Secure AI-Driven Cloud-Native Enterprise Platforms for Compliance Automation Healthcare Analytics and Cyber Defense
DOI:
https://doi.org/10.15662/IJEETR.2023.0506018Keywords:
AI-driven enterprise platforms, Cloud-native security, Compliance automation, Healthcare analytics, Cyber defense, DevSecOps, Zero-trust architecture, Privacy-preserving AI, Microservices, Regulatory governanceAbstract
The rapid digital transformation of enterprise ecosystems has accelerated the adoption of cloud-native architectures and artificial intelligence (AI) technologies across critical sectors such as healthcare, regulatory compliance, and cybersecurity. However, the integration of AI within distributed cloud environments introduces new security, governance, and ethical challenges. This paper explores the design and implementation of secure AI-driven cloud-native enterprise platforms tailored for compliance automation, healthcare analytics, and cyber defense applications. The study examines architectural frameworks leveraging microservices, containerization, zero-trust security models, and automated governance mechanisms to ensure regulatory adherence and operational resilience. In healthcare analytics, AI models deployed within secure cloud infrastructures enable predictive diagnostics, patient risk stratification, and operational optimization while maintaining strict compliance with regulatory standards such as HIPAA and GDPR. In cyber defense, AI-powered anomaly detection and threat intelligence systems enhance proactive security capabilities. The research further proposes a comprehensive methodology for developing scalable, secure, and compliant enterprise platforms by integrating DevSecOps, privacy-preserving machine learning, and policy-driven orchestration. The paper concludes by outlining advantages, limitations, and future research directions toward trustworthy, explainable, and autonomous enterprise AI ecosystems.
References
1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A.,& Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
2. Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. IEEE BigData.
3. Bozic, D., & Vukasinovic, M. (2019). Microservices security: A systematic mapping study. Journal of Systems and Software, 156, 110–130.
4. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
5. Ananth, S., Kalpana, A. M., & Vijayarajeswari, R. (2020). A dynamic technique to enhance quality of service in software-defined network-based wireless sensor network (DTEQT) using machine learning. International Journal of Wavelets, Multiresolution and Information Processing, 18(01), 1941020.
6. Dinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. S. (2017). Federated learning for wireless networks: Methods, opportunities, and challenges. IEEE Communications Magazine, 57(12), 66–71.
7. Gangina, P. (2023). Service mesh implementation strategies for zero-downtime migrations in production environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7208–7220.
8. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
9. Malarkodi, K. P., Sugumar, R., Baswaraj, D., Hasan, A., & Kousalya, A. (2023, March). Cyber Physical Systems: Security Technologies, Application and Defense. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2536-2546). IEEE.
10. Nagarajan, C., Neelakrishnan, G., Janani, R., Maithili, S., & Ramya, G. (2022). Investigation on Fault Analysis for Power Transformers Using Adaptive Differential Relay. Asian Journal of Electrical Sciences, 11(1), 1-8.
11. Sriramoju, S. (2022). API-driven account onboarding framework with real-time compliance automation. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8132–8144.
12. Mudunuri, P. R. (2023). Automation-driven reliability engineering for public-sector biomedical systems. International Journal of Humanities and Information Technology (IJHIT), 5(1), 68–86.
13. Anumula, S. R. (2023). Resilience engineering for intelligent enterprise platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5954–5965.
14. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
15. Chivukula, V. (2022). Improvement in Minimum Detectable Effects in Randomized Control Trials: Comparing User-Based and Geo-Based Randomization. International Journal of Computer Technology and Electronics Communication, 5(4), 5442-5446.
16. Hasenkhan, F., Keezhadath, A. A., & Amarapalli, L. (2023). Intelligent Data Partitioning for Distributed Cloud Analytics. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 106-145.
17. Panda, M. R., & Sethuraman, S. (2022). Blockchain-Based Regulatory Reporting with Zero-Knowledge Proofs. Essex Journal of AI Ethics and Responsible Innovation, 2, 495-532.
18. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
19. Singh, A. (2021). Evaluating reliability in mission-critical communication: Methods and metrics. International Journal of Innovative Research in Computer and Technology (IJIRCT), 7(2), 1–11. Retrieved from https://www.ijirct.org/download.php?a_pid=2501102 Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
20. Surisetty, L. S. (2022). Designing Intelligent Integration Engines for Healthcare: From HL7 and X12 to FHIR and Beyond. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(1), 5989-5998.
21. Chennamsetty, C. S. (2023). Neural Pipeline Orchestration: Deep Learning Approaches to Software Development Bottleneck Elimination. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(4), 8674-8680.
22. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
23. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111.
24. Pandey, A., Chauhan, A., & Gupta, A. (2023). Voice Based Sign Language Detection For Dumb People Communication Using Machine Learning. Journal of Pharmaceutical Negative Results, 14(2).
25. Ramidi, M. (2023). Accessibility-centered mobile architectures for government health initiatives. International Journal of Research and Applied Innovations (IJRAI), 6(2), 8597–8610.
26. Gaddapuri, N. S. (2022). APPLICATION OF QUANTUM COMPUTING IN DIGITAL EDUCATION SYSTEMS. Power System Protection and Control, 50(2), 12-24.
27. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
28. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
29. Genne, S. (2022). A secure architecture for real-time data exchange in HIPAA-compliant patient portals. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6202–6215.
30. Mogil, V. B. (2023). Implementing role-based access control for healthcare data using SharePoint. International Journal of Engineering & Extended Technologies Research, 5(2), 6323–6333.
31. Vaidya, S., Shah, N., Shah, N., & Shankarmani, R. (2020, May). Real-time object detection for visually challenged people. In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 311-316). IEEE.
32. Sethuraman, S., Devi, C., & Murthy, C. G. (2022). Policy-as-Code Row-Level Security: Compiling DPL Rules into Spark SQL Views. American Journal of Data Science and Artificial Intelligence Innovations, 2, 673-705.
33. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
34. Verma, A., Cherkasova, L., & Campbell, R. (2014). ARASHI: A self-tuning task scheduler for Hadoop. Cluster Computing, 17(3), 753–770.
35. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18.
