Secure Enterprise Ecosystems for AI-Enabled Financial Healthcare Intelligence Platforms and Autonomous DevSecOps Automation
DOI:
https://doi.org/10.15662/IJRAI.2025.0806037Keywords:
AI enabled secure cloud ecosystems, enterprise platform security, financial technology security, healthcare intelligence systems, autonomous DevSecOps automation, cloud ecosystem architecture, machine learning cybersecurity, zero trust cloud environments, intelligent cloud security analytics, automated security orchestration, predictive cyber threat detection, secure digital transformationAbstract
The transformation of enterprise digital infrastructures through cloud computing has enabled organizations to deliver services more efficiently, scale operations, and integrate advanced analytics. However, this shift also introduces critical challenges in security, compliance, and operational efficiency, especially in industries handling sensitive data, such as finance and healthcare. Traditional security and operations models are insufficient for dynamic cloud environments, where continuous monitoring, rapid threat mitigation, and automated governance are required.
This research proposes an AI-enabled secure cloud ecosystem framework for enterprise platforms, financial technologies, and healthcare intelligence, integrating autonomous DevSecOps automation. The proposed architecture leverages artificial intelligence for real-time threat detection, predictive analytics, and intelligent automation of security and operational workflows. Cloud-native microservices, container orchestration, and continuous integration/continuous deployment (CI/CD) pipelines are combined with AI-driven monitoring to enhance enterprise agility and resilience.
The framework also incorporates autonomous DevSecOps principles, enabling automated security policy enforcement, vulnerability remediation, and operational scaling without human intervention. By integrating secure cloud-native infrastructure, AI analytics, and autonomous automation, the proposed ecosystem supports financial transaction integrity, healthcare data privacy, and enterprise operational efficiency. The study provides architectural design principles, integration strategies, and evaluation methodologies for implementing AI-driven secure cloud ecosystems that enable scalable, intelligent, and resilient enterprise platforms.
References
1. Thota, S. (2025). A Secure Multi-Tenant AI Framework for Enterprise CRM Automation on Salesforce Cloud Platforms. International Journal of Emerging Trends in Computer Science and Information Technology, 6(2), 106-114.
2. Kamadi, S. (2025). Machine learning and AI architecture: A comprehensive framework for production-grade intelligent systems. World Journal of Advanced Research and Reviews, 27(1), 2789–2799. https://doi.org/10.30574/wjarr.2025.27.1.2654
3. Gowda, M. K. S. (2024). Leveraging Machine Learning to Enhance Accuracy and Efficiency in Regulatory Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683-10692.
4. Potel, R. (2025). Fleet, Driver & Supply Chain Optimization Achieving First-and Last-Mile Excellence through SYNAPSE Orchestration. International Journal of AI, BigData, Computational and Management Studies, 6(4), 46-74.
5. Karvannan, R. (2025). Advancing Hospital Pharmacy Automation: Impacts, Challenges, and Future Innovations in AI-Driven Medication Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12207-12216.
6. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
7. Anumula, S. R. (2025). Real-Time Scheduling Optimization Using Machine Learning in Pilot Trading and Tracking Systems. Journal Of Multidisciplinary, 5(7), 128-133.
8. Panda, S. S. (2024). Managing BSL Implementation: A TPM’s Guide to Robust Data Centers. International Journal of Technology, Management and Humanities, 10(01), 33-38.
9. Subramanian, T., Chinnadurai, N., & Singaram, U. (2025). Performance Investigation on OCF and SCF Study in BLDC Machine Using FTANN Controller. Journal of Electrical Engineering & Technology, 20(4), 2675-2688.
10. Dave, B. L. (2025). LEVERAGING AI-DRIVEN PLATFORMS FOR ADVANCED IMPACT ANALYSIS AND QA IN SALESFORCE IMPLEMENTATIONS. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 11798-11803.
11. Thumala, S. R., Madathala, H., & Mane, V. M. (2025, February). Azure Versus AWS: A Deep Dive into Cloud Innovation and Strategy. In 2025 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 1047-1054). IEEE.
12. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
13. Gowtham, M. S., Ramkumar, M., Jamaesha, S. S., & Vigenesh, M. (2024). Artificial self-attention rabbits battle royale multiscale network based robust and secure data transmission in mobile Ad Hoc networks. Computers & Security, 142, 103889.
14. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
15. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
16. Dama, H. B. (2024). Cross-Cloud Data Consistency Models for Always-On Banking Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8468-8476.
17. Thota, S. (2025). A Secure Multi-Tenant AI Framework for Enterprise CRM Automation on Salesforce Cloud Platforms. International Journal of Emerging Trends in Computer Science and Information Technology, 6(2), 106-114.
18. P. Jothilingam, “Advancing cybersecurity in industrial control systems: Frameworks, threat modeling, and resilience strategies,” International Journal of Supportive Research (IJSR), vol. 2, no. 2, pp. 69–75, Jul. 2024.
19. Uttama Reddy Sanepalli, "Adaptive Intelligence Framework for Retirement Portfolio Management: Self-Optimizing Infrastructure for Dynamic Asset Allocation and Risk Mitigation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 8, Issue 6, pp. 769-780, November–December 2022. https://doi.org/10.32628/CSEIT22557
20. Gopinathan, V. R. (2024). Secure Explainable AI on Databricks–SAP Cloud for Risk-Sensitive Healthcare Analytics and Swarm-Based QoS Control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
21. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
22. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E. (2025, September). Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency. In 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 1348-1353). IEEE.
23. Ravi Kumar Ireddy, "AI Driven Predictive Vulnerability Intelligence for Cloud-Native Ecosystems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 9, Issue 2, pp. 894-903, March–April 2023. https://doi.org/10.32628/CSEIT2342438
24. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
25. Anitha, K., Vijayakumar, R., Jeslin, J. G., Elangovan, K., Jagadeeswaran, M., & Srinivasan, C. (2024, March). Marine Propulsion Health Monitoring: Integrating Neural Networks and IoT Sensor Fusion in Predictive Maintenance. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 1-6). IEEE.
26. Gowda, M. K. S. (2024). Leveraging Machine Learning to Enhance Accuracy and Efficiency in Regulatory Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683-10692.
27. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
28. Anumula, S. R. (2025). Real-Time Scheduling Optimization Using Machine Learning in Pilot Trading and Tracking Systems. Journal Of Multidisciplinary, 5(7), 128-133.
29. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
30. Subramanian, T., Chinnadurai, N., & Singaram, U. (2025). Performance Investigation on OCF and SCF Study in BLDC Machine Using FTANN Controller. Journal of Electrical Engineering & Technology, 20(4), 2675-2688.
31. Gurram, S. (2025). Data product valuation: Pricing, risk, and ROI of enterprise datasets. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (ISCSITR-IJCSE)-ISSN: 3067-7394, 6(5), 1-17.
32. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
33. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
34. Dama, H. B. (2024). Cross-Cloud Data Consistency Models for Always-On Banking Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8468-8476.
35. Rahman, M. H., Dipa, S. A., Hasan, K., & Hasan, M. M. (2025). Health at Risk: Respiratory, cardiovascular, and neurological impacts of air pollution. Innovations in Environmental Economics, 1(1), 56-69.
