International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
AI-Driven Cloud Forensics: A Novel Framework for Cybercrime Investigation and Digital Evidence Extraction
Kali Rama Krishna Vucha1 , Karthik Kamarapu2
1Independent Software Researcher, Computer Science, Acharya Nagarjuna University, Andhra Pradesh, India
2 Independent Software Researcher, Computer Science, Osmania University, Telangana, India
Abstract - In the era of cloud computing, cybercriminal activities have evolved, making traditional digital forensic methodologies inadequate for modern investigative challenges. This research introduces an AI-driven cloud forensic framework designed to enhance cybercrime investigation and digital evidence extraction in cloud environments. The proposed framework leverages machine learning (ML) algorithms, deep learning models, and intelligent automation to detect, analyze, and attribute cyber threats in real time. Unlike conventional forensic approaches,whichrelyheavilyon manualintervention,our AI-poweredsolutionoffersautomatedloganalysis,anomaly detection, and forensic evidence correlation across multicloud architectures. By integrating natural language processing (NLP) for log analysis, predictive analytics for cyber threat anticipation, and blockchain-based forensic data integrity validation, this research ensures a secure, scalable, and legally admissible forensic process. Additionally, the study explores the challenges of data sovereignty, multi-jurisdictional compliance, and digital evidence admissibility in AI-driven forensic investigations. The proposed model is tested against real-world cloud security incidents, demonstrating its effectiveness in reducing forensic investigationtime, improvingaccuracy in cyberattack attribution, and enhancing evidence traceability. This research aims to set a new benchmark in cloud forensics by introducing an intelligent, automated, andscalableapproachtocybercrimedetection.Thefindings providecriticalinsightsforlawenforcementagencies,cloud service providers, and cybersecurity professionals to fortify cloud security postures and streamline forensic investigations.
Key Words: AI-Driven Cloud Forensics, Automated Forensic Analysis, Cybercrime Investigation, Digital Evidence Extraction, Machine Learning
1.INTRODUCTION
The rapid adoption of cloud computing has significantly transformed the digital landscape, offering scalable and cost-effectivesolutions fordata storage,computation,and business operations. However, this shift has also introducednewchallengesincybersecurity,particularlyin cybercrime investigation and digital forensics. As organizations increasingly migrate sensitive data to the cloud, cybercriminals exploit vulnerabilities to conduct
unauthorized access, data breaches, and complex cyberattacks. Traditional forensic methodologies, designed for on-premise systems, are often ineffective in dynamic cloud environments where data is decentralized, volatile,andgovernedbymulti-jurisdictionalpolicies.
ArtificialIntelligence(AI)hasemergedasatransformative force in cybersecurity, offering automated, intelligent solutionsforreal-timethreatdetection,incidentresponse, and forensic analysis. AI-driven cloud forensics leverages machine learning algorithms, deep learning models, and natural language processing to analyze vast datasets, identify anomalies, and reconstruct cyber incidents with enhanced accuracy. Unlike conventional forensic approachesthatrelyonmanualinterventionandstaticlog analysis, AI-powered techniques provide adaptive and scalable solutions that improve investigation speed and accuracy.
This research aims to develop a novel AI-driven forensic framework tailored for cloud environments, addressing critical issues such as digital evidence integrity, legal admissibility, and automated forensic analysis. The proposed framework integrates predictive analytics, blockchain technology for data validation, and federated learning for secure forensic model training. By bridging the gap between AI and digital forensics, this study seeks to provide a scalable, efficient, and legally compliant solutionforcloud-basedcybercrimeinvestigations.
2. Literature Review
The integration of Artificial Intelligence (AI) in cloud forensics has emerged as a pivotal solution to modern cybersecurity challenges. Traditional forensic methodologies, primarily developed for static and onpremise infrastructures, struggle to adapt to the dynamic, multi-tenant, and decentralized nature of cloud environments. As cyber threats become more sophisticated, conventional forensic techniques prove insufficient in handling large-scale digital investigations. AI-drivenforensicframeworksofferadvancedautomation, real-time data analysis, and anomaly detection, thereby enhancing the accuracy and efficiency of cybercrime investigations. This section explores key developments in cloud forensics, AI-driven forensic models, and existing challenges in digital evidence extraction and cybercrime investigation.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
2.1 Cloud Forensics and Cybercrime Investigation
Cloud forensics is a specialized field of digital forensics that focuses on investigating security incidents within cloud computing environments. Unlike traditional digital forensics, cloud forensics faces unique challenges due to multi-tenancy, data volatility, and the reliance on thirdparty cloud service providers. Digital evidence in cloud environments is often distributed across multiple data centers, making acquisition and preservation difficult. Researchers have proposed cloud forensic models that incorporate forensic-as-a-service (FaaS) and remote forensic capabilities to enhance evidence collection and investigation. However, these models rely heavily on manualforensicinterventions,whichintroducedelaysand inconsistencies in cybercrime investigations. As cybercriminals exploit cloud infrastructure for illicit activities such as data breaches, ransomware attacks, and insider threats, AI-driven solutions are gaining traction in forensicinvestigations.
2.1.1 AI-Driven Forensics in Cloud Security
Artificial Intelligence has significantly enhanced the field of digital forensics by automating forensic data acquisition, log analysis, and threat detection. AI-driven forensictechniquesleveragemachinelearning(ML),deep learning, and natural language processing (NLP) to identify cyber threats and reconstruct attack patterns. These models enable real-time forensic analysis, allowing investigators to process large datasets and extract relevant evidence without extensive manual intervention. Unlike traditional forensic approaches that rely on predefined rule-based mechanisms, AI-powered forensic tools can dynamically adapt to evolving cyber threats. Despite these advantages, AI-driven forensics faces challenges related to model interpretability, bias, and the legal admissibility of AI-generated forensic evidence. To ensure reliability, forensic AI models must undergo rigorousvalidationandcomplywithstandardizedforensic methodologies.
2.2 Challenges in AI-Driven Cloud Forensics
The integration of AI in cloud forensic investigations introduces several challenges that must be addressed to ensure forensic reliability, data integrity, and legal admissibility. One of the primary concerns is data sovereignty, which refers to the legal and regulatory constraints surrounding digital evidence stored across multiple geographic locations. Cloud service providers operate in globally distributed infrastructures, making it difficult for forensic investigators to access data that falls under different jurisdictional regulations. Legal frameworks for AI-generated forensic evidence remain ambiguous,raisingconcernsabouttheadmissibilityofAIdrivenforensicfindingsincourtproceedings.Additionally, AImodelsin forensicinvestigationsare often regarded as
"black boxes," meaning their decision-making processes lacktransparencyandexplainability.
Another major challenge is forensic data integrity. Digital evidence must maintain its authenticity throughout the forensic process to ensure credibility in legal and cybersecurity contexts. AI-driven forensic tools rely on largedatasetsfortraining,whichincreasestheriskofdata poisoningattacksandadversarialmanipulations.Ensuring forensic model robustness against such attacks is critical for maintaining the reliability of AI-generated forensic results.Furthermore,thedependencyonmachinelearning modelsintroducesconcernsregardingfalse-positiverates, model drift, and the need for continuous retraining. Addressing these issues is essential to establish AI-driven cloud forensics as a credible and legally sound investigativeapproach.
2.3 AI-Based Solutions in Cloud Forensics
To overcome the limitations of traditional forensic methods, researchers have proposed AI-driven forensic models that integrate automation, predictive analytics, and intelligent decision-making. Machine learning algorithms are employed to analyze large volumes of forensicdata,detectanomalies,and establish correlations between cyber incidents. Deep learning techniques enhance forensic accuracy by identifying hidden attack patternsthat maybeoverlooked byconventional forensic tools.AI-poweredforensicmodelsutilizenaturallanguage processing(NLP)toanalyzesystemlogs,detectsuspicious behaviors, and generate forensic reports with minimal humanintervention.
Blockchain technology has also been integrated into AIdriven cloud forensic models to ensure data integrity and tamper-proof forensic logs. By implementing blockchainbased forensic validation mechanisms, investigators can verify the authenticity of digital evidence while maintaining a secure and immutable audit trail. Additionally, federated learning has been introduced to enableAI-drivenforensicmodelstotrainondecentralized forensic data while preserving user privacy. These advancements contribute to the development of scalable and legally compliant AI-powered forensic frameworks that enhance cybercrime investigation in cloud environments.
3. AI-Driven Cloud Forensic Framework
The increasing complexity of cybercrime in cloud environmentsnecessitatesashiftfromtraditionalforensic techniques to AI-driven solutions. Conventional forensic models struggle with the vast volume of cloud-generated data, the decentralized nature of cloud architectures, and the evolving sophistication of cyber threats. AI-driven cloud forensic frameworks offer an adaptive and intelligentapproachtoforensicinvestigations,automating
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
evidence collection, analysis, and cybercrime attribution. This section presents an AI-driven forensic framework designed to enhance investigation efficiency, improve evidence accuracy, and ensure legal compliance in cloudbasedforensicprocesses.
3.1 Framework Overview
The proposed AI-driven forensic framework consists of multiple interconnected components designed to enhance forensic investigation capabilities in cloud environments. The framework integrates machine learning algorithms, natural language processing, and blockchain-based validation mechanisms to ensure data integrity. It follows a structured approach that includes forensic data acquisition, preprocessing, anomaly detection, evidence correlation, and automated report generation. Each of these components functions cohesively to streamline the forensic process while maintaining compliance with digitalforensicsbestpractices.
Forensicdataacquisitionservesastheinitialphase,where system logs, network traffic, and access records are collected from cloud environments. AI-powered algorithms analyze these logs in real time to identify potential cyber threats and extract relevant forensic evidence. Machine learning-based forensic analysis then processes the collected data, detecting anomalies and classifying forensic artifacts. Blockchain technology is incorporated to provide tamper-proof forensic logs, ensuring the integrity and legal admissibility of digital evidence.Finally,anautomatedforensicreportgeneration module compiles the findings, creating structured reports that assist forensic analysts and legal professionals in cybercrimeinvestigations.
Theoverall structureofthe AI-drivenforensicframework is illustrated in Figure 1. This diagram represents the sequential flow of forensic data acquisition, AI-based forensic analysis, blockchain validation, and automated forensic reporting. By integrating these components, the framework ensures an efficient, scalable, and legally compliantforensicinvestigationprocess.
3.2 AI-Powered Forensic Data Acquisition
Forensic data acquisition in cloud environments presents a significant challenge due to the distributed nature of cloud storage and real-time data volatility. The proposed framework employs AI-powered techniques to automate evidence collection, ensuring that forensic data is retrieved in a forensically sound manner. AI-based log analysistoolsscansystemlogs,networktraffic,andvirtual machine snapshots to extract relevant forensic evidence. Additionally, machine learning models are used to differentiate between normal and suspicious activities, reducing the risk of irrelevant data inclusion in forensic investigations.
3.3 Machine Learning-Based Anomaly Detection
Anomaly detection plays a crucial role in forensic investigations, enabling investigators to identify cyber threats and suspicious behaviors. Traditional rule-based detection methods are often insufficient in handling evolvingattackpatterns.Theproposedframeworkutilizes supervised and unsupervised machine learning models to detect anomalies in cloud environments. Deep learning models analyze historical forensic data to predict and classify cyber incidents with high accuracy. Additionally, AI-driven behavioral analytics monitor user and system activities, flagging deviations from normal patterns that mayindicatemaliciousactivity.
3.4 Blockchain for Digital Evidence Integrity
Ensuring the integrity of digital evidence is critical for maintaining the credibility of forensic investigations. The proposed framework incorporates blockchain technology to establish an immutable and tamper-proof forensic log. Eachforensicrecordiscryptographicallysecuredwithina decentralized ledger, preventing unauthorized alterations or deletions. Blockchain-based timestamping mechanisms further enhance evidence authenticity, ensuring that
Fig. 1 AI-DrivenCloudForensicFramework
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
forensic findings remain legally admissible in court proceedings. By integrating blockchain, the framework provides a transparent and verifiable forensic data management system that strengthens trust in AI-driven forensicinvestigations.
3.5 Automated Forensic Report Generation
Traditional forensic investigations require extensive manual documentation, which is time-consuming and pronetohumanerror.Theproposedframeworkleverages AI-driven report generation systems to automate forensic documentation. Natural language processing (NLP) algorithms process forensic data, generate case summaries, and produce structured forensic reports in compliance with industry standards. This automation significantly reduces investigation time, allowing forensic analysts to focus on higher-order decision-making tasks. Additionally, AI-driven forensic reporting enhances collaboration between cybersecurity professionals, law enforcement agencies, and legal entities by providing standardizedandeasy-to-understandforensicfindings.
4. Legal and Ethical Considerations in AI-Driven Cloud Forensics
The integration of AI into cloud forensic investigations raises significant legal and ethical concerns. While AI enhances forensic capabilities by automating evidence collection and analysis, it also introduces challenges related to data privacy, regulatory compliance, and legal admissibility of AI-generated evidence. Cloud environments further complicate forensic investigations due to jurisdictional constraints and multi-tenant data governance.Thissectionexploresthekeylegalandethical issues associated with AI-driven cloud forensics, highlighting the need for standardized frameworks to ensure accountability and transparency in forensic investigations.
4.1 Data Privacy and Jurisdictional Challenges
Data privacy is a critical concern in cloud forensic investigations, as forensic tools often access and analyze sensitive user data. AI-driven forensic models must complywithglobaldataprotectionregulationssuchasthe General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations impose strict guidelines on data access, processing,andstorage,requiringforensicinvestigatorsto obtain proper authorization before analyzing digital evidence. Furthermore, jurisdictional conflicts arise when forensic investigations involve data stored in different geographic regions, each governed by distinct legal frameworks. Cloud service providers operate across multiple jurisdictions, complicating data retrieval processesandlegalenforcementactions.Addressingthese challengesrequiresthedevelopmentofAI-drivenforensic
frameworks that adhere to international data protection lawswhilemaintaininginvestigativeeffectiveness.
4.2 Legal Admissibility of AI-Generated Evidence
The legal admissibility of AI-generated forensic evidence remains a contentious issue in judicial proceedings. Traditional forensic methodologies emphasize the need for transparent and reproducible investigative processes to ensure the credibility of digital evidence. However, AIdriven forensic models operate as complex systems with opaquedecision-makingmechanisms,makingitdifficultto validate their findings in court. Machine learning models, particularly deep learning algorithms, often lack explainability,raisingconcernsaboutpotentialbiasesand errors in forensic investigations. To enhance legal acceptance, AI-based forensic frameworks must incorporate explainable AI (XAI) techniques that provide clear and interpretable forensic conclusions. Additionally, standardization of forensic AI methodologies and validation protocols is necessary to establish credibility andreliabilityinlegalcontexts.
4.3 Ethical Implications of AI in Forensic Investigations
AI-driven cloud forensics introduces ethical dilemmas related to algorithmic bias, data manipulation risks, and forensic accountability. Machine learning models are trainedonhistoricaldatasets,whichmaycontaininherent biases that influence forensic decision-making. Biased forensic models can lead to inaccurate attributions, wrongful accusations, and disparities in cybercrime investigations. Furthermore, the potential for AI-driven forensic tools to be manipulated through adversarial attacks raises concerns about the integrity of forensic findings. Ensuring ethical AI implementation in forensic investigations requires transparency in algorithmic design, continuous model validation, and adherence to ethical guidelines established by forensic and legal communities.
4.4 Standardization and Regulatory Compliance
The absence of standardized regulatory frameworks for AI-driven forensic investigations poses challenges in establishing uniform forensic methodologies. While traditional digital forensics follows established guidelines suchastheISO/IEC27037standardforevidencehandling, AI-driven forensics lacks comprehensive regulatory oversight. Governments and cybersecurity organizations must collaborate to develop standardized protocols that define AI forensic best practices, data handling procedures,andlegalvalidationmechanisms.Establishing industry-wide AI forensic standards will ensure consistency, reliability, and compliance with legal and ethicalprinciplesinforensicinvestigations.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
5. Implementation and Performance Evaluation
The effectiveness of AI-driven cloud forensic frameworks depends on their implementation and performance evaluation. To validate the proposed model, a structured approachisrequiredtoassessitsaccuracy,efficiency,and scalability in forensic investigations. This section outlines the implementation details of the AI-driven forensic framework, the performance metrics used for evaluation, and the experimental results comparing AI-driven forensicswithtraditionalforensicmethods.
5.1 Implementation of AI-Driven Forensic Framework
The implementation of the proposed AI-driven forensic framework involves multiple components, including machine learning models for anomaly detection, blockchain for forensic data integrity, and natural language processing (NLP) for forensic report generation. Theframeworkisdeployedinacloud-basedenvironment, integrating forensic data collection, evidence correlation, andautomatedreportingfunctionalities.
Forensic data acquisition is performed using AI-based log analysis tools that extract relevant forensic artifacts from cloud infrastructure, including network traffic logs, user accessrecords,andvirtualmachinesnapshots.Supervised andunsupervisedmachinelearningmodelsaretrainedon forensic datasets to identify cyber threats and generate investigative insights. Additionally, blockchain technology is integrated to establish a tamper-proof forensic log, ensuringdataauthenticityandtraceabilitythroughoutthe investigationprocess.
The implementation is carried out in a cloud-based testbed, simulating real-world cyberattack scenarios to evaluate the forensic framework’s effectiveness. Opensource forensic tools such as Autopsy and AI-driven intrusion detection systems are incorporated to benchmark the system’s performance against conventionalforensicmethods.
5.2 Performance Metrics and Evaluation Criteria
To assess the efficiency and accuracy of the AI-driven forensic framework, various performance metrics are used.Theseinclude:
Detection Accuracy: The ability of AI models to correctly identify cyber threats and classify forensicevidence.
False Positive and False Negative Rates: The proportion of incorrect detections in forensic analysis.
Response Time: The time taken by the forensic system to analyze logs and generate investigative reports.
Scalability: The framework’s ability to handle large volumes of forensic data across multi-cloud environments.
Forensic Data Integrity: The reliability of blockchain-based forensic logs in maintaining unaltereddigitalevidence.
5.3 Experimental Results and Comparative Analysis
The proposed AI-driven forensic framework is evaluated through a series of experiments comparing its performance with traditional forensic techniques. Forensic logs from simulated cloud security incidents are analyzed to assess the system’s accuracy in detecting cyberthreatsandreconstructingattacksequences.
Experimental results indicate that AI-powered forensic models outperform conventional methods in terms of detection speed and forensic accuracy, significantly reducing investigation time while maintaining high detection rates. Blockchain integration ensures tamperproof forensic logs, enhancing the credibility of digital evidence. Furthermore, automated forensic reporting reduces manual effort and improves standardization in forensicdocumentation.
A comparative analysis with rule-based forensic methods highlights the advantages of AI-driven forensic frameworks, demonstrating superior adaptability to evolvingcyberthreats.However,challengessuchasmodel interpretability and adversarial robustness remain areas forfurtherresearch.
Table 1: PerformanceMetricsandEvaluationResults
Measures the percentage of correctly identifiedcyber threats and forensic artifacts.
of benignactivities incorrectly classified as cyberthreats.
of actual cyber threats missed bytheforensic system.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
Response Time (seconds )
Scalabilit y (Logs Analyzed per Second)
Forensic Data Integrity Score(01)
Time taken by the forensic framework to analyzelogsand generate reports.
Ability to handle large volumes of forensic data across multicloud environments.
Ensures that forensic logs remain unaltered and legally admissible.
Fig. 2 PerformanceTrendsofAI-DrivenandTraditional ForensicMethods
6. Future Directions and Research Challenges
The evolution of AI-driven cloud forensics presents several promising avenues for future research and development.WhileAIhassignificantlyenhancedforensic investigations, numerous challenges remain, particularly in terms of AI model interpretability, adaptability to emergingcyberthreats,andlegalandethicalimplications. Advancements in AI methodologies and forensic frameworks are necessary to address these limitations andestablishAI-drivenforensicsasareliableinvestigative approach.
6.1 Advancements in AI for Cloud Forensics
The continued advancement of AI technologies holds immense potential for enhancing cloud forensic capabilities. Federated learning is emerging as a novel approach that allows AI models to train on decentralized forensic data without exposing sensitive information,
thereby improving data privacy in forensic investigations. Explainable AI (XAI) is another key area of research, focusing on making AI-driven forensic models more interpretable and transparent to forensic analysts and legal professionals. Additionally, reinforcement learning techniques could be leveraged to develop adaptive forensic models capable of dynamically adjusting to evolvingcyberthreatsincloudenvironments.
6.2 Enhancing AI Model Interpretability
One of the major challenges in AI-driven forensics is the lack of transparency in AI model decision-making processes. Deep learning models, in particular, operate as complexblack-boxsystems,makingitdifficultforforensic investigatorstovalidatetheirfindings.EnsuringAI model interpretability is essential for establishing trust and reliability in forensic evidence. Future research should focus on developing AI techniques that provide explainable forensic insights, allowing investigators to trace the reasoning behind forensic decisions. Incorporating visualization tools and AI explainability frameworks will help bridge the gap between AI automationandhumanforensicexpertise.
6.3 Cyber Threat Evolution and AI Adaptability
The continuous evolution of cyber threats presents an ongoing challenge for AI-driven cloud forensics. As cybercriminals adopt sophisticated attack techniques, forensicAImodelsmustadapttodetectandmitigatenew threatseffectively.AdversarialAIattacks,whereattackers manipulate forensic AI models to evade detection, pose a significant risk to forensic accuracy. Future research should explore the development of adversarially robust forensic AI models that can withstand evasion attempts and ensure reliable forensic outcomes. Additionally, integrating real-time threat intelligence with forensic AI systems will enhance adaptability and improve incident responsecapabilities.
6.4 Legal and Ethical Implications of AI in Forensics
The legal and ethical dimensions of AI-driven forensic investigations continue to be areas of concern. The admissibility of AI-generated forensic evidence in court proceedings remains a topic of debate, as forensic methodologies must meet legal standards for credibility and reproducibility. Future research should focus on defining standardized legal frameworks for AI-driven forensic investigations to ensure compliance with digital forensics best practices. Ethical considerations such as bias in forensic AI models, potential misuse of forensic AI tools, and data privacy concerns must also be addressed. EstablishingethicalAIguidelinesandregulatoryoversight will be crucial in ensuring the responsible and fair use of AIincloudforensicinvestigations.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 02 | Feb 2025 www.irjet.net p-ISSN: 2395-0072
7. Conclusion
The increasing complexity of cybercrime in cloud environments necessitates the development of AI-driven forensicframeworksthatenhancetheefficiency,accuracy, andreliabilityofdigitalinvestigations.Traditionalforensic methodologiesstruggletoaddressthechallengesposedby cloud computing, including data volatility, multi-tenancy, and jurisdictional complexities. AI-driven forensic frameworksleveragemachinelearning,deeplearning,and blockchain-based validation mechanisms to automate forensic processes, detect anomalies, and ensure the integrityofdigitalevidence.
ThisresearchhighlightsthepotentialofAI-drivenforensic models in revolutionizing cloud forensics by automating evidence acquisition, improving forensic analysis, and reducing investigation time. The implementation of AIpoweredforensicdataacquisitiontools,machinelearningbased anomaly detection, and automated forensic reporting significantly enhances forensic efficiency and accuracy. Additionally, the integration of blockchain technologyensurestamper-proofforensiclogs,addressing concernsregardingdataintegrityandlegaladmissibility.
Despite these advancements, challenges remain in the adoption of AI-driven cloud forensics. The lack of interpretabilityinAIforensicmodels,legalandregulatory ambiguities,andtheevolvingnatureofcyberthreatspose significantobstacles.Addressingthesechallengesrequires continued research in explainable AI, adversarial robustness, and standardization of AI forensic methodologies.FutureadvancementsinAI,particularlyin federated learning and reinforcement learning, have the potential to further enhance forensic investigation capabilities.
The findings of this research contribute to the growing field of AI-driven cloud forensics, providing insights into the development of scalable, legally compliant, and ethicallyresponsibleforensicframeworks.Bybridgingthe gap between AI automation and forensic analysis, this study aims to support cybersecurity professionals, law enforcement agencies, and policymakers in strengthening digitalforensicpracticesforcloud-basedenvironments.
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