Architecting Self-Healing Cloud Platforms with Intelligent Security Analytics and Enterprise Data Governance
Abstract
The rapid adoption of cloud computing has transformed enterprise operations by enabling scalable, flexible, and cost-efficient digital infrastructures. However, the increasing complexity of cloud environments has introduced significant challenges related to cybersecurity, system reliability, data management, and regulatory compliance. Modern organizations require resilient cloud platforms capable of automatically detecting, analyzing, and recovering from failures while maintaining robust security and governance standards. This study explores the integration of self-healing cloud platforms, intelligent security analytics, and enterprise data governance as a comprehensive framework for enhancing cloud resilience and operational effectiveness. Self-healing cloud architectures leverage automation, artificial intelligence, machine learning, and predictive monitoring to identify anomalies, prevent service disruptions, and execute corrective actions without human intervention. Intelligent security analytics strengthens cybersecurity by continuously analyzing large volumes of data to detect threats, assess risks, and support proactive incident response. Enterprise data governance ensures data quality, security, privacy, compliance, and accountability across distributed cloud ecosystems. The research examines the relationships among these technologies and their collective impact on organizational performance, business continuity, and digital transformation. The findings indicate that integrating self-healing mechanisms, advanced security intelligence, and governance frameworks creates adaptive, secure, and reliable cloud environments capable of supporting mission-critical operations. This integrated approach enables organizations to improve resilience, optimize resource utilization, strengthen regulatory compliance, and maintain trust in increasingly complex digital ecosystems
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17800-17809 |
Published |
November 11, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Christian Klein (2025). Architecting Self-Healing Cloud Platforms with Intelligent Security Analytics and Enterprise Data Governance. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17800-17809. https://doi.org/10.15662/IJAESIT.2025.0806012 |
References
2. Narayanan, S. (2024). Third-party AI vendor risk: Developing assessment frameworks for machine learning service providers. International Journal of Computer Science and Engineering and Information Technology, 10(4), 1133–1142. https://philarchive.org/archive/NARTAV
3. Appani, C. (2024). Explainable AI for fraud detection in financial transactions. Journal of Information Systems Engineering and Management, 9(3). https://jisem-journal.com/download/32_Explainable_AI_for_Fraud_Detection.pdf
4. Wen, B., Li, Y., & Bresler, Y. (2020). Image recovery via transform learning and low-rank modeling: The power of complementary regularizers. IEEE Transactions on Image Processing, 29, 5310-5323.
5. Parasa, M. (2025). Creating hyper-personalized learning journeys using AI in SAP SuccessFactors LMS for individual development and business alignment. International Research Journal of Engineering & Applied Sciences, 13(4), 241–255. https://doi.org/10.55083/irjeas.2025.v13i04022
6. Vayyasi, N. K. (2023). Designing a multi-domain predictive framework using Java and generative AI for financial, retail, and industrial use cases. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8060–8069.
7. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.
8. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
9. Boddupally, H. L. (2023). Intelligent semantic retrieval pipelines driving scalable, context-aware, and high-fidelity knowledge management capabilities across complex enterprise application landscapes. Context-Aware, and High-Fidelity Knowledge Management Capabilities Across Complex Enterprise Application Landscapes (August 30, 2023).
10. Namdeo, A. (2023). Neuromorphic edge analytics for industrial IoT. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8113–8123.
11. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
12. Kasireddy, J. R. (2025). The cloud cost-optimization flywheel: A systematic approach to reducing infrastructure waste without compromising delivery velocity. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(2), 16087.
13. Kavuri, S. (2025). Critical Review of Software Testing Problems in the Current Decade. IJSAT-International Journal on Science and Technology, 16(2).
14. Karnam, V. S. (2025). Intelligent SOS (Safety and Security operations): Real-Time Surveillance with Risk Forecasting and Assessment of SOS (Safety and Security operations) using Edge-AI and Cloud Infrastructure. Journal Of Multidisciplinary, 5(7), 552-562.
15. Ratkunas, V., Misiulis, E., Lapinskiene, I., Skarbalius, G., Navakas, R., Dziugys, A., ... & Petkus, V. (2024). Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study. European Journal of Radiology, 176, 111483.
16. Akila, R. (2024). A deep reinforcement learning approach for optimizing inventory management in the agri-food supply chain. J. Electrical Systems, 20(4s), 2238-2247.
17. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
18. Subramanyam, S. P. (2024). Advanced role-based access control models for Azure DevOps and CyberArk integration. International Journal of Advanced Engineering Science and Information Technology, 7(3), 14069–14076. https://doi.org/10.15662/IJAESIT.2024.0703004
19. Anbazhagan, K., Kumar, R., Thilagavathy, R., & Anuradha, D. (2024, March). Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud Computing. In 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS) (pp. 1-6). IEEE.
20. Vayyasi, N. K. (2023). Retail fraud analytics using generative intelligence and Java cloud frameworks. International Journal of Science, Research and Technology (IJSRAT), 6(4), 10324–10337.
21. Mathew, A. (2023). The Power of Cybersecurity Data Science in Protecting Digital Footprints. Cognizance Journal of Multidisciplinary Studies, 3(2), 1-4.
22. Adepu, R. (2025). AI-enabled autonomous infrastructure monitoring and self-healing cloud systems. International Journal of Future Innovative Science and Technology (IJFIST), 8(3), 234–251.
23. Narayanan, S. (2023). Operationalizing AI risk frameworks in financial services: A second line of defense perspective. World Journal of Advanced Research and Reviews, 20(1), 1436–1446. https://philarchive.org/archive/NAROAR
24. Panyala, V. R. (2024). Pioneering architectures for resilient multi-region cloud platforms supporting mission-critical internet services. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 1041–1058. https://doi.org/10.15662/410
25. Nerella, A., Badri, P., Kandula, S. T. R., Surasani, V. R., Muthukamatchi, P. K., & Jain, A. (2025, August). Neurosymbolic AI for IoT Security: A Knowledge-Guided Framework for Real-Time IoT Anomaly Detection and Response. In 2025 Seventeenth International Conference on Contemporary Computing (IC3) (pp. 1-5). IEEE.
26. Sugumar, R. (2024). Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
27. Shewale, V. (2024). Generative AI Threats and SEC Cyber Disclosure Readiness for Energy Sector CISOs. International Journal of Research and Applied Innovations, 7(5), 11504-11509.
28. Kunadi, S. K. (2023). Entity resolution at scale: Advanced fuzzy matching techniques for company and project data. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8014–8022.
29. Narayanan, L. K., Loganayagi, S., Hemavathi, R., Jayalakshmi, D., & Vimal, V. R. (2024, March). Machine learning-based predictive maintenance for industrial equipment optimization. In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (pp. 1-5). IEEE.
30. Adepu, G. (2024). Explainable AI Frameworks for Transparent Healthcare Reimbursement and Policy Compliance Systems. International Journal of Research and Applied Innovations, 7(5), 11490-11494.
31. Balamuralidhar Sarabu, V. (2025). Architecting scalable data integration frameworks for hybrid enterprise platforms with strong data governance. International Journal of Advanced Research in Computer Science & Technology, 8(3), 149–164.