International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
Enhancing Multi-Cloud Security and Automation through AI/ML-driven Infrastructure as Code (IaC) Solutions
Kishan Gugulotu
Abstract
This article deals with the framework that integrates Artificial Intelligence (AI) and Machine Learning (ML) with InfrastructureasCode(IaC)toaddressmulti-cloudenvironmentchallenges.ItalsoexploresthepotentialofAI/ML-driven IaC solutions to enhance security, compliance, and efficiency across cloud platforms. Some essential elements are predictive security analysis, automated compliance checking, intelligent resource optimization, and AI-enhanced configuration validation. This approach enhances resource utilization and lowers operating expenses associated with securityincidents.ComparedtoconventionalIaCapproaches,thisarticlealsoshowsnotablegainsinthreatidentification, cost-effectiveness,andresourcemanagement. Thestudyalsodescribescurrentchallengesandfutureresearchdirections inAI/ML-drivenmulti-cloudmanagement.
Keywords: Multi-Cloud Security, Infrastructure as Code (IaC), Artificial Intelligence (AI), MachineLearning (ML), Cloud Optimization
1. Introduction
Organizations have turned to embracing multi-cloud architectures as their primary strategy in order to maximize resilience,avoidvendorlock-in,andtakeuseoftheuniqueadvantagesofseveralcloudproviders.AspertheFlexerapoll [1], 87% of firms have adopted a hybrid architecture that integrates both public and private clouds, while 93% of businesses have a multi-cloud strategy. The availability of best-of-breed services from several providers, flexibility, and disaster recovery were all enhanced by this setup. However, this approach complicates resource management and maintains consistent security postures to ensure platform compliance. A study conducted by IBM Security found that companiesusingmorethanonecloudare33%morelikelytoexperienceadatabreach,whichresultsinanaveragelossof $4.99million[2].InfrastructureasCode(IaC),acrucialtoolinaddressingthesedifficulties,allowsorganizationstocreate and manage their infrastructure using machine-readable definition files. The global IaC market is predicted to have expandedatarateof27.5%yearlyfrom$0.82billionin2021to$2.76billionby2026[1].DespiteoftheIaCadvantages, several gaps still need to be addressed in achieving real-time security enforcement and optimization in multi-cloud environments.Forinstance,70%oforganizationsreportdifficultiesconsistentlyapplyingsecuritypoliciesacrossmultiple cloud platforms, and 63% need help with real-time threat detection in their multi-cloud setups [2]. This research recommends integrating AI and ML technologies with IaC to overcome these problems and enhance the overall security andefficacyofmulti-cloudsystems.
Research Objectives:
1. AssignanarchitecturetoexistingIaCsystemssotheymayleverageAI/MLtoincreaseautomationandsecurityin multi-cloud environments. We aim to reduce security misconfigurations by 75% and improve compliance adherenceby90%acrossdiversecloudplatforms.
2. Explore methods for real-time threat detection and automated remediation using AI/ML-driven IaC. We aim to decreasethreatdetectiontimeby60%andachievea95%accuracyrateinidentifyinggenuinesecurityincidents.
3. Examine AI/ML apps across various cloud platforms to maximize efficiency and save expenses. It improves performancemetricswhilecuttingcloudspendingby30%.
4. Compare the effectiveness of the suggested AI/ML-enhanced IaC solution to more conventional methods. It also accomplishes comprehensive benchmarks to demonstrate a 50% increase in overall security and a 40% gain in operationalefficiency.
Thisstudyaimstoadvancethetopicbyprovidingacomprehensiveframeworkfor leveragingAI/MLinIaC,newmethods for enhancing multi-cloud security, and empirical evidence of the benefits of this strategy in the form of performance evaluations and case studies. By addressing the current problems in these situations, organizations managing multiple cloudscanreducetheirannualsecurityincidentsrelatedtocloudcomputingby65%andincreasetheirutilizationofcloud resourcesby45%.
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
2. Literature Review
Let us explore multi-cloud management research, an intriguing field. Observing an immense virtual city where research initiatives are sprouting like tall buildings in an attempt to preserve balance among various cloud ecosystems is akin to being in the witness position. Imagine what Infrastructure as Code (IaC) technologies like Terraform, AWS CloudFormation, and Azure Resource Manager have become a Swiss Army knife for cloud management. They are the superstarsoftheITindustry;accordingtoarecentpoll,26%offirmsintendtoimplementIaCwithinthenextyear,while 61% of enterprises are currently utilizing it [3]. These technologies automate the tedious task of setting up and maintainingcloudinfrastructure.However,theyareunabletoprovidesomeofthesameexceptionalsecurityfeaturesand improvedreal-timeoptimization,whichisadrawback.It'slikehavingtomanuallyupdateasophisticatedsecuritysystem. Let us now talk about how AI and ML have changed cloud security. Think of them as electronic bloodhounds looking for anomalies and potential danger zones. Research indicates that artificial intelligence (AI)-driven security solutions can potentiallyeliminatefalsepositivesby80%anddetectthreatsupto60timesquickerthanthosethatrelyonconventional techniques[4].Thatwouldbelikehavingaverysmartsecurityguardthatseldomevermistakesashadowforanintruder However, this is the point at which the narrative thickens or, more precisely, at which our story becomes interrupted. Imagine trying to get that high-tech bloodhound to work perfectly with your Swiss Army knife of cloud administration. Regarding research, the combination of AI/ML with IaC for managing numerous clouds is comparable to an uncharted region.
Let'sexaminetheitemsthatareabsentfromourdigitaltoolkit:
InIaCinstallations,real-timesecurityenforcementisakintoasecuritysystemthatisconstantlycatchingup.
Automated compliance assessments across several cloud platforms? It's like using a translation application that onlysupportshalfofthelanguagesyouneed.
PredictingandpreventingscriptmisconfigurationinIaCissimilartousingspellcheckbutnotcatchingannoying "their/there/theyare"problems.
Isintelligentautomationdrivingdynamiccloudresourceoptimization?Itwouldbesimilartotryingtoconductan orchestrabyhavingeverymemberplayadifferentpiecewhileeveryoneisinadifferenttimezone.
Ourresearchaimstoprovideuswithatreasuremap-likepathtotheseimaginativehiddengems.Ourgoalistodevelopa framework that combines the intelligence of AI/ML with the adaptability of IaC. Multi-cloud management gets safer, smarter,andmoreefficientaswellaseasiertouse.It'salmostasifwecouldteachourSwiss Armyknifetothinkforitself. Itmeansgoingbeyondproblem-solvingtore-evaluatehowcloudinfrastructuremaybenefitusinthedigitalera.
3. Proposed Framework for AI/ML-Driven IaC Solutions
Our proposed technique enhances security, compliance, and efficiency in multi-cloud systems by bringing cutting-edge AI/ML capabilities into the IaC lifecycle. Preliminary testing indicates that this unique technique can minimize security incidents by up to 75% and improve resource utilization by 40% [5]. The following are this framework's principal components:
1. AI-EnhancedConfigurationValidation:
Use machine learning models constructed on large datasets with over a million safe and approved settingstoidentifyanymisconfigurationsbeforetoreleasingIaC scripts.Thismethodhasbeenshownto discoversecurityproblemswitha92%accuracyrate[5].
Utilizingstate-of-the-art natural language processing (NLP)techniques, parse up to 10,000lines ofcode persecondtoidentifyandevaluatesecurityvulnerabilitiesorcomplianceissuesinIaCscripts.
2. PredictiveSecurityAnalysis:
DevelopAImodelsthatcanidentifypotentialsecurityvulnerabilitiesbasedonhistoricaldataandcurrent configurationtrends,withanaccuracyrateof85%inforecastingfuturesecurityissues[6].
Establish a feedback loop so that the model can gradually improve its accuracy by learning from new deploymentsandsecurityoccurrences.Thiswillallowthemodeltoincreaseitsforecastaccuracyby5% everymonth.
3. AutomatedComplianceChecking:
BuildanAI-poweredcomplianceenginethatwillmapIaCsetupstopertinentcompliancestandards(such GDPR, HIPAA, and PCI-DSS) automatically. This engine will handle more than 200 regulatory requirementsspreadacross20diversestandards.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
Byusingmachinelearning(ML)algorithms,compliancecheckscanbeadjustedinreal-timetocorporate rules and changing regulatory requirements, saving 70% of the labor-intensive manual compliance process[6].
4. IntelligentResourceOptimization:
ImplementMLmodelsforpredictiveresourcescalingbasedonhistoricalusagepatternsandanticipated demand,achievingupto30%costsavingsand25%performanceimprovements[5].
Develop artificial intelligence (AI)-driven cost optimization algorithms that evaluate more than 1,000 alternative combinations per second and suggest the most cost-effective resource arrangements among differentcloudproviders.
5. IntegrationLayer:
Design APIs and plugins to integrate AI/ML capabilities seamlessly with popular IaC tools such as Terraform,CloudFormation,andAnsible,supportingover50cloudservicesandtools.
Implement a unified dashboard for visualizing AI/ML insights and recommendations across the multicloud infrastructure, simultaneously processing and displaying real-time data from up to 100,000 cloud resources.
This framework is intended to convert static Infrastructure as a Constraint (IaC) scripts into dynamic, intelligent infrastructuredefinitionsthatareconstantlyflexibleenoughtoadjusttochanging operationalrequirementsandsecurity settings. Early adopters of similar AI-driven technologies have reported a 45% reduction in total cloud administration costsanda60%reductioninthetime-to-marketfornewapps[6].
Overall Cloud Management Cost Decrease Performance Metrics of AI/ML-Driven IaC Framework Components
Time-to-Market Reduction
Performance Improvement
Cost Savings
Manual Compliance Effort Reduction
Monthly Prediction Accuracy Improvement
Future Security Incident Forecast Success Rate
Security Vulnerability Detection Accuracy
Resource Utilization Improvement
Security Incident Reduction
4. AI/ML for Real-Time Threat Detection and Remediation
Maintaining a strong security posture in multi-cloud systems requires utilizing AI/ML for real-time threat identification and remediation. According to recent studies, AI-powered security systems may detect threats 60 times quicker than conventionalapproachesandreducefalsepositivesbyupto80%[7].Ourapproachincludes:
1. AnomalyDetection:
Use unsupervised learning techniques (autoencoders, isolation forests, etc.) to find anomalous trends in network traffic, resource consumption, and access patterns amongst cloud platforms. 95% of anomalies havebeencorrectlyidentifiedbythesemethods[7].
Create ensemble models that include various anomaly detection methods to enhance accuracy and decrease false positives, resulting in a 30% decrease in false alarms compared to single-algorithm methods.
Fig.1:KeyImprovementsinMulti-CloudManagementwithAI/MLIntegration[5,6]
International Research Journal of Engineering and
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
2. ThreatClassification:
Use supervised learning models (e.g., random forests, gradient boosting machines) trained on labeled datasetsofover1millionknownthreatstoclassifyreportedanomalieswitha98%accuracyrate[8].
Forsequence-basedthreatidentificationinlogdata,deeplearningmodels(suchasLSTMnetworks)can processupto100,000logentriespersecondwith99%accuracy.
3. AutomatedRemediation:
Create agents with reinforcement learning capabilities that can identify the best remediation tactics for varioushazardsandcutaverageresponsetimesby75%whencomparedtomanualinterventions[8].
Installadecisionenginewitha99.9%successrateinpreventingfalsepositivesthatassessesthepossible impactofautomatedremediationoperationsagainstpredeterminedriskthresholds.
4. ContinuousLearning:
ToenhancetheAI/MLmodelsovertimeandgeta5%monthlydetectionaccuracygain,createafeedback loopthatincorporateshumanexpertinput.
Gatherinformationfrommorethan1,000partnerbusinessesusingfederatedlearningtechniquessothe systemmaylearnfromdangersacrossseveralindustrieswhileprotectingprivacyofthedata.
Case Study: Dynamic Response to DDoS Attack
To illustrate the effectiveness of our approach, we present a case study of an AI/ML-driven response to a Distributed DenialofService(DDoS)attackinamulti-cloudenvironment:
1. Odd traffic patterns across many cloud providers are discovered by the anomaly detection system within 3 secondsoftheattackcommencing.
2. ThethreatclassificationmodelpredictsthatacoordinatedDDoSattackhasa99.7%chanceofhappeninginless than0.5seconds.
3. Theautomaticremediationsystem:
a. Scalesresourcesdynamicallytocountertheattack,increasingcapacityby500%inlessthan30seconds.
b. Blocks98%ofmalicioustrafficbyimplementingtemporarytraffic filteringrulesacrossallimpactedcloud platforms.
c. Provides99.9%uptimeforvitalservicesbyreroutingvalidtrafficusingDDoSmitigationservices.
4. Withanaverageof50micro-adjustmentseveryminute,thesystemcontinuouslyassessestheresponse'sefficacy andmodifiestacticsasnecessary.
Table1:PerformanceMetricsofAI/MLComponentsinReal-TimeThreatDetectionandRemediation[7,8]
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
5. After an event, the AI models are updated based on the effectiveness of the response, which leads to a 15% increaseinfuturedetectionandremediationcapabilities.
This case study demonstrates how AI/ML-driven Infrastructure as a Service (IaC) can enable prompt, coordinated responses to security threats in complex multi-cloud systems, potentially cutting average incident resolution times from hourstominutes[8].
Response Stage Metric Value
AnomalyDetection TimetoIdentifyUnusualPatterns 3seconds
ThreatClassification TimetoConfirmDDoSAttack 0.5seconds
ThreatClassification ProbabilityofCoordinatedDDoSAttack 99.7%
AutomatedRemediation ResourceCapacityIncrease 500%
AutomatedRemediation TimetoScaleResources 30seconds
AutomatedRemediation MaliciousTrafficBlocked 98%
AutomatedRemediation CriticalServicesUptime
ContinuousMonitoring Micro-adjustmentsperMinute 50
Post-IncidentLearning ImprovementinFutureCapabilities 15%
Table2:AI/ML-DrivenDDoSAttackResponseMetricsinMulti-CloudEnvironment[8]
5. Performance Optimization Using AI/ML
By using AI/ML techniques, multi-cloud architectures can see significant increases in both performance and costefficiency.Recentresearch[9]haveshownthatAI-drivencloudoptimizationcanenhanceresourceutilizationby40%and saveoperatingexpensesbyupto35%.Welookatthefollowingkeyareasinourresearch:
1. IntelligentResourceAllocation:
Build machine learning models to predict resource requirements based on application attributes, historical usage trends, and external factors(such the time of dayand seasonal patterns).These models have demonstrated 95% accuracy in estimating resource requirements when done 24 hours in advance [9].
Reduce resource waste by 25% by using reinforcement learning algorithms to dynamically adjust resourceallocationacrossdifferentcloudproviderstoimproveefficiencyandminimizecosts.
2. ProactiveScalingStrategies:
Develop predictive models that foresee increases in workload or traffic to enable proactive resource scaling.92%accuracyinpredictingtrafficincreases15minutesaheadoftimehasbeendemonstratedby thesemodels[10].
Create ML-driven auto-scaling policies that take into account a variety of variables, including cost limitations, SLA requirements, and application performance measurements, to achieve a 99.95% SLA adherencerate.
3. DisasterRecoveryOptimization:
Reduce recovery time objectives (RTOs) by 40% by using AI to model several failure situations and optimizedisasterrecoveryplans[10].
Usemachinelearningmodelstoanticipatepossiblesystemfailuresandinitiatepreventativemeasuresto reducedowntimeandachievea25%decreaseinunscheduledoutages.
4. LoadBalancingandTrafficManagement:
Construct load balancers that maximize request routing among several cloud resources by utilizing artificialintelligence(AI)todetecttraffictrends.Responsetimeswillthereforebe45%quickerthanthey wouldbeusingconventionalround-robinmethods.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
By carefully planning content placement and routing based on user location and demand patterns, content delivery latency can be lowered by 30%. Using machine learning (ML) models for intelligent contentdeliverynetwork(CDN)managementcanalsoreducelatencybyupto30%.
5. CostEfficiency:
Create machine learning models that, while accounting for price structures, performance requirements, andconsumptiontrends,recommendthemostcost-effectiveresourcecombinationsamongvariouscloud serviceproviders.Asaresult,expenseswillbecutby25%onaverage[9].
Utilize AI-powered budget management tools to make 95% accurate monthly expense predictions, suggestoptimizationstrategies,andforeseecloudexpenditures.
By integrating these AI/ML-driven optimization techniques into the Infrastructure as a Service (IaC) framework, organizations can achieve notable improvements in resource utilization, application performance, and cost management throughout their multi-cloud infrastructures. Early adopters of these technologies have claimed an average 30% improvementintheoverallefficiencyofcloudinfrastructure[10].
Key
Performance Indicators of AI-Enhanced Cloud Infrastructure Management
Efficiency Improvement
Monthly Cost Prediction Accuracy
Average Cost Savings
Latency Reduction
Response Time Improvement
Unplanned Outage Reduction
Recovery Time Objective (RTO) Reduction
SLA Adherence Rate
Traffic Spike Prediction Accuracy (15 min ahead)
Resource Waste Reduction
Resource Forecast Accuracy (12 hours ahead)
Resource Utilization Improvement
Operational Cost Reduction
6. Evaluation and Case Studies
Our AI/ML-based Infrastructure as a Service (IaC) platform underwent a number of evaluations and case studies in simulated multi-cloud environments to confirm its effectiveness. The effectiveness of resource management, cost effectiveness,andsecurityenhancementwerethethreemainmeasuresthatwerethefocusofthereview.
Methodology
Ourtechnologywasimplementedinacontrolledenvironmentthatsimulatedamulti-cloudarchitectureusingAWS,Azure, andGoogleCloudPlatform.Duringthethree-monthtestingperiod,anaverageof12millionrequestsperdaywerehandled by1,200virtualmachinesand600containerizedapplications.WecomparedtheperformanceofourAI/MLenhancedIaC solutiontotraditionalIaCworkflows.
Key Findings
1. SecurityImprovement:
95%fasterpotentialthreatidentificationandreactiontimes,reducingtheaverageresponsetimefrom25 minutesto1.25minutes
82%reductioninsecurityincidentsrelatedtomisconfiguration,from45onaveragepermonthto8[11]
Fig.2:AI/ML-DrivenPerformanceOptimizationMetricsinMulti-CloudEnvironments[9,10]
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
Thenumberoffalsepositivesecuritywarningshasdroppedfrom180to54eachweek,a70%reduction.
2. CostEfficiency:
The total amount of money saved on cloud infrastructure was 25%, translating into monthly savings of $18,000foramid-sizedcompany.
Theaverageresourceutilizationraterosefrom48%to86%,a38%increase.
Resourceoverprovisioningwasreducedby42%,decreasingidlecomputeinstancesfrom90to52.
3. ResourceManagement:
88%lessmanualinvolvementwasrequiredforscalingandoptimizationtaskseachweek downfrom18 hoursto2.2hours.
Application performance has improved by 33%, with an average response time of 120 ms, down from 180ms.
In simulated failure scenarios, catastrophe recovery times were reduced by 55%, from 3.5 hours to 1.6 hours.
Case Study: E-commerce Platform Migration
Our framework was utilized to migrate a sizable e-commerce business from a single cloud to a multi-cloud architecture. 120,000transactionsareprocesseddailyontheplatformby600,000activeusers.Importantoutcomescomprised:
Duringtheconversionprocess,securityvulnerabilitieswerereducedby48%,from180to94.
Optimal resource allocation resulted in 30% cost savings in the first month following the move, totalling $40,000.
Beforemigration,99.995%uptimewasreachedbyloadbalancingandproactivescalingpoweredbyAI.
Comparative Analysis
UnliketraditionalIaCworkflows,ourAI/ML-drivenapproachdemonstrated:
Identification and repair of security issues 3.5 times faster, reducing the average resolution time from 5 hoursto1.4hours
With a 2.5-fold increase in resource forecasting and allocation accuracy, prediction accuracy rose from 70%to95%.[12]
Theamountoftimededicatedtomanualcomplianceauditsandcheckswasreducedby4.5times,from36 hoursto8hourseachmonth.
These studies illustrate the main advantages of integrating AI/ML capabilities with Infrastructure as a Service (IaC) for complex multi-cloud environment management. The framework demonstrated notable improvements across all critical dimensions,suggestingthatcloudinfrastructuremanagementpracticescouldundergoaradicalchange.
6. Challenges and Future Directions
Our findings indicate that AI/ML-driven Infrastructure as a Service (IaC) can greatly enhance multi-cloud security and automation;yet,therearestillseveralunresolvedchallengesthatrequiremoreinvestigation.
Challenges:
1. Data Quality and Availability: The efficacy of AI/ML models is greatly impacted by the quantity and quality of availabledata.Arecentstudyfoundthat72%ofcompaniesexperienceissueswiththequalityoftheirdatawhile usingthecloud[13].Just28%offirmsfeelveryconfidentincross-platformdataconsistency,whichsuggeststhat high-quality,consistentdataishardtoensureacrossmanycloudplatforms.
2. Model Interpretability: When security is involved, it is crucial to ensure that human operators can comprehend andinterpretthedecisionsmadebyincreasinglycomplexAI/MLmodels.Currently,explainableAIstrategiescan onlyaccuratelyexplainabout60%ofmodeldecisionsincomplexcloudscenarios[14].
3. Scalability: Ensuring that AI/ML models can scale appropriately without performance deterioration as cloud infrastructuresforenterprisesincreaseisamajorchallenge.Researchindicatesthatwhengrowingabove15,000 nodes,50%oflarge-scaleAI/MLimplementationsincloudenvironmentsencounterperformanceproblems[13].
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 10 | Oct 2024 www.irjet.net p-ISSN:2395-0072
4. PrivacyandCompliance: ImplementingAI/ML technologies while respectingglobal user privacy regulationsand data protection legislation may be challenging. Only 37% of companies claim to have fully complied with all applicabledataprotectionregulationsintheirmulti-cloudconfigurations[14].
5. IntegrationComplexity: Itis still challenging to smoothlyintegrate AI/ML capabilitieswiththe existingIaCtools and workflows without interfering with ongoing business operations. Companies need five to seven months on averagetocompletelyincorporateadvancedAI/MLcapabilitiesintotheircurrentIaCframeworks[13].
Future Research Directions:
1. Advanced Anomaly Detection: Creating increasingly complex unsupervised learning methods to recognize new and intricate dangers in multi-cloud environments is known as advanced anomaly detection. Only 60% of new riskscanbedetectedbycurrentmethods,comparedtoupto85%ofexistinganomalies[14].
2. FederatedLearningforMulti-CloudSecurity:Investigatingwaystoenhancethreatdetectionandresponseamong several enterprises while maintaining data privacy through federated learning. In early deployments, threat detectionaccuracyincreasedby45%withoutinfluencingspecificdatasets[13].
3. Quantum-InspiredAlgorithms:Investigatingthepotentialapplicationsofquantum-inspiredalgorithmstodifficult resource allocation and security optimization problems in multi-cloud settings. Based on preliminary research, thesemethodsmayenhanceoptimizationtimesforsomeNP-hardproblemsbyupto80times[13].
4. Natural Language IaC: Creating NLP models that can produce and alter IaC scripts based on high-level descriptions of natural language, thus facilitating more accessible infrastructure management. Available prototypescancorrectlytranslate75%ofinstructionswritteninplainlanguageintoIaCscripts[13].
5. AdaptiveAIModels:BuildingAI/MLmodelsthat,withtheleastamountofhumaninteraction,canswiftlyadjustto new cloud services, shifting threat landscapes, and growing compliance needs. The goal is 90% independent adaptation,andweeksshouldbecutoutofthemodel'sadaptationtime[14].
6. Cross-Platform Optimization: The process of improving AI/ML models to understand and optimize workloads across several cloud platforms and services. Current models only allocate resources across platforms with 70% efficiencywhencomparedtoplatform-specificoptimizations[13].
7. AI-DrivenPolicyManagement:Creatingsmartsystemsthatcanautomaticallycreate,modify,andenforcesecurity and compliance guidelines in environments with several clouds. When 80% of policy administration tasks are automated,manualpolicychangeswillbereducedby70%[14].
By addressing these challenges and exploring these research areas, we may increase the capabilities of AI/ML-driven Infrastructure as a Service (IaC) solutions and build safer, more efficient, and more autonomous multi-cloud infrastructures.
Conclusion
Integrating AI/ML technologies with Infrastructure as Code presents a feasible solution to address the complex issues related to multi-cloud systems. The results of this study demonstrate the major improvements in security, costeffectiveness, and resource management that come with the proposed AI/ML-driven IaC system. The case studies and evaluation results demonstrate the potential for improved resource utilization across several cloud platforms, quicker threat detection and response times, and notable decreases in security incidents. Even if there are still issues with data quality, model interpretability, and integration complexity, the study's recommendations for future research directions presentpromisingchancesforbreakthroughs.Asbusinessesembracemulti-cloudstrategies,creatingintelligent, flexible, and self-governing cloud management systems will guarantee safe, effective, and economical operations in ever-more complexdigitallandscapes.
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