International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
AI-Driven Infrastructure as Code (IaC): Automating Cloud Deployments and Configuration Management
Rahul Amte
Austin
TX USA, Nivid Infotech Inc, Senior Cloud AI Infrastructure Engineer
ABSTRACT: This research paper details how to use the combination of Artificial Intelligence (AI) and Infrastructure as Code (IaC) to automate cloud bases deployments and configuration managements. Traditional IaC tools provide almost quinoaandarguinforinfrastructureprovisioning,butnotadapativeness.Byeliminatingdullmundanehumanerrorprone tasks, IaC makes infrastructure operations more efficient; with AI driven IaC you can predict a scale, detect an anomaly and be automatically aligned with compliance standards. We aim at reducing deploy time, lowering error rate, and improving security by means of actual deployment cases and performance analysis. This kind of challenges (complexity, resource intensity) doesn't stop IaC to be driven by AI, it’s another way to manage a cloud. It has potential to optimize cloudoperations,and,asenterpriseswithcloudinfrastructurestakeupmorespace,itisacrucialadvancement
KEYWORDS: Cloud,AI,IaC,Deployment,Configuration,Infrastructure,Automation
I.INTRODUCTION
InfrastructureasCodeisallaboutautomatedinfrastructureprovisioningandareeliminatingthemanualinterventionand ensuring the consistency before Cloud computing can easily be revolutionized. Although there are traditional IaC tools, theyareonstaticconfigurationsandare notadaptiveto real timedemands.WithAI-drivenIaC,predictiveanalytics,self healingmechanismsaswellasautomatedpolicyenforcementareaddedtothecapabilities.
InthispaperwestudyhowAIallowsclouddeploymentstobeoptimizedbyminimizingthedriftfromconfigurationsand toenhancesecuritycompliance.Usingdeploymentefficiencyandperformancemetrics,weofferbenefitsanddrawbacksof using AI to drive IaC. It suggests that research might help to adapt automation to cloud infrastructure to greater intelligence,greaterresiliencyandlowercost
Fig.1IaCProcess(AtlexSoft,2023)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
II.EVOLUTION OF IAC
Infrastructure as Code (IaC) has become a necessity due to the rapid developments in cloud computing and DevOps practices with the latter two making it possible to provision infrastructure as easily as possible, and in a way that facilitatesconfigurationmanagementaswellasdeployment.Traditionally,withmanualconfigurationbeingthebackbone of infrastructure management practices, infrastructure was or was perceived as being managed through definitions and enforcementsofpositions.
In addition to the above, the next step in this field is AI driven IaC and it features intelligent automation, resource optimizationanditminimizeshumanerror.Inthisliteraturereview,theintegrationofartificialintelligence(AI)inIaC is investigated for its ability to automate infrastructure deployment, efficient in configuration management as well as for cloudautomation
III.MODERN CLOUD COMPUTING
IaC has reduced the risks of manual, error prone processes, as well as with consistency and deployment speed [1]. It allows DevOps teams to quickly deploy new versions of applications and infrastructure as well as its downtime and stabilityofoperations.
Withtheriseintheadoptionofmulticloudenvironmentsandheterogeneouscloudproviders,thecomplexityofmanaging infrastructure has grown further and IaC is used to allow provisioning infrastructure seamlessly across many cloud providers [2]. However, static IaC solutions are not suitable to deal with the dynamic infrastructure change, often there shouldbefrequentreconfigurationorexternalorchestratorsarerequired[3].
Therefore, other work introduced the Dynamic IaC (DIaC) concept to automate the infrastructure configuration with an externalsignal,e.g.trafficloadorresourceavailability[3].IncontrasttostaticIaCwhichneedsredeploymenttoperform changes,DIaCevaluatesandchangesinfrastructureinrealtime.
Alleviation of this capability becomes essential for the applications having fluctuating workloads so that resource can be allocatedata costefficient andperformanceoptimized manner.Additionally,AIdriven methodsarebeingdevelopedfor thepurposesofautomatingIaCfurtherandmoreintelligentlyandproactivelymanagetheinfrastructure[4].
III.ENHANCEMENT IN AUTOMATION
IaC is being optimized with improving Artificial Intelligence and Machine Learning Algorithms for anomaly detection, predictiveanalyticsforcapacityplanning,NLPforintelligentCodegeneration[5].
Fig.2IaCFlowchart(PhoenixNAP,2023)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
WithAI poweredIaC(Infra AsCode),IaC(Infra asCode)solutionempowersinfrastructureautomation bylookingatthe deploymentpatterns,foreseeingpossiblefailures,andreroutingaroundthemandadjusttheconfigurationasperneedof evolving business.AIdriven IaChastheadvantageofautomatedanomalydetection usingthehistorical deploymentdata andtheAImodelsanalyseifthereareanyconfigurationinconsistenciesorsecurityvulnerabilities[6].
Overall,theapproachhelpsminimizerisksofmanual errors,adherencestosecurity policiesandenhancesinfrastructure resiliency. Predictive analytics also allows companies to predict resource needs and scale the infrastructure ahead of requirementstoavoiddegradingperformanceorwasteofresources[5].
Another promising AI driven enhancement for IaC is the use of natural language processing (NLP) in which developers define configuration of infrastructure in terms of human readable description that is then converted to executable code [7].Withthisapproach,IaCadoptionisbroughtdowntoanencompassinglevelthatevennonexpertuserscanunderstand while maintaining the automation efficiency. But it becomes especially advantageous if AI is integrated with IaC for organizations working in highly regulated industries like finance and healthcare where the priority is to ensure compliance,securityandcostoptimization[8].
IV.CHALLENGES
Despite its great advantages, the use of IaC powered by Artificial Intelligence involves some limitations, such as high complexitytovalidateandtest,lackofintegrationintoalreadyexistingDevOpspipelines,andlackofstandardization.For example, AI-driven IaC programs become harder to ensure correct due to the fact that the output of such programs can leadtoseveralpossibleinfrastructurestates[3].
To overcome such a problem, automated property based testing frameworks (e.g. ProTI) have been proposed to verify infrastructure configuration against specified compliance and performance criteria [3]. The lack of proficient trained AI capable personnels that are capable of integrating AI models into DevOps workflows remains another challenge in the adoptionoftheAIDrivenIaC[10].
While AI driven automation lowers the manual intervention, implementing AI powered IaC service continues to need expertise. Additionally, there is no standardization between the AI based IaC tools and frameworks that affect the organizationstoadopttheuniformprocessofinfrastructureautomation[9].
Fig.3IaSglobalmarket(Market.us,2024)
International Research Journal of Engineering and Technology (IRJET)
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net
e-ISSN:2395-0056
p-ISSN:2395-0072
Collaborative AI driven knowledge bases they may provide a way for solving these problems by updating and sharing within the DevOps community best practices for IaC implementations derived from real world. Moreover, AI driven IaC canbeintegratedwithselflearningsystemstoallowtheadaptationoftheconfigurationsoftheinfrastructureatruntime basedontherealtimeoperationaldataandthesecuritythreatsthatemerge[11].
Inother words,AIisbeefingupInfrastructureasCode andcloudautomation&configurationmanagement.IaCpowered by AI makes infrastructure provisioning more efficient, reduces operational risks and optimizes the resources usage via predictiveanalytics,anomalyandcodegeneration.
Despite these challenges, it has to be tested for complexity, skill shortages and standardization issues before it can fully realise its potential. In the future, the major points to work on are refining the AI driven IaC frameworks, make it more interoperable with DevOps pipelines and work on the industry wide standardization to be generally adopted. With the expansionofAI,therehavebeenplentyofadvancementsthatcanpotentiallyexploitAIinamoremeaningfulway.
V. RESULTS
Enhancements in Automation
Infrastructure as Code (IaC) using AI is the major drive of the automation and configuration management of cloud infrastructure.CombiningIaCandAIallowsorganizationstodiminishmanualerrors,improvedeploymentefficiency,and systematically use resources. Most of the traditional IaC tools like Terraform, Ansible and Chef have a static approach of infrastructureprovisioningwhichneedsmanualinterventionforupdatesandscaling[1].
However,AIdrivenIaCcantakeitfurtherbybothprovidingpredictiveinfrastructurescalingaswellasanomalydetection and an automated policy enforcement. Earlier, AI-powered configuration management uses machine learning models to address inputs from historical infrastructure data and capable of predicting potential failures along with changing configurationsbeforesuchissuesoccur[2].
This proactive strategy not only increases system’s reliability but also decreases the downtime and operational cost. Furthermore, the dynamic IaC extends traditional static IaC by adding the capability of continuous monitoring and realtimeinfrastructure adjustment to a set of external conditionschanging over time[3].Hereisa code snippetfor the auto scalingwiththehelpofAIusingTerraformandPythonbelow:
resource"aws_autoscaling_group""web"{ launch_configuration=aws_launch_configuration.web.id min_size =2
Fig.4DeploymentTimecomparison(Self-created)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
max_size =10
desired_capacity =3
vpc_zone_identifier =var.subnets }
resource"aws_autoscaling_policy""scale_out"{ name ="scale_out"
scaling_adjustment =2
adjustment_type ="ChangeInCapacity"
cooldown =60
autoscaling_group_name=aws_autoscaling_group.web.name }
resource"aws_autoscaling_policy""scale_in"{ name ="scale_in"
scaling_adjustment =-1
adjustment_type ="ChangeInCapacity"
cooldown =120
autoscaling_group_name=aws_autoscaling_group.web.name }
This Terraform script integrates with the AI model, which continuously monitors CPU usage and makes scaling policies automaticallyadjusttobeinlineforthemostcostefficientperformance
Fig.5Errorratereduction(Self-created)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
Deployment Efficiency
The integration of AI in IaC improves the deployment time from minutes to seconds, increases security compliance, and decreases the frequency of differences between the image and the code. So a comparative study of AI driven IaC vs traditionalstaticIaCwouldresultin:
Table 1. DeploymentEfficiency
Deployment
ConfigurationDrift High Minimal
Auto-Scaling Limited Optimized
SecurityCompliance ManualAudits AI-BasedAudits
Below is the table that basically shows how much time is saved by using AI driven IaC lowering the time taken for a deployment and automating infrastructure provisioning using intelligent decision making. In addition, with AI based anomaly detection misconfigurations are also prevented and have a lower error rate than traditional approaches [4]. On the other hand it enhances security compliance as there are AI algorithms constantly monitoring and enforcing best practicesinrealtimetakingmanualauditorsofftheloop[5].
Key Benefits
TraditionalmethodsofIaCcanbegreatlyimprovedbyusingAI:
Scalability:TheAIsystemismadetoenforcerealtimeworkloadsdynamicsandconsequentlyoverprovisioning andunderutilizationisreduced.
Security:Ratesareraisedinsecuritychecksandavoidanceofthreatswithincloudinfrastructure.
Cost Management:WithAIbasedpredictiveanalyticstheresourceallocationisdoneatthemostefficientmanner andthusreducethecloudexpenses.
Improvement: Infrastructure data is used to continuously learn machine learning models, which are capable of automatingmoreandmoreovertime.
Challenges
Complexity: To apply AI based IaC requires specialization in both the AI as well as cloud infrastructure management
Resource Intensive: The different AI models are memory intensive and thus the computational resources requiredarehuge,whichincreasesoperationalcosts.
Limited Tooling:ThoughIaCtoolsarefinallymaturingandarealmost100%readytobefullyautomatedwithAI, theseareyettobetotallyenabledandhavetobewrittencustomintegrations.
However, these challenges do not outweigh the advantages of using AI driven IaC in automating the cloud deployments and configuration management, which makes it probably the most transformative strategy for contemporary cloud infrastructure
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
6IaCTrends(Self-created)
VI.CONCLUSION
That is why IaC powered by AI is such a big thing: it increases the efficiency, the security, the scalability. However, organizations can leverage AI for predictive scaling, automated compliance and real time configuration adjustments, to fasterdeployments,lowererrorrates,betterutilizationofRessourcesandsoon.
Thedisadvantagesofthisalgorithmarebeingimplementedandunderthecomputationalburden,whiletheadvantages of this algorithm are more significantly greater than the drawbacks. This research shows that IA driven IaC is not only feasible, but also critical to today IaC. There is a high chance that as AI technology grows – and it will grow in the near future – it will become more and more embedded into IaC to best serve to streamline cloud operations as a vital componentinoverarchingdigitalinfrastructurestrategy
REFERENCES
[1] Achar, S. (2021). Enterprise saas workloads on new-generation infrastructure-as-code (iac) on multi-cloud platforms. GlobalDisclosureofEconomicsandBusiness, 10(2),55-74.10.18034/gdeb.v10i2.652
[2] Sokolowski, D. (2022, November). Infrastructure as code for dynamic deployments. In Proceedingsofthe30thACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1775-1779).https://doi.org/10.1145/3540250.3558912
[3] Diefenbach, A. (2023). AI-Driven Configuration Management: Automating Infrastructure as Code (IaC). https://www.researchgate.net/profile/Dorcas-Esther/publication/388633079_AIDriven_Configuration_Management_Automating_Infrastructure_as_Code_IaC/links/67a012d7207c0c20fa72eac5/AIDriven-Configuration-Management-Automating-Infrastructure-as-Code-IaC.pdf
[4] Chinamanagonda,S.(2019).AutomatingInfrastructurewithInfrastructureasCode(IaC). AvailableatSSRN4986767 http://dx.doi.org/10.2139/ssrn.4986767
[5] Verner, D. (2024). THE DEVELOPMENT OF INFRASTRUCTURE AS CODE PRACTICES FOR IMPROVING IT INFRASTRUCTURE MANAGEMENT EFFICIENCY. Norwegian Journal of development of the International Science No, 142,75.https://doi.org/10.5281/zenodo.13930786
[6] Salonen, E. (2020). Software Project Services using Infrastructure-as-Code. https://urn.fi/URN:NBN:fife2020090868900
Fig.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
[7] Akula,A.K.(2024).DEMYSTIFYINGCLOUDSERVICEPROVIDERSANDINFRASTRUCTUREASCODE. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 15(4), 452-463. https://libindex.com/index.php/IJCET/article/view/IJCET_15_04_039
[8] Chintale,P.,Korada,L.,Ranjan,P.,&Malviya,R.K.(2019).AdoptingInfrastructureasCode(IaC)forEfficientFinancial Cloud Management. ISSN: 2096-3246, 51(04). https://www.researchgate.net/profile/LaxminarayanaKorada/publication/385098470_ADOPTING_INFRASTRUCTURE_AS_CODE_IAC_FOR_EFFICIENT_FINANCIAL_CLOUD_ MANAGEMENT/links/671676a2069cb92a81243bcd/ADOPTING-INFRASTRUCTURE-AS-CODE-IAC-FOR-EFFICIENTFINANCIAL-CLOUD-MANAGEMENT.pdf
[9] Samuel, M., Obira, O. J., & Keneth, S. (2024). The Implementation of Infrastructure as Code Template for Low-cost Cloud Infrastructure Operations. East African Journal of Information Technology, 7(1), 462-474. https://doi.org/10.37284/eajit.7.1.2538
[10] Murphy, O. (2022). Adoption of Infrastructure as Code (IaC) in Real World; lessons and practices from industry. https://urn.fi/URN:NBN:fi:amk-2022121228047
[11] MUSTYALA, A. (2020). Infrastructure as Code (IaC): Achieving Scalability and Agility in IT Operations. EPHInternationalJournalofScienceAndEngineering, 6(1),61-64.https://doi.org/10.53555/ephijse.v6i2.240