Articles Open Access
Automated Enterprise Cloud Risk Governance Through Intelligent Machine Learning-Based Security Policy Analysis
Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta, Indonesia
Abstract
The increasing adoption of public, private, hybrid, and multi-cloud infrastructures has introduced complex security risks that challenge traditional enterprise governance mechanisms. Organizations must continuously evaluate security policies, identify configuration weaknesses, monitor compliance requirements, and respond to evolving cyber threats across distributed cloud environments. Manual policy assessment and static rule-based governance often struggle to accommodate dynamic workloads, heterogeneous cloud configurations, and rapidly changing threat conditions. This research proposes an automated enterprise cloud risk governance framework that integrates intelligent machine learning, security policy analysis, risk prediction, and adaptive enforcement. The framework collects cloud configuration metadata, identity and access events, audit logs, vulnerability information, and compliance indicators to identify policy violations and estimate associated security risks. Machine learning algorithms classify misconfigurations, detect anomalous access patterns, prioritize vulnerabilities, and support risk-based policy recommendations. Explainable artificial intelligence provides interpretable evidence for governance decisions, while policy-as-code mechanisms translate approved security requirements into testable and enforceable controls. The methodology incorporates data preprocessing, feature engineering, model training, risk scoring, policy validation, and comparative performance evaluation. Evaluation criteria include classification accuracy, precision, recall, F1-score, false-positive rate, policy violation detection time, and governance automation overhead. The proposed framework aims to strengthen continuous compliance monitoring, improve risk prioritization, and reduce repetitive manual assessments while preserving human oversight for high-impact decisions. Its practical effectiveness must be established through reproducible experiments across representative enterprise cloud environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 5 |
| Pages | 17606-17615 |
| Published | 2025-10-14 |
| Issue | Vol. 9 No. 5 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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34. Mudunuri, P. R. (2024). Designing high-availability automation architectures for mission-critical research systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13864.
35. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
36. Gurram, A. (2023). A Framework for Achieving Zero-Downtime Data Migration Using Resilient Patterns for Production Database Schema Evolution. International Journal of Enhanced Research in Management & Computer Applications.
37. Garg, D. K. (2025). Manufacturing execution systems: A comprehensive guide to selection and implementation. International Journal of Research in Computer Applications and Information Technology, 8(1), 2001–2015. https://doi.org/10.34218/IJRCAIT_08_01_146
38. Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-7). IEEE.
39. Aesha, U. H., Bhuiyan, R. H., Jabeer, M. A., & Khan, S. M. (2024). AI-Driven Predictive Maintenance for Manufacturing and Aerospace Systems. Journal of Mechanical, Civil and Industrial Engineering, 5(1), 50-72.
2. Kumar, Y., Kaul, S., & Hu, Y.-C. (2022). Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey. Sustainable Computing: Informatics and Systems. https://doi.org/10.1016/j.suscom.2022.100780
3. Vemireddy, S. (2024). Intelligent expert matching architectures for high-scale digital tax assistance platforms. International Journal of Computer Technology and Electronics Communication, 7(4), 9281–9285.
4. Gurram, A. (2023). A Framework for Achieving Zero-Downtime Data Migration Using Resilient Patterns for Production Database Schema Evolution. International Journal of Enhanced Research in Management & Computer Applications.
5. Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-7). IEEE.
6. Kalakoti, M. K. R. (2022). Dependency-aware migration and resilience validation of stateful applications to enterprise cloud. International Research Journal of Innovative Engineering, 6(2), 9997–10005.
7. Chinthamani, R. (2024). Modernizing enterprise workload identity through SPIFFE-based certificate automation. International Journal of Research and Applied Innovations, 7(4), 11240–11245.
8. Bhuiyan, R. H., Aesha, U. H., Khan, S. M., & Jabeer, M. A. (2023). AI-driven digital twin technologies for advanced manufacturing systems. American Journal of Engineering, Mechanics and Architecture, 1(10).
9. Kommineni, R. (2022). Monitoring as code for self-hosted Elasticsearch: Independent, delta-based lifecycle management of alerting watchers and dashboards across multi-cloud environments. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7437–7446.
10. Ponugoti, M. (2025). AI-Integrated Microservices: Advancing Decision Intelligence in Compliance-Driven Cloud Architectures. Journal Of Engineering And Computer Sciences, 4(7), 1232-1240.
11. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
12. Jinna, K. R., & Subramani, S. K. (2024). Private LLM serving on enterprise container platforms: A reference architecture for GPU sharing, version governance and policy-enforced rollouts in regulated industries. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 10056-10066.
13. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
14. Gadige, C. D. (2023). Observability for distributed CRM architectures: Monitoring Salesforce, APIs, middleware, events, and AI agents. International Journal of Future Innovative Science and Technology, 6(5), 11388–11397.
15. Selvarajan, K. (2023). Optimizing large-scale data lakehouse architectures for performance and cost efficiency. International Journal of Research and Applied Innovations, 6(6), 10093–10101.
16. Addepalli, D. N. V. (2025). Evidence-Aware Large Language Model Framework for Explainable Root Cause Analysis in Dynamic Cloud-Native Systems under Partial Observability. International Journal of IT Management and Commerce, 2(3), 34-43.
17. Polamarasetty, V. K. (2023). Modern enterprise HR technology through Workday-based benefits and absence management. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6908–6913.
18. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
19. Mole, M. (2024). Modern business intelligence: Big data analytics and artificial intelligence for creating the data-driven value. International Research Journal of Innovative Engineering, 8(4), 15010–15022.
20. Bhagwat, V. B. (2024). A simplified transition from EBS Payroll to Cloud Payroll: Benefits and Drawbacks. Journal of Computational Analysis and Applications, 33(6), 463-474.
21. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.
22. Karrothu, A. (2025). Exploitability-aware vulnerability prioritization for automated security enforcement in cloud-native applications. International Journal of Science, Research and Technology, 8(2), 13948–13961.
23. Puli, S. K. (2022). An Event-Driven Canonical Data Architecture for Integrating Microsoft Dynamics 365 Customer and Dealer Data with On-Premises Enterprise Systems. European Journal of Advances in Engineering and Technology, 9(1), 105-114.
24. Gantikota, S. (2023). Integrating SonarQube and IBM AppScan into Enterprise CI/CD Pipelines: A Vulnerability Mitigation Framework Achieving Over Eighty Percent Risk Reduction. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 240-244.
25. Shaik, N. (2024). AI-driven scheduling for multi-cloud Kubernetes: Lessons from enterprise deployment. International Journal of Research and Applied Innovations, 7(3), 10880–10891.
26. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
27. Arumilli, S. V. T. (2024). Evaluating Reliability Risks and Failure Propagation in Autonomous Multi-Step Agentic AI Frameworks. International Journal of Communication Networks and Information Security, 16(2), 279-289.
28. Mysula, S. (2024). Event-driven integration architecture using publication methods for real-time DealCloud business operations & workflows. International Journal of Research and Applied Innovations, 7(4), 11233–11239.
29. Karuturi, Y. (2025). Serverless computing framework for enterprise ERP background job processing in cloud SAP environments. International Journal of Science, Research and Technology, 8(2), 13938–13947.
30. Padamati, J., Nunnaguppala, L., & Sayyaparaju, K. (2021). Evolving Beyond Patching: A Framework for Continuous Vulnerability Management. Journal for Educators, Teachers and Trainers, 12(2), 185.
31. Ramasamy, M. (2022). Closed-loop network automation: Architectural principles from Catalyst Center-scale systems. International Research Journal of Innovative Engineering, 6(3), 10698–10708.
32. Basavala, S. R. (2023). Automated threat modeling and vulnerability prioritization for secure software development. International Journal of Research and Applied Innovations, 6(1), 8414–8424.
33. Agrawal, A. M., Shinde, R. P., Chaudhary, A. K., Puram, K. K. R., Katakam, R., Poola, P. R., & Sadasivapani, K. (2025, July). Identifying Drug-Binding Domains in DNA Gyrase Using Advanced NLP Techniques. In 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-6). IEEE.
34. Mudunuri, P. R. (2024). Designing high-availability automation architectures for mission-critical research systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13864.
35. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
36. Gurram, A. (2023). A Framework for Achieving Zero-Downtime Data Migration Using Resilient Patterns for Production Database Schema Evolution. International Journal of Enhanced Research in Management & Computer Applications.
37. Garg, D. K. (2025). Manufacturing execution systems: A comprehensive guide to selection and implementation. International Journal of Research in Computer Applications and Information Technology, 8(1), 2001–2015. https://doi.org/10.34218/IJRCAIT_08_01_146
38. Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-7). IEEE.
39. Aesha, U. H., Bhuiyan, R. H., Jabeer, M. A., & Khan, S. M. (2024). AI-Driven Predictive Maintenance for Manufacturing and Aerospace Systems. Journal of Mechanical, Civil and Industrial Engineering, 5(1), 50-72.
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