AI-Driven Secure Cloud Transformation Integrating Governance Automation Observability Healthcare Intelligence and Enterprise Data Systems
DOI:
https://doi.org/10.15662/IJARCST.2025.0805035Keywords:
Artificial Intelligence, Secure Cloud Transformation, Governance Automation, Cloud Security, Observability, Healthcare Intelligence, Enterprise Data Systems, Data Governance, Predictive Analytics, Digital Transformation, Compliance Management, Enterprise ArchitectureAbstract
Artificial Intelligence (AI) is transforming cloud computing by enabling intelligent automation, enhanced security, real-time monitoring, and advanced analytics across enterprise environments. The integration of AI-driven governance automation, observability platforms, healthcare intelligence systems, and enterprise data architectures has emerged as a critical strategy for achieving secure cloud transformation. Organizations increasingly rely on cloud infrastructures to manage large-scale data, streamline operations, and improve decision-making processes. However, challenges related to security, compliance, data privacy, and operational complexity necessitate advanced AI-based solutions. This study explores how AI technologies support secure cloud transformation through automated governance frameworks, predictive observability mechanisms, healthcare data intelligence, and integrated enterprise data systems. Governance automation ensures regulatory compliance and policy enforcement, while observability platforms provide continuous monitoring and anomaly detection. In healthcare environments, AI-driven cloud systems facilitate clinical decision support, patient analytics, and secure data sharing. Enterprise data systems benefit from intelligent data integration, processing, and management capabilities that enhance organizational efficiency. The research highlights the benefits, challenges, and implementation considerations associated with AI-enabled cloud ecosystems. Findings indicate that combining AI with cloud security and data governance significantly improves operational resilience, scalability, and business intelligence. The study concludes that AI-driven secure cloud transformation represents a sustainable framework for modern digital enterprises seeking innovation, compliance, and long-term competitive advantage.
References
1. Yatam, S. N. K. (2025). Infrastructure as Code with Embedded Security Controls: A Policy-as-Code Approach in Multi-Cloud Environments. Journal Of Engineering And Computer Sciences, 4(7), 131-140.
2. Devineni, A. (2023). Automated Compliance-Driven Patch Management and Security Hardening in Multi-Cloud Banking Infrastructure Using IaC and Python Orchestration. The American Journal of ET, 5(12), 68-80.
3. Lanka, S. (2025). Architectural patterns for AI-enabled triage and crisis prediction systems in public health platforms. International Journal of Research and Applied Innovations, 8(1), 11648–11662. https://doi.org/10.15662/IJRAI.2025.0801003
4. Konakalla, K. (2024). Integrating ChatGPT with Salesforce for real-time market insights on accounts. International Journal of Scientific Research in Engineering and Management, 8, 1-5.
5. Hussain, S., Barigidad, S., Srivastava, L., Srivastava, P. K., Gupta, S., & Kanaujia, S. (2025, June). Novel Diabetic Retinopathy Disease Predictor using CNN for Healthcare Systems. In 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 1065-1070). IEEE.
6. Veershetty, G. (2023). SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis. International Journal of Emerging Research in Engineering and Technology, 4(1), 218-227.
7. Gopisetty, S. (2024). When Healthcare Lags, Banking Leaks: A Generative AI Framework to Stop Time‑Based Data Spills in Cross‑Sector Federated Learning. International Journal of AI, BigData, Computational and Management Studies, 5(4), 238-260.
8. Polamreddy, V. R. (2024). Hybrid On-Premise to Cloud Data Migration: Architectural Patterns for Controlled One-Way Synchronization. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8143-8156.
9. Manda, P. (2023). Leveraging AI to Improve Performance Tuning in Post-Migration Oracle Cloud Environments. International Journal of Research Publications in Engineering Technology and Management (IJRPETM), 6(3), 8714–8725.
10. Makkena, B. (2024). Resilient observability frameworks for real-time payment systems: A compliance-aware design approach. Journal of Information Systems Engineering and Management, 9(3).
11. Navandar, P. (2024). Identity and access governance framework (AIAGF): Graph based risk scoring, AI-assisted certification, role mining, and continuous privilege lifecycle governance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 10004–10017. https://doi.org/10.15662/IJRPETM.2024.0701012
12. Kotla, M. R. T. (2024). Optimizing enterprise integration pipelines using cloud-native data engineering and middleware solutions. International Journal of Research Publications in Engineering, Technology and Management, 7(5), 11311–11314.
13. Kavuri, S. (2023). Machine learning approaches for security vulnerability detection in software testing. Computer Fraud & Security, 21-31.
14. 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
15. Juvvadi, R. R. (2022). Machine learning for anomaly detection in the financial close: A journal entry risk-scoring framework for SAP S/4HANA. International Journal of Communication Networks and Information Security, 14(3), 1684–1695.
