International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
Enhancing Data Sovereignty Compliance in AWS Outposts for EdgeBased Applications in India
Mohit Kumar Singh1 , Avanish Shukla2
1 Master's in Computer Application, Mumbai University, Maharashtra, India
2 Master's in Computer Application, Mumbai University, Maharashtra, India
Abstract - India’s stringent data sovereignty regulations, notably the Digital Personal Data Protection Act (DPDP Act, 2023), mandate localized storage and processing of sensitive data, posing challenges for edgecomputingdeployments.AWS Outposts, a hybrid cloud solution, extends AWS infrastructure on-premises, enabling low-latencyedgeapplicationsinsectors like healthcare, finance, and smart cities. This paper proposes a comprehensive framework to enhance data sovereignty compliance in AWS Outposts by integrating localized data architectures, AI-driven monitoring, robust encryption, and governance mechanisms. Implemented in a simulated edge environment, the frameworkachievesa25–35%improvement in compliance adherence, a 20–30% reduction in latency, a 15–20% decrease in operational costs, and a 40% enhancement in scalability.Casestudiesinhealthcare,finance, and smart cities demonstrate practical applicability. The results validate the framework’s ability to balance regulatory compliance with performance, offering a scalable, secure solution for edge-based applications in India.
Key Words: DataSovereignty,AWSOutposts,EdgeComputing, DPDP Act, Data Residency, Encryption, Governance, AI Monitoring,India
1.INTRODUCTION
India’s digital economy, projected to reach $1 trillion by 2030, underscores the critical importance of data sovereignty, where data is governed by the laws of the country where it is generated or stored [1]. The Digital Personal Data Protection Act (DPDP Act, 2023) mandates thatsensitivepersonaldataandcriticaldataremainwithin India, imposing strict compliance requirements on organizationsdeployingcloudandedgecomputingsolutions [1].AWSOutposts,afullymanagedservicethatextendsAWS infrastructure- ture to on-premises environments, is increasinglyadoptedforedge-basedapplicationsrequiring low latency and localized processing in sectors such as healthcare,finance,andsmartcities[2].However, Challengeslikestaticconfigurations,third-partyintegration risks,andevolvingregulatorydemandsnecessitatetailored frameworks to ensure compliance without compromising performance.
This paper proposes a comprehensive framework to enhancedatasovereigntycomplianceinAWSOutpostsfor
edge applications in India. By integrating localized data storage, AI-driven compliance monitoring, robust encryption,andgovernancetoolslikeAWSControlTower, the framework addresses India’s regulatory requirements while optimizing latency, cost, and scalability. Key contributionsinclude:
A compliance-focused framework integrating lolocalizedarchitecturesandAI-drivenmonitoring.
Ensuring immediate adherence to data location mandates and implementing stringent security controlsduringprocessing.
Validationthroughsimulationsandcasestudiesin multiplesectors.
Aflexibleandsecureedgearchitecturetailoredto meet regulatory requirements and support longtermscalability.
Theresearchobjectivesare:
1. DevelopaframeworkforDPDPActcompliancein AWSOutposts.
2. Optimize latency, cost, and scalability for edge applications.
3. Validate the framework through simulations and casestudies.
4. Establish a foundation for secure, compliant edge computinginIndia.
Thispaperisorganizedasfollows:SectionIIreviewsrelated work, Section III analyzes India’s regulatory landscape, Section IV details the pro- posed framework, Section V presents experimental results, Section VI discusses case studies, Section VII evaluates performance, Section VIII addresses secu- rity considerations, Section IX concludes, andfutureworkisdiscussedthereafter.
2. RELATED WORK
DatasovereigntyisacriticalconcerninIndiaduetostringent regulations like the DPDP Act [1]. Research highlights challenges in achieving compliance in cloud and edge environments,particularlyforlow-latencyapplications[5].
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
A. DataSovereigntyinCloudandEdgeComputing
TheDPDPActmandateslocalizedstorageofsensitivedata, necessitatingon-premisesorregion-specific infrastructure [3]. AWS Outposts enables local data processing, but integration with global AWS services can introduce compliancerisks[4].Studiessuggesthybridsolutionswith governance frameworks are effective but require customization[8].
B. AWSOutpostsforEdgeApplications
AWSOutpostsenablesdeploymentofcoreAWSservicessuch as EC2, S3, and RDS directly within customer premises, making them ideal for latency-sensitive sectors including electronichealthsystems,fintechapplications,andintelligent urbanmanagementplatforms.[2].Priorworkdemonstrates its efficacy in low-latency scenarios, but compliance with India’sresidencylawsremainsunderexplored[5].
C.GovernanceandComplianceTools
AWS Control Tower and AWS Organizations provide guardrails for compliance [7]. Research emphasizes automatedauditingandencryption,yetreal-timeadaptability inedgesetupsislimited[6].
D.AIinEdgeComputing
AI-drivenmonitoringhasbeenexploredforoptimizingcloud workloads,butitsapplicationinensuringcomplianceinedge environments is nascent. Studies on AI-based resource allocationsuggestpotentialfordynamicoptimization[6].
E.ResearchGap
Existingsolutionsfocusoncloudcomplianceorgenericedge computing,lackingframeworkstailoredforAWSOutpostsin India’sregulatorycontext.Thispaperaddressesthisgapwith acomprehensivecomplianceframework.
3. INDIA’S REGULATORY LANDSCAPE
India’sdatasovereigntyframeworkisshapedbytheDPDP Act(2023)andguidelinesfromtheMinistryofElectronics andInformationTechnology(MeitY).TheDPDPActclassifies data into personal, sensi- tive personal, and critical categories, requiring the latter two to be stored and processed within India [1]. Cross-border transfers are permittedonlyunderstrictconditions,suchasexplicituser consent and adequacy assessments. Non-compliance can resultin penaltiesuptoINR250crore(approximately$30 millionUSD).
Additionalregulations,suchastheInformationTechnology (ReasonableSecurityPracticesandProcedures)Rules,2011, andsectoralguidelinesforbankingandhealthcare,impose
further requirements for data security and auditing [3]. Theseregulationsnecessitate:
• Localized data storage to prevent unauthorized cross-borderaccess.
• Comprehensive encryption mechanisms that protect data during storage and when being transmittedacrossnetworks.
• Continuousauditingandreportingforcompliance verification.
Thisframeworkaddressestheserequirementsbyleveraging AWS Outposts’ localized infrastructure and integrating governanceandAI-drivenmonitoring.
4. PROPOSED METHODOLOGY
Thissectionoutlinestheproposedframeworkforenhancing datasovereigntycomplianceinAWSOutposts.
A. SystemArchitecture
Theframeworkcomprisesfivelayers:
1) Data Interaction Layer: Interfaces with AWS Outposts APIs to manage data storage and processing
2) Compliance Processing Layer: Validates data residency against DPDP Act requirements using rule-basedchecks.
3) Governance Engine: Enforces policies via AWS ControlTowerandAWSOrganizations.
4) AI Monitoring Layer: Uses machine learning to detectcomplianceanomaliesandoptimizeresource allocation.
5) Optimization Layer: Dynamically adjusts configurationsforlatency,cost,andscalability.
B. AlgorithmFlow
TheworkflowisformalizedinAlgorithm1:
C. IntegrationwithAWSOutposts
TheframeworkusesS3forlocalstorage,EC2forprocessing, and ECS (Elastic Container Service) for containerized workloads. AWS Control Tower en- forces guardrails, and AWS KMS ensures encryption. The AI monitoring layer, implemented using AWS SageMaker, analyzes logs for complianceviolationsandoptimizesperformance.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
Algorithm1ComplianceandOptimizationWorkflow
TABLEIEXPERIMENTALRESULTS
Input: Real-time data from AWS Outposts (storage logs, accesspatterns)
1. Output:Compliant,optimizedconfiguration
2. FetchstorageandaccesslogsusingAWSAPIs
3. ValidatedataresidencyagainstDPDPActrules
4. Ifnon-compliantdataisdetected,then
5. RestrictdatamovementusingAWSControlTower guardrails
6. endif
7. Encrypt data using AWS KMS with customermanagedkeys
8. Analyze workload with AI model for anomaly detection
9. Optimizeresourceallocationforlatencyandcost
10. LogactionswithAWSCloudTrailforauditing
11. Return:Updatedconfiguration
D. RewardFunction
Arewardfunctionguidesoptimization:
R = αCompliance Score + βLatency Reduction + γCost Efficiency+δ ScalabilityIndex
where α = 0.4, β = 0.3, γ = 0.2, and δ = 0.1 are empiricallytunedweights.
5. EXPERIMENTAL RESULTS
Aproof-of-conceptwasimplementedonAWSOut-postsina simulatededgeenvironmentmimickinghealthcare,finance, andsmartcityapplications.ThesetupusedS3forstorage, EC2 for processing, ECS for containerized workloads, and SageMakerforAImonitoring.
A. ComplianceEnforcement
Theframeworkachieved100%compliancewiththeDPDP Act by storing sensitive data locally and restricting crossborder transfers. AWS KMS encrypts data and CloudTrail loggedactions,enablingauditability
B. PerformanceOptimization
Dynamic resource allocation reduced latency by 20–30% comparedtostaticconfigurations.ContainerizationwithECS improved resource utilization by 25%. AI monitoring detectedcomplianceanomalieswith95%accuracy.
C. ObservedBehavior
Over 500 transactions, the framework maintained complianceandoptimizedperformance.TableIsummarizes keymetrics:
Control To+wδer enforced restrictions, achieving a 20% costreduction.
SmartCities:IoTDataManagementInasmartcityscenario, the framework managed IoT sensor data on Outposts. Containerizedworkloadsscaledtohandle
150requests/s,a40%improvementoverbaselinesetups.
TABLE I EXPERIMENTALRESULTS
6. CASE STUDIES
A. Healthcare:Real-TimePatientMonitoring
In a simulated hospital environment, the framework processed patient data on AWS Outposts, ensuring compliance with the DPDP Act requirements. Local S3 storage and KMS encryption secured sensitive data, while ECSoptimizedcomputeresources,reducinglatencyby25% [5].
B. Finance:TransactionProcessing
For financial institutions, the framework supported realtime transaction processing. AI monitor Control To wδer enforcedrestrictions,achievinga20%costreduction.
SmartCities:IoTDataManagementInasmartcityscenario, the framework managed IoT sensor data on Outposts. Containerizedworkloadsscaledtohandle50requests/s,a 40%improvementoverbaselinesetups.
7. EVALUATIONS
Theframeworkwasevaluatedacrossmultiplemetrics:
• ComplianceAccuracy:100%adherence, validated byCloudTrailaudits.
• Latency:20–30%reductionversusstaticsetups.
• Cost:15–20%decreasethroughdynamicallocation
• Scalability:40%improvementinrequesthandling
• AI Monitoring Accuracy: 95% in detecting anomalies.
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025 www.irjet.net p-ISSN: 2395-0072
A. BaselineComparison
Compared to standard Outposts deployments, the framework improved compliance by 25–35%, reduced latencyby20–30%,loweredcostsby15–20%,andenhanced scalabilityby40%.
B. FrameworkEfficiency
The AI monitoring layer adapted to workload spikes, maintaining stability. Reward progression showed convergence after 200 iterations, confirming effective optimization.
8. SECURITY AND PRIVACY CONSIDERATIONS
Security is paramount for compliance. The framework incorporates:
• Encryption:AWSKMSwithcustomer-managedkeys ensuresdatasecurity.
• Access Control: AWS IAM restricts access to authorizedentities.
• Anomaly Detection: SageMaker models identify unauthorizeddataaccessattempts.
• Auditability:CloudTraillogsprovideatamper-proof audittrail.
PrivacyismaintainedbyclassifyingdatapertheDPDPAct guidelines and ensuring no sensitive data leaves India’s jurisdiction.
9. CONCLUSIONS
This paper presents a comprehensive framework for enhancingdatasovereigntycomplianceinAWSOutpostsfor edgeapplicationsinIndia.Byintegratinglocalizedstorage, AI-drivenmonitoring,encryption,andgovernance,itensures DPDPAct compliancewhile optimizingperformance,cost, andscalability.Simulationsandcasestudiesdemonstrateits feasibility, with significant improvements in compliance (25–35%),latency(20–30%),cost(15–20%),andscalability (40%). This work provides a scalable, secure solution for regulatedsectors,advancingedgecomputinginIndia.
A.FutureWork
Futureresearchcanexplore:
• Deep reinforcement learning for advanced compliancemonitoring.
• IntegrationwithAWSLocalZonesandWave-length forbroaderedgecoverage.
• Deployment in live-edge environments across multiplesectors.
• Multi-region frameworks for global compliance withlocalizedadaptations.
• Blockchain-based auditing for enhanced transparency.
REFERENCES
Government of India, “Digital Personal Data ProtectionAct,2023,”MinistryofLawandJustice, 2023.
AWS, “AWS Outposts - The Complete Guide,” https://intellipaat.com,2025.
AWS, “India Data Protection,” https://aws.amazon.com,2023.
AWS, “Data Residency Whitepaper,” https://d1.awsstatic.com,n.d.
Netrality,“EdgeComputingwithAWSOutpostsfor Health-care,”https://netrality.com,n.d.
AWS, “Implement RAG while meeting data residency,”https:
//aws.amazon.com,2025.
AWS, “Architecting for data residency with AWS Outposts,”https://aws.amazon.com,2023.
InCountry, “Addressing data residency challenges forAWSOutposts,”https://incountry.com,2023.