Dynamic Security Optimization for Enterprise Cloud Platforms Using Predictive Risk Analytics and Automated Enforcement
DOI:
https://doi.org/10.15680/scqj1n42Keywords:
Dynamic security optimization, enterprise cloud security, predictive risk analytics, automated enforcement, machine learning, threat detection, adaptive access control, cloud governance, cybersecurity automation, risk-based security, anomaly detection, security policy managementAbstract
The increasing adoption of enterprise cloud platforms has introduced complex security challenges associated with dynamic workloads, distributed infrastructures, evolving cyber threats, and continuously changing access requirements. Traditional security mechanisms often depend on static policies and reactive threat detection, limiting their ability to respond effectively to emerging vulnerabilities and sophisticated attacks. This study examines dynamic security optimization using predictive risk analytics and automated enforcement to improve enterprise cloud protection. The proposed approach integrates cloud telemetry, identity and access management data, vulnerability assessments, behavioral indicators, and threat intelligence to estimate security risks continuously. Predictive analytics techniques, including machine learning, anomaly detection, and risk scoring, enable the identification of suspicious activities and potential attack pathways before significant damage occurs. Automated enforcement mechanisms translate analytical outcomes into adaptive security actions, including access restriction, workload isolation, policy modification, and incident escalation. The research adopts a quantitative, analytical methodology to evaluate threat detection accuracy, false-positive rates, response latency, policy compliance, and operational efficiency. It also considers the importance of explainability, privacy, scalability, and human oversight in security automation. The study proposes an integrated framework that balances proactive threat prevention with operational continuity and regulatory requirements. By combining predictive intelligence with policy-driven enforcement, enterprise cloud platforms can improve threat responsiveness, reduce security management overhead, and strengthen resilience against evolving cyber risks. The research contributes a structured approach to adaptive cloud security governance and continuous risk-based protection.
References
1. Mitropoulou, K., Kokkinos, P., Soumplis, P., et al. (2024). Anomaly detection in cloud computing using knowledge graph embedding and machine learning mechanisms. Journal of Grid Computing, 22, Article 6. https://doi.org/10.1007/s10723-023-09727-1
2. Narra, S. L., Vavilala, V., & Ambikapathy, A. (2026, May). Deep Learning Approaches for Identifying Cybersecurity Risks in IoT Systems. In 2026 2nd International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) (pp. 1–6). IEEE.
3. Kalakoti, M. K. R. (2023). Interference-aware resource scheduling for cloud microservices on Kubernetes. International Journal of Future Innovative Science and Technology, 6(1), 9900–9909.
4. Varanasi, B. S. S. (2025). AI-Enabled Continuous Authority-to-Operate (ATO) Frameworks for Information Systems. Enterprise Development and Microfinance, 35(2), 748–757.
5. Ramasamy, M. (2022). Closed-loop network automation: Architectural principles from Catalyst Center-scale systems. International Research Journal of Innovative Engineering, 6(3), 10698–10708.
6. Shaw, R. K. (2026, July). Kerberos vs. Public Key Authentication in z/OS Environments: Performance, Scalability, and Security Trade-offs. In 2026 9th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1305–1312). IEEE.
7. Manigandan, A., Bandari, U., Thilagaraj, S., Kondisetty, K., Anbalagan, A., & Soundarapandiyan, R. (2026, June). Deep Learning Based Multi-Object Detection for Smart Surveillance Applications. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1–7). IEEE.
8. Kopparthi, G. S. (2025). Cost Optimization in Azure and AWS Cloud. Journal of Marketing & Social Research, 2, 177–182.
9. Guram, S., & Pahune, S. (2026, March). Multi-Agent Deep Learning Framework for Supply Chain Resilience. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1-8). IEEE.
10. Reddy, K. V. (2024). Formal Verification with ABV: A Superior Alternative to UVM for Complex Computing Chips. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 90–98.
11. Shaik, N. (2026). AI-driven scheduling for multi-cloud Kubernetes: Lessons from enterprise deployment. International Journal of Advanced Engineering Science and Information Technology, 9(1), 88–98.
12. Kasarapu, B. C., Balerao, R., Kammari, P., & Chalicham, H. (2026, August). Design and Implementation of an AI-Driven Conversational Agent for Real-Time HR Support in Retail Environments. In 2026 10th International Conference on Inventive Systems and Control (ICISC) (pp. 1–8). IEEE.
13. Ponugoti, M. (2022). Integrating full-stack development with regulatory compliance in enterprise systems architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(2), 6550–6563.
14. Varanasi, B. S. S., Rai, S., Kesarpu, S., Nelavala, M. M., Matta, R., & Bayya, A. K. (2026). AI & ML for Secure Payment Systems and Fraud Detection. International Journal of Artificial Intelligence and Machine Learning, 6(11s), 266–272.
15. Mysula, S. (2025). Engineering resilient enterprise integration platforms through automated monitoring and intelligent error management. International Journal of Computer Technology and Electronics Communication, 8(2), 10508–10513.
16. Gantikota, S. (2024). Shift-Left Security for Decentralized Engineering Organizations: Embedding SAST, DAST, and Penetration Testing Throughout the Software Development Lifecycle in University and Research Computing Environments. International Journal of Emerging Research in Engineering and Technology, 5(4), 175–179.
17. Manoharan, D. (2026). Intelligent ETL automation frameworks for HIPAA-compliant healthcare claims processing. Journal of Computational Analysis and Applications, 35(1), 1060–1073.
18. Bansal, D. K., & Garg, D. K. (2026). Intelligent data engineering pipelines for enterprise applications: Architecture challenges and optimization strategies. In 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE. https://doi.org/10.1109/ICSSIT69151.2026.11656623
19. 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.
20. Polamarasetty, V. K. (2024). Workday-based enterprise benefits management: Configuration, automation, and operational optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11347–11352.
21. Vemireddy, S. (2026). Secure AI agent architectures for autonomous operations in regulated financial systems. International Journal of Advanced Research in Computer Science & Technology, 9(3), 1024–1028.
22. 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.
23. Padmanabham, S., Khokhar, J., & Bhandari, R. S. (2025, November). Cluster Analysis and Latent Class Modelling for Consumer Segmentation in Digital Ecosystem. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1–5). IEEE.
24. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970–8977.
25. Basavala, S. R. (2023). Enterprise application security governance through continuous compliance monitoring and risk assessment. International Journal of Engineering & Extended Technologies Research, 5(4), 6942–6952.
26. Meghani, Viren, AI-Powered Regulatory Change Impact Analysis for Legacy Banking Systems (April 24, 2026). Available at SSRN: https://ssrn.com/abstract=6846121 or http://dx.doi.org/10.2139/ssrn.6846121
27. 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.
28. Katakam, R. (2023). Integrating SAP finance and banking systems for efficient enterprise financial management. International Research Journal of Innovative Engineering, 7(2), 12136–12147.
29. Manigandan, A., Bandari, U., Thilagaraj, S., Kondisetty, K., Anbalagan, A., & Soundarapandiyan, R. (2026, June). Deep Learning Based Multi-Object Detection for Smart Surveillance Applications. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1–7). IEEE.
30. 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.
31. Madabathula, L. (2025). A governance-driven enterprise lakehouse architecture for real-time analytics and machine learning enablement. International Journal of Applied Mathematics, 38(12s), 5022–5033.
32. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231–244). Singapore: Springer Nature Singapore.
33. 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.
34. Mudunuri, P. R. (2025). Automation, compliance, and public health reliability in biomedical infrastructure. Journal of International Crisis and Risk Communication Research, 8(S12), 437–444.
35. Gadige, C. D. (2025). Building the adaptable enterprise: Trends in composable and event-driven Salesforce architectures. International Journal of Research and Applied Innovations, 8(6), 13119–13125.
36. Jiang, G. (2024). Artificial intelligence-based adaptive anomaly detection technology for IaaS cloud virtual machines. Journal of Engineering and Applied Science, 71, Article 102. https://doi.org/10.1186/s44147-024-00439-1
