durga Ganesh Nandigam Saurabh Srivastava Pradeep Kumar Muthukamatchi Venkata Reddy Pasam Saurabh Aggarwal Pradeep Kumar Sharma Venkata Sai Reddy Kotu Scott Quilter Praneeth Reddy Amudala Puchakayala Selvamurugan Ramamoorthy Venkata Siva Prasad Bharathula Venkateshwarlu Koyeda Serhii Savin Pranjal Mallick Venus Garg Shankar Raj Prathap Abbareddy Sharad Jain Pratikkumar Dilipkumar Patel Vigneshwaran Jagadeesan Pugazhenthi Sharanya Vasudev Prasad Preetish Kakkar Vijay Kumar Reddy Atla Sharat Priya Prem Kumar Tamanam Vijayanand Balasubramaniam Sheetal Kaul Pritam Karmakar Vijayaraghavan Govindarajan Shelly Chen Priyadharshini Krishnamurthy Vineel Muppa Shib Shankar Golder Pruthvi Tatikonda Vinodhini Chandrasekaran Shikha Gupta Radha Sampathkumar Vinodhkumar Gunasekaran Shravan Kumar Joginipalli Rajesh Vayyala Vinopriya Vijayaboopathy Shweta Venkateswaran Rajesh Purushothaman Viswanathan Ranganathan Dr Siân Lindley Rajesh Vakkalagadda Viswapriyan Ragupathy Siddarth Kumar Choudary Rajeshkumar Rajubhai Golani Vivek Venkatesan Siddharth Dixit Rajiv Peter Vyom Mittal Siddharth Raina Rakesh Pai Yaroslav Polyakov Siva Teja Reddy Kandula Rakesh Ranjan Sukla Yauheni Kanavalik Sofia Iskandarova Ran Tao Sreejith Kaimal Rangarajan Ramanujam Ravi Mani

Sri Krishna Ravulapalli

BECOME A BCS FELLOW Join the tech industry’s most influential professionals in shaping a diverse, ethical, safe, and innovative future. Find out more

ITNOW | Issue 3 2026

TODAY

A SIX-STEP METHOD TO INVESTIGATE DATA QUALITY Good data is the ﬁrst step on any project. Professor Roy Ruddle provides a checklist to help you ensure your data is prepared correctly and that unpleasant surprises are avoided.

cardiologist once told me that their team had been sitting on a dataset for weeks but couldn’t open it. It was simply too large to inspect. Three lines of code later, I let them see the values in a selection of rows for the 116 columns. Within minutes, they could begin to check the data ﬁle’s contents. That experience shaped one of the simplest rules in data quality: look at the data before you do anything else.

doi:10.1093/itnow/bwag092

Most data science projects spend more time preparing data than analysing it, yet data quality is rarely approached with the same rigour as modelling. In practice, checks are partial and reactive. Issues surface late, assumptions go untested and teams end up reworking analysis they thought was complete. Across interviews and work with practitioners in more than 15 sectors, the pattern is consistent. People want to do this properly, but lack a clear, usable method. This article outlines a six-step approach designed to make data quality systematic rather than incidental. The method draws on a rulebook of 69 tasks, each framed as questions to ask of your data. The aim is not to add burden, but to provide structure so that important checks are not missed and effort is not wasted revisiting problems later. The steps are organised to avoid rework and reduce the risk of unpleasant surprises late in a project.

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STEP ONE: IS ANYTHING OBVIOUSLY WRONG?

features may be created. Both lead to incorrect results.

There are two aspects to this. The first is simple — is there any documentation? When it exists and is reliable, it defines variables, formats, units, ranges and relationships. Machine-readable documentation should be treated as the most valuable dataset you have.

Systems that return zero when they fail are more problematic because those false values do not tend to stand out numerically from valid values.

The second aspect is basic data inspection. Simple checks such as empty columns or unexpected file sizes can reveal missing data at the outset. Looking at example values, such as the first and last rows, helps establish formats and often surfaces issues that need further investigation in subsequent steps.

Once special values are understood, missing data can be assessed properly. However, missingness is not limited to individual values. It also includes coverage and granularity. Coverage may be incomplete across time, geography or population. Granularity may be too coarse or inconsistent for the intended analysis.

These checks take minutes but can prevent days of rework.

If the data is insufficient, additional data may be required. That may feel like a step backwards, but it is far better than discovering the limitation when you thought your analysis and modelling were complete.

STEP TWO: WATCH OUT FOR SPECIAL VALUES Many data scientists rely on software defaults to detect missing values. That is risky. Defaults can be case-sensitive (for example, the Python Pandas package treats ‘None’ as a missing value but not ‘NONE’), and genuine values can match those defaults. Datasets also use their own conventions. Sentinel values such as 99 or dates such as 1801-01-01 are common. If these are not identified early, the values will be silently included in calculations or false

STEP THREE: IS ANY DATA MISSING?

Duplicate records are the inverse problem and surprisingly common. They should also be checked for and addressed in this step.

STEP FOUR: CHECK EACH VARIABLE With missingness understood, attention turns to the values themselves. Variables should be checked individually for format, type, validity

Image source: Adobe Stock

and uniqueness. Additional checks apply depending on the data type; categorical variables may contain misspellings or inconsistent labels, and numerical variables may contain noise or extreme values. Machine-readable documentation is particularly useful here. It allows checks to be automated against the values that are valid for the application domain, while also revealing inconsistencies in the documentation itself. Dates and times should be examined at multiple levels of detail. The overall distribution can often mask patterns or issues at the level of month, day of week, hour or minute. That is particularly true if the data spans a large period.

STEP FIVE: CHECK COMBINATIONS OF VARIABLES Data rarely exists in isolation. Relationships between variables are often defined by business rules or functional dependencies. Therefore, you should begin this step by writing down those rules and dependencies. To that you should add any assumptions that you make. Each variable should be included at least once.

Next, those rules, dependencies and assumptions should be tested against the data. It is common to assume they hold, but in practice they often do not. Sometimes the data is wrong. Sometimes the assumption is. The result is a clearer understanding of what can be trusted, what cannot and where assumptions need to change.

STEP SIX: PROFILE THE CLEANED DATA The purpose of the first five steps is to identify and address data quality issues so that the dataset is suitable for analysis or modelling. The data should be cleaned where practical as those steps progress, and any remaining issues documented. The final step of the method is to profile the cleaned data. This involves repeating some checks from steps three, four and five, but now they describe the dataset that will be used in practice. This also provides a baseline understanding and a reference point for future work that utilises the data.

CONCLUSION Years ago, an economist described how a statistical method only became widely adopted once someone made it easy to use. Data quality faces the same challenge. The principles are known (ISO/IEC 25012:2008 Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model) and certain terminology is in widespread use (https://tinyurl.com/53fvzntp), but data scientists and analysts lack an essential ingredient. The aim of this method is to provide that missing ingredient — a concise (8 page) data quality rulebook, written in plain English and easy to use in everyday work. The 69 tasks are mapped onto the six steps in an order designed to help you detect major issues early and minimise re-work. The method is openly available from: https://tinyurl.com/6stepDQ. Roy Ruddle is Professor of Computing and Director of Research Technology, Leeds Institute for Data Analytics (LIDA), part of the School of Computer Science, University of Leeds, UK.

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TODAY

THE FIVE DATA ARCHITECTURE MISTAKES THAT END YOUR ENTERPRISE AI INITIATIVE (AND WHAT TO DO ABOUT IT) Enterprise AI initiatives do not usually fail because the model is wrong. They fail because the data infrastructure underneath was not built to support it. After 15 years building enterprise data architecture across clinical research, ﬁnancial services and healthcare, Milan Parikh, Lead Enterprise Data Architect at Cytel, explores the ﬁve most common mistakes.

he ﬁve most common data architecture mistakes are not exotic — they are predictable, and preventable.

doi:10.1093/itnow/bwag093

Here is what they look like, why teams make them, and what to do instead.

MISTAKE ONE: BUILDING THE DATA PIPELINE AFTER THE MODEL Most organisations treat pipeline development as delivery. First, there is model development, then there are requirements, and finally, someone hands out a ticket with instructions for getting data from point A to point B. It works in theory. In practice, it creates a fragile pipeline that breaks the moment requirements change. This is what happened when my team needed to migrate data from

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SQL Server into Dataverse. The SQL schema was huge and constantly changing according to business needs. Meanwhile, Dataverse has a fixed schema. As each new requirement popped up, I had to rewrite the entire pipeline actions set again. There was no problem with data migration per se; the problem was in designing the whole pipeline based on a point-in-time snapshot of requirements, instead of assuming that schema drift would happen. A middle ground solution was the transformation layer. Instead of relying on hardcoded column-to-column connections, the transformation step could absorb all the changes in the source schema. Only variables would be adjusted. Thus, the entire pipeline would withstand any new requirements, without requiring total reconstruction.

Be prepared for those requirements that you don't know yet.

MISTAKE TWO: TREATING DATA GOVERNANCE AS A REGULATORY CHECKBOX Without any guidance, different departments design and implement their data structures to solve their own issues. Each department is solving a real problem, but there are issues in terms of siloed data, duplication, conflicting ownership and lack of a single source of truth. This kind of situation emerges slowly, then becomes an issue suddenly; for instance, a governance discussion in our organisation resulted from some compliance and audit issues. The solution was not to write a policy document. Instead, we adopted medallion architecture with a certified

layer above the raw and aggregated layers. This was where governance would be: controlling permissions and endpoints to access different resources. No data would reach consumers or reporting tools without going through this layer. In other words, governance is not about filling out a form before go-live.

MISTAKE THREE: DESIGNING FOR DEMO RATHER THAN PRODUCTION Deadlines result in a unique kind of tunnel vision. The team needs to make sure that the demo is done, MVP is ready to ship, and the stakeholder presentations are prepared. It sounds right, but consistently misses the same thing. A project management and accounting application was created and successfully tested. Then came the launch. What was overlooked in this case were multiple security roles for each user, as well as other things which could have been checked during testing but which were ignorable under a tight deadline. This problem is common; everything works until someone whose job is to use the product starts using it. Additional testing can’t solve this issue; checklists, documentation and end-user sign off can. A demo shows that the product works. Production readiness shows that it works for everybody involved.

MISTAKE FOUR: DEFERRING DATA LINEAGE UNTIL AN AUDIT FORCES IT Data lineage is that feature everyone agrees on but that few teams will prioritise until something breaks. In a large enterprise reporting environment, fragmented data lineage is invisible in a normal day because sources are only partially documented; column-level lineage may not have been implemented completely. Data pipelines will run, checks will pass and reports will be delivered right on schedule, and no one will notice the problems until something breaks.

A senior stakeholder decides to check a report. Nothing is broken, but there is missing data for sure. Someone has dropped a source table from the lineage tracking without generating any notification. In an environment with hundreds of data tables available, it takes two days to figure out which table it was, and it’s only possible by tracing back to the source manually from the pipeline. Two days to find an answer that clean lineage could have produced in minutes. The price of fragmented lineage is clear in such situations, because that is when you need visibility in your pipeline the most. This problem can be avoided by building lineage right into layer transitions instead of documenting it afterwards. Source tables tracking and column-level lineage become an integral part of data pipelines. Build lineage into your systems; don't document it afterwards.

MISTAKE FIVE: DEPLOYING AI INTO A MONOLITHIC ARCHITECTURE A monolithic architecture seems to make perfect sense in the beginning. One data storage, one source code, everything consolidated into one place. But it works well only when there is little data and very few use-cases. People do it with the assumption that it will suffice for now. Six months later, that assumption becomes a structural liability. I analysed the experience of an organisation that consolidated its data architecture like this, and then built its AI on top. Once the data volume started to grow, duplicate models were created to extract different pieces of information without isolating them properly. That led to the breakdown of any naming structure, and the once neat architecture became bloated and convoluted. In such conditions, everything wrong with the data architecture translates into an issue with your AI project: data duplication leads to inconsistent training sets; lack of proper naming structure makes it hard to establish which model consumes which source;

Image source: Adobe Stock

and with the growth of complexity, drift in the models' performance occurs. Some models even receive conflicting inputs. The solution lies in separation. By creating a layered architecture — raw, aggregated and certified — you ensure that updates can be made separately for each layer. Models work with certified data rather than raw sources, so retraining one does not affect the rest of the system. The more data and models accumulate, the more pressure is put on the architecture.

THE COMMON THREAD None of these are technological failures and all of them are avoidable; they are planning decisions made too late. They require decisions about pipeline design, governance, production readiness, lineage and scalability of the architecture made earlier than most teams are comfortable with — but that discomfort is far cheaper than rebuilding a data ecosystem after an AI programme failure. Milan Parikh FBCS, Lead Enterprise Data Architect, Cytel, CES Innovation Awards 2026 Judge, IEEE Senior Member, and Secretary for BCS Enterprise Architecture Group.

ITNOW | Issue 3 2026

TODAY

BUILDING TRUSTED PROFESSIONAL STANDARDS Professor Edward Rochead, Mathematical Engineering Strategist at the UK Ministry of Defence, talks to Grant Powell MBCS about the role of mathematics, data science and professional accreditation in creating a trusted, ethical and effective digital future.

rofessor Edward Rochead has spent more than two decades applying mathematics to some of the government’s toughest challenges. Now Deputy Head of the MOD Science Function and Chair of the Alliance for Data Science Professionals (https://tinyurl.com/3pp2p35x), he explores how the combination of mathematics, data analytics and professional oversight can support a safer, fairer and more dependable digital landscape.

doi:10.1093/itnow/bwag094

Please introduce yourself and provide some background on your career. I've worked in government since graduating from Loughborough University with a Master of Mathematics degree in the late 1990s. I began my career as a mathematical modeller in the Ministry of Defence, developing models to support analysis and decision making, before moving into operational research, applying mathematics to improve business decisions across defence. Over time, I took on roles in strategic partnerships, programme management and research commissioning, eventually becoming chief mathematician of the Defence Science and Technology Laboratory. Today, as Deputy Head of the MOD Science Function, I focus on the foundations that enable defence science to succeed, including skills, data, professionalism and assurance. Alongside my government career, I've

ITNOW | Issue 3 2026

remained active in professional bodies. I'm a Chartered Mathematician and Fellow of the Institute of Mathematics and its Applications, and I currently chair the Alliance for Data Science Professionals (AfDSP), which brings together organisations including BCS to maintain professional standards for data science practitioners.

From your perspective, what skills or ways of thinking transfer most effectively into data science? Many mathematicians already possess a significant proportion of the skills needed for data science, but there isn’t a single pathway. I tend to think of data science as bringing together three key elements. First, there is access to, management of and ethical stewardship of data. Second, there are the mathematical and statistical algorithms applied to that data. Third, there is the computing and coding needed to make everything work in practice. Different mathematics graduates arrive with different strengths across those areas. Someone with a strong statistical background may already be very comfortable with data analysis and modelling, but may need to strengthen their computing skills. Others may have substantial programming experience but require additional training in statistics and data science methods. The common thread is that mathematicians are trained to think logically, tackle complex problems and develop rigorous solutions. Those

skills transfer extremely well. The challenge is identifying your personal gaps and finding ways to address them through professional development and accreditation.

Data science draws from multiple disciplines. How important is it to establish a recognised professional standard across such a diverse field? I think it’s absolutely vital. Data science increasingly influences decisions that directly affect people’s lives. If it is carried out poorly or without appropriate ethical considerations, real harm can result. Professional standards help assure that practitioners have the knowledge, competence and ethical awareness needed to work responsibly. A recognised professional standard creates a common benchmark. There are practical benefits too. It helps employers identify capable professionals, supports recruitment and career progression, and gives practitioners a clear framework for development. One of the strengths of the AfDSP is that it brings together multiple professional bodies, including BCS, the Royal Statistical Society and others, to work towards common standards. That collaborative approach preserves the diversity that makes data science such an innovative field while ensuring consistency in professional expectations. Cross-disciplinary working is one of the

greatest strengths of data science, and common standards help us retain that advantage.

For mathematicians or other STEM professionals considering a move into data science, how valuable is formal accreditation, such as the AfDSP, in demonstrating credibility and competence? Formal accreditation can be incredibly valuable. One of the early AfDSP registrants described it as giving them a ‘professional home’. In their case, they had originally trained as a statistician but no longer felt that traditional statistical accreditation fully reflected the breadth of their work. The data science accreditation recognised the wider combination of skills and responsibilities they had developed throughout their career. Image source: Adobe Stock

I think that experience resonates with many professionals. People often evolve beyond a single discipline as their careers progress. Data science accreditation provides a way of recognising broader expertise while still maintaining links to established professional bodies, such as BCS. For individuals, accreditation demonstrates professional commitment, capability and competence. For employers, it offers external validation that someone’s knowledge and skills have been independently assessed against recognised standards. That can be particularly valuable when organisations are seeking trusted practitioners to lead increasingly important data-driven activities.

In a rapidly evolving field like data science and AI, how important is CPD in maintaining both technical expertise and ethical standards? Data science and AI are changing at an extraordinary pace. New tools, methods and applications emerge constantly, which means professionals have to keep learning throughout their careers if they want to remain effective. Equally important is the ethical dimension. When I studied mathematics, ethics barely featured in the curriculum; today, that would be unthinkable. Ethical awareness is now a fundamental

I believe that professional bodies have a crucial role to play here. They provide access to ethics training, specialist events, conferences, seminars, mentoring and professional networks. Those opportunities often expose practitioners to ideas and experiences they would never encounter within their own organisation. For example, I recently discussed AI applications in both the power sector and flood management. Although those sectors are very different from my own, the conversations generated insights that could be applied elsewhere.

helps demonstrate that practitioners are committed to working competently and ethically. It reinforces the principle that technical capability alone is not enough; professionals must also understand their responsibilities and the wider impact of their work. As AI and data-driven technologies become increasingly embedded in everyday life, public confidence will depend not only on the systems themselves but also on trust in the people who develop and use them. Professional accreditation helps provide that assurance. It shows that individuals have met recognised standards and that ethical practice forms part of their professional identity.

Professional bodies also provide access to mentors and peer networks. This can be especially important for people working in smaller organisations, where a senior specialist may not be available to support their development. CPD through a professional body, therefore, complements employer-led training by giving members access to a much broader professional community.

In many ways, that reflects one of the traditional foundations of professional status. For generations, professions have earned public trust by demonstrating competence, accountability and ethical behaviour. Through initiatives such as the AfDSP, the data science community is applying those same principles to the challenges and opportunities of the digital age.

Ultimately, how do you see professional accreditation helping to build trust in data-driven work?

Learn more about the Alliance for Data Science Professionals: https://tinyurl.com/3pp2p35x

professional competency.

At its core, professional accreditation

ITNOW | Issue 3 2026

TODAY

CODING AGENTS NEED GUIDANCE, NOT FAITH Coding agents powered by multimodal models are becoming increasingly capable. They explore and write code, ﬁx tests, even coordinate subagents for complex changes. Used well, they expedite development and help deliver solid software at pace. Used badly, they produce conﬁdent nonsense at machine speed. Tim Clark guides you to success.

tart by building a good foundation. An instruction ﬁle, like AGENTS.md or CLAUDE. md, is the ﬁrst step to guiding agents working with your codebase. Effective AGENTS.md files are written by humans (not autogenerated), with concise guidance on how your codebase is unique. Agents know a JavaScript codebase likely has a package.json, but it doesn’t know your code style, security concerns or application architecture. Tell it what it can’t figure out.

doi:10.1093/itnow/bwag095

If you already have good machinereadable documentation, don’t repeat it. Well-maintained documentation in a format like Markdown is an excellent way to help guide agents (and humans) to write better code.

effort will try to deeply understand your instruction, the structure of the codebase, and will ‘think’ hard to determine the best solution. It often locates or creates unit tests to verify successful completion. That’s perfect for a complex new feature. It’s not ideal for a quick change. For that, you may wish to use lower effort. You will likely find that medium effort is great for most feature work, but sometimes lowering this further still produces good results.

and figures out what it should do, presenting this information as a plan that you can approve, alter, or outright reject. This prevents agents from digging a hole you have to get them out of half an hour later. Instead, they explain their approach, what it will touch, and how it will verify the change is correct.

BEST LAID PLANS

This is probably the most impactful change you can make to improve your workflow, especially if you work with a large codebase. AI agents often behave like someone who forgets everything about the codebase, each session.

Plan Mode explores your codebase

Add guidance in your user-level

Concision and maintainability are paramount. Agents get distracted by irrelevant or outdated information. Be selective. Commit to a small set of agent-specific guidance, and point it towards specific information, if needed. If writing frontend unit tests, it probably doesn’t need your database design principles. If you have guidelines for specific file types, you can add path-specific ‘rules’, a great place for specific code style.

WORK SMARTER, NOT HARDER Many agents will allow you to set their effort level. This concerns how efficiently it consumes tokens. Higher

ITNOW | Issue 3 2026

Image source: Adobe Stock

'THE BEST WAY TO GET BETTER RESULTS IS TO SIMPLY WORK WITH AGENTS, USE THEM BADLY, ITERATE AND IMPROVE... CODING AGENTS AREN’T MAGIC. WITH CONSISTENT PRACTICE, CLEAR REQUIREMENTS AND EFFECTIVE REVIEW, THEY CAN TAKE YOUR WORKFLOW TO THE NEXT LEVEL.'

AGENTS.md, encouraging agents to favour concision when writing plans. This makes them easier to skim before approving.

TIGHTEN THE BLAST RADIUS AI agents can behave more reliably if you scope work to concrete files, directories, or a set of changes. We’re not always good at communicating requirements, but being specific guides the agent to produce reliable output. Make sure you mention any manual fixes you’ve made before moving on. Agents know what they’ve done, what they’re told, and what they read in the code. If you touch up its changes, it may think they were ‘removed by the linter/ build’ or similar, and revert. Tell it when it makes a mistake, and steer it in the right direction. It may even be easier to get it to fix it for you.

WHAT’S THE CONTEXT?

EXPERIMENT!

AI agents behave quite similarly to humans — they lose focus, miss details and get distracted by contradictory information. ‘Context’ refers to everything your agent is currently thinking about.

AI models are non-deterministic so behave very differently to other programming tools. But they are still a tool.

Imagine it like a conversation. You probably remember why you’re talking, and what was just said, but you’d struggle to recall details from a few minutes ago. Agents have a similar ‘context window’ so providing too much detail not only leads to wasted time, more token consumption, and therefore a larger bill, but it can fail to complete the task. Called ‘context rot’, this is where important information becomes buried amongst many tokens. To fix this, ensure you ‘compact” the conversation history so important details can surface. For a new feature, you may need a fresh session.

The best way to get better results is to simply work with agents, use them badly, iterate and improve! You may find manual corrections speed things up sometimes, but steering the agent instead may mean it is more productive in the long term. Coding agents aren’t magic. With consistent practice, clear requirements and effective review, they can take your workflow to the next level. Timothy Clark is a full–stack software engineer, Chair of the BCS Early Careers Executive and Chair of the BCS Preston and District branch. LinkedIn: https://tinyurl.com/mryam6y7

ITNOW | Issue 3 2026

TODAY

TOWARDS A LEARNINGORIENTATED APPROACH TO CYBER SECURITY For all the focus on tools and technology, cybersecurity remains a deeply human challenge. Matthew Mackay, Security Practice Lead at Logiq, explains.

ybersecurity is inherently sociotechnical, shaped by the interaction of people, processes and technology. Despite this, the human element is too often overlooked. The difference between organisations that recover and strengthen and those that suffer lasting damage lies in their ability to learn, adapt and continuously improve. Open feedback loops, where organisations actively use failure data to adapt systems and prevent recurrence, are therefore not simply a ‘nice-to-have’. These three distinct perspectives converge on the same truth: in cybersecurity, we must learn from our mistakes.

doi:10.1093/itnow/bwag096

CYBERSECURITY IS A HUMAN SYSTEM BBC Cyber Correspondent and author Joe Tidy’s account of the Kivimäki breach of a Finnish psychotherapy provider in ‘Ctrl-Alt-Chaos’ is a stark demonstration. The attack itself was not technically groundbreaking. The real failure lay in the organisation’s inability to recognise the sensitivity of the data it held, to implement basic controls, and to prepare for the consequences of a compromise. The result was catastrophic. Tens of thousands of individuals were personally extorted using their most private therapy records. This was not just a data breach; it was a large-scale human crisis.

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What this incident exposes is that cybersecurity failures are rarely about a single vulnerability. More often, they are systemic failures of governance, awareness, prioritisation, and learning. Without mechanisms to detect weaknesses, provide feedback lessons and adapt controls, organisations unknowingly carry risk forward until it manifests.

should be treated as valuable data points to optimise and learn from experience. Each one is an opportunity to strengthen the system before an adversary exploits the weakness.

THE ROLE OF FEEDBACK

In cybersecurity, it is easy to blame a user for clicking a link or an analyst for missing an alert. Still, perhaps the more valuable question is: what flaw in the system made that error likely or hard to detect?

This is where ‘black box thinking’ becomes critical. As author Matthew Syed highlights in the book of the same name, in the aviation industry, every incident is analysed, understood and used to improve the system. This illustrates how high-performing organisations learn from small failures and near misses before they become disasters, resulting in one of the safest industries in the world.

Organisations fail to learn when people are chided for mistakes. In cybersecurity, this is particularly relevant, as teams may hide errors, near misses, or weak controls if they fear reputational or professional consequences. A blame culture suppresses the very information needed to improve security. As aviation has a ‘just safety’ culture, we need a ‘just security’ culture in cybersecurity.

The point is proportionality: highhazard industries such as aviation invest heavily in learning because failure can create large-scale human consequences, not just operational disruption.

CONTINUOUS IMPROVEMENT

Learning does not happen automatically after failure. It requires structured reflection, investigation, and institutional memory. Organisations need mechanisms for capturing lessons, not just good intentions. Cybersecurity demands the same culture and approach. Near misses, minor breaches and audit findings

The book The Phoenix Project (Kim et al.) shows how this learning is operationalised. Its ‘three ways’ of DevOps — flow, feedback and continuous learning — provide a blueprint for turning insight into action. Although this is geared toward the context of an IT department and DevSecOps, the implications are equally relevant to cybersecurity. In cyber security, ‘flow’ means understanding how security controls support business processes end-to-end rather than operating in isolation,

positioning cyber security as a business enabler. ‘Feedback’ means rapidly identifying when controls fail or are bypassed and ensuring that information reaches decision-makers, so that security continues to serve the organisation and protect what matters most. ‘Continuous learning’ means embedding improvements into architecture, processes and behaviours, rather than simply fixing the immediate issue. Organisations often fix the obvious direct cause but miss the deeper root cause; techniques such as the ‘5 Whys’ help expose and address the underlying systemic issue. This is where many organisations struggle. They treat incidents as discrete events rather than signals of systemic weaknesses. As a result, fixes are localised and temporary, rather than structural and enduring.

FROM COMPLIANCE TO LEARNING A recurring challenge, particularly in regulated environments, is the dominance of compliance-driven thinking. Frameworks are implemented, audits are passed and controls are evidenced. However, compliance poses a few challenges. Principally, the illusion of security, but also, how do we learn from experience to ensure we are suitably protecting what truly matters? The Kivimäki case illustrates this clearly. Even where controls exist on paper, without active feedback and continuous improvement, they may be ineffective and possibly untested in practice. True cyber security maturity comes when organisations move beyond asking, ‘are we compliant?’, to asking, ‘what have we learned, how does this align to our risk appetite, and how can we improve?’.

CREATING A LEARNING CULTURE At its core, this is a cultural challenge. Open feedback loops depend on psychological safety, so that people feel able to report issues, mistakes, and near misses without fear of reprisal.

Image source: Adobe Stock

They also rely on transparency, ensuring that incidents and findings are shared rather than hidden, and accountability at the system level, where the focus is on improving processes rather than blaming individuals. Without this, critical information is lost. Teams optimise locally rather than collectively, leaving leadership with a false sense of security.

THE PRACTICAL IMPLICATION For cybersecurity leaders, the implication is clear. Resilience is not built through more controls alone; it is built through better learning systems. In practice, that means treating every risk assessment, control assessment, audit and incident as an opportunity to improve, creating structured mechanisms to capture and act on feedback, and embedding continuous improvement into governance rather than treating it as an afterthought. In a world of constant threat, the organisations that continue to thrive are not those that avoid failure but those that learn faster than their adversaries can adapt. The point here is that compliance approaches don’t necessarily ‘test’ controls; real life and adversaries do.

CLOSING THOUGHT ‘Ctrl Alt Chaos’ showcases the cost of failure, ‘black box thinking’ highlights how organisations can learn from it, and The Phoenix Project emphasises how that learning can be turned into operational discipline. Together, they point to a simple but powerful conclusion: in cybersecurity, the strongest defence is not perfect prevention but the capacity to learn, adapt and continuously improve. This is also one of the many arguments in my forthcoming book, Cyber Security for Business Leaders, to be published by BCS in October 2026 (https://tinyurl.com/3f6erv9b). The book reframes cyber security as a business risk and a resilience challenge, not simply an IT problem, and focuses on the questions that matter most: what do we need to protect, what can we not afford to lose, how secure do we need to be, and how do we know when we are secure enough? In a threat landscape that never stands still, the organisations that endure will not be those that chase the illusion of perfect security, but those that build the discipline to learn faster, adapt sooner, and improve before the next failure becomes a crisis.

ITNOW | Issue 3 2026

TODAY

THE AI ADOPTION GAP: WHAT UK SMES NEED THAT NOBODY IS BUILDING FOR THEM Off-the-shelf AI is failing UK small businesses. AI Systems Engineer at Advance Training & Consulting, Itunu Ijila MBCS, explains why SMEs need practical deployments, not endless reports.

here is no shortage of AI tools available to UK small businesses in 2026. There is a shortage of AI tools that actually work in UK small businesses. I have spent the past year deploying generative AI systems across six UK SMEs. What I have found is that the problem is not access, awareness, budget or ambition, but a structural gap between how AI products are built and how small businesses actually operate. Until that gap closes, the graveyard of abandoned AI projects will keep growing.

doi:10.1093/itnow/bwag097

WHY OFF-THE-SHELF AI TOOLS CONSISTENTLY FAIL SMES Most AI tools available to small businesses today were built with two types of user in mind: large enterprises with dedicated technical teams, or individual consumers managing personal tasks. SMEs sit awkwardly between both and are well served by neither. Enterprise AI tools assume infrastructure and expertise that most SMEs do not have. Consumer AI tools, on the other hand, are built for simplicity but lack the reliability and customisation that a business context demands. What SMEs actually need is something

ITNOW | Issue 3 2026

in between: tools flexible enough to be configured to a specific business workflow, robust enough to run reliably without constant supervision and simple enough to be maintained by a non-specialist. The market has not built that yet at scale. In the meantime, the businesses that are succeeding with AI are the ones working with engineers to build bespoke systems fitted precisely to their operations.

THE HIDDEN COST OF AI READINESS ASSESSMENTS THAT GO NOWHERE Before any business I work with receives a deployed AI system, they go through an AI readiness assessment. This is standard practice. It is also, in my experience, where a significant amount of time and expectation gets quietly buried. Readiness assessments are valuable in principle. Understanding a business's data quality, workflow structure and technical infrastructure before recommending an AI solution prevents costly mismatches. The problem is that most assessments are designed to produce a report, not a deployment. I have seen businesses invest weeks of management time in detailed AI readiness processes only to receive a document telling them what they already suspected: their data is

inconsistent, their processes are not well documented and they need to do preparatory work before AI can help them. The report sits on a shelf. Nothing ships. The hidden cost is not just the time spent on the assessment. It is the erosion of confidence. Business owners who go through a readiness process and come out the other side with nothing to show for it become harder to re-engage. The next conversation about AI starts from a position of scepticism rather than enthusiasm. Readiness assessments need to be redesigned around a question that is rarely asked: not ‘is this business ready for AI?’ but ‘what is the smallest AI system we can deploy in the next four weeks that will demonstrate measurable value?’ Starting with deployment changes everything.

WHAT GENUINELY EFFECTIVE SME AI ADOPTION LOOKS LIKE The Intelligence Engine is an automated AI reporting pipeline I built for a UK media client. Before I built it, the client's team was spending significant manual hours each week aggregating data, writing reports, and distributing them to stakeholders. It was repetitive, time consuming and exactly the kind of work that AI is well suited to replace.

Image source: Adobe Stock

The system I built ingests business data automatically, generates structured intelligence reports using a language model, converts them to PDF, stores them in cloud infrastructure and logs metadata to a database. It runs autonomously. The client does not touch it. Their team recovered the hours they were spending on manual reporting and redirected them to higher-value work. What made this work was not the sophistication of the technology. It was the specificity of the problem. We did not ask ‘how can AI help this business?’ We asked ‘what is the most painful, repetitive task this team does every week?’ and then built something narrow and reliable that solved exactly that problem. That specificity is the common thread across every successful AI deployment I have seen in the SME context. Not broad transformation. One workflow, done well, measurably improved.

WHAT SMES CAN DO TO CLOSE THE GAP THEMSELVES Waiting for the market to build the right tools is not a strategy. Here is what the businesses I have worked with that have seen the best results have done differently. Define the problem before you touch the technology. Map your most painful,

repetitive, time consuming workflow in detail before you speak to any vendor or engineer. Know what it costs you in hours per week, what the output looks like and what good would look like. That specificity is what makes an AI brief actionable rather than abstract. Demand a deployment, not a report. When working with any AI consultancy or implementation partner, push for a working prototype within four to six weeks. Not a roadmap. Not a readiness score. Something that runs on your actual data and produces a real output. If a partner cannot deliver that in six weeks, the scope is too broad.

is, and how it will be accessed. This is unglamorous work. It is also the foundation everything else is built on. Treat your AI system like a member of staff. AI systems in production need monitoring, maintenance and occasional retraining. The businesses that get the most from their AI investments are the ones that build in a regular review process: checking outputs for quality, updating the system when business processes change and measuring performance against the original baseline on a quarterly basis.

THE GAP IS CLOSABLE

Start small and measure everything. Pick one workflow, deploy one system, and measure its impact against a clear baseline. How many hours did this task take before? How many does it take now? What is the error rate? Having those numbers makes the case for the next deployment significantly easier, both internally and with any future funders or partners.

The gap between UK SMEs’ AI ambitions and adoption is not primarily a technology problem. It is a design problem. AI tools need to be built differently for SME contexts. Readiness processes need to end in deployments, not documents. And SMEs themselves need practical, specific guidance on where to start and how to measure whether it is working.

Own your data from day one. The businesses that struggle most with AI implementation are the ones whose operational data is scattered across spreadsheets, email threads and legacy software with no clear owner. Before any AI project begins, assign someone the responsibility of understanding where your data lives, how clean it

None of that is out of reach. But it requires the people building and deploying AI systems to be honest about what is not working, and willing to share what is. That is why I am writing this.

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TODAY

AI ENABLED VALUES? Christina Lovelock MBCS takes a look at whether AI can help organisations turn their values from statements into everyday behaviours.

his may seem counter intuitive, as ‘values’ are an inherently human concept, but AI tools give us the opportunity to rapidly evaluate our written organisational assets against stated values.

doi:10.1093/itnow/bwag098

Many organisations define a set of values which seek to guide culture and act as a market differentiator, providing signals to potential employees, customers and suppliers. In reality these are often very separate to an organisation’s strategy, policies, governance structures, design principles, job descriptions and procedures. Can AI help us to better reflect our values in our ways of working? An effective way of ‘bringing the values to life’, and holding people to account for meeting them, is to discuss and agree the behaviours which demonstrate and uphold the values every day. ‘Customer Service’ and ‘Collaboration’ are two of the most common values selected, according to University of Oxford research (https://tinyurl.com/2rmvrjby). Taking each team or job role and asking ‘what does excellent customer service mean to us?’ or ‘how can we enable effective collaboration with colleagues, partners and suppliers?’ helps make somewhat abstract values into tangible, observable behaviours. If many organisations are selecting ‘customer service’ and ‘collaboration’, then are these values really acting as a differentiator, helping to attract

the colleagues and clients we want, or actually guiding the dayto-day interactions of our teams? Many organisations allow their AI tool of choice access to masses of information, including shared drives, emails, messages and websites. An AI ‘values audit’ can look across all this information and interactions and tell us which values are actually reflected in how we are speaking to each other, and what we are prioritising. If we are able to minimise AI’s people pleasing tendencies, we may not like what it has to report!

leading to interesting insights.

While these terms are often discussed together, in most organisations there is a disconnect between the two. Strategies are broken down into business plans and objectives, and perhaps even cascaded to team targets and individual objectives, but where is the direct traceability to values?

‘Review this job description and identify where our organisational values of [values] are not clearly expressed or reinforced. Suggest improvements that would make it more values aligned.’ It can also be useful to be explicit about the negative — stating examples of behaviours and actions that are in conflict with your values.

When budgets are tight, do our organisations still prioritise values, or do they become an optional luxury? AI can help us frame and phrase our strategic direction to reflect our values. Many business analysts and leaders are familiar with cost-benefit analysis. While this regularly includes both tangible and intangible elements, and may even consider strategic alignment, it does not often evaluate identified options for how well they align to the organisation’s values. If we share our options analysis and cost-benefit analysis with AI tools, we can prompt it to evaluate for values alignment,

About the author

These form a key part of managing people’s expectations and contributions, and while they may list the organisational values, most organisations are missing the opportunity to really describe what is needed, and what alignment to the values looks like at different levels of seniority. While the values should apply to everyone in the organisation, how those values are demonstrated will be very different, depending on the context of the role. Example prompt:

Of course, to our AI tools, these are not values, but just words. There is a risk that AI makes us sound more aligned to our values than we actually are (values-washing). AI cannot make us more aligned to our values if we don’t act in line with them, but it can provide useful suggestions for how changes in language in some of our key documents, and small tweaks to our policies and procedures can lead to nudges towards embedding the values every day. This is a ‘values by design’ approach, and it can start small. Try it with one job description, and discuss the results with colleagues.

Christina Lovelock (LinkedIn: https://tinyurl.com/2se8ecbw) is a digital leader, coach and author. She is active in the business analysis professional community and champions entry level roles. She is the author of the BCS books Careers in Tech, Data and Digital and Delivering Business Analysis: The BA Service Handbook, which are both available in the BCS bookshop (https://shop.bcs.org/).

ITNOW | Issue 3 2026

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ITNOW | Issue 3 2026

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