Zero Trust AI Architecture for Enterprise Healthcare Risk Governance in Cloud Ecosystems with Secure Data Encryption
DOI:
https://doi.org/10.15662/IJRAI.2025.0806035Keywords:
Zero Trust Architecture, Healthcare AI Governance, Cloud Security, Secure Data Encryption, Enterprise Risk Management, HIPAA Compliance, AI Risk Governance, Identity Access Management, Cybersecurity in Healthcare, Secure Cloud AIAbstract
The accelerated adoption of cloud computing and artificial intelligence (AI) in healthcare has significantly enhanced predictive risk analytics, operational efficiency, and patient-centered care. However, increased data exchange, distributed infrastructure, and AI-driven automation expose healthcare enterprises to heightened cybersecurity, privacy, and governance risks. Traditional perimeter-based security models are insufficient in protecting sensitive health data within complex cloud ecosystems. This research proposes a Zero Trust AI Architecture designed specifically for Enterprise Healthcare Risk Governance, integrating secure data encryption, identity-centric access control, continuous verification, and AI governance mechanisms.
The proposed framework adopts a “never trust, always verify” security paradigm across cloud-native AI pipelines. It embeds encryption at rest and in transit, role-based and attribute-based access controls, multi-factor authentication, secure API gateways, and continuous monitoring. AI risk governance modules incorporate model validation, bias detection, audit trails, explainability tools, and regulatory compliance alignment with HIPAA and GDPR standards.
By combining Zero Trust principles with adaptive AI governance in cloud environments, healthcare enterprises can mitigate cyber threats, prevent unauthorized data access, ensure regulatory compliance, and maintain operational resilience. This study provides a comprehensive enterprise architecture and implementation methodology for secure, trustworthy, and compliant healthcare AI risk ecosystems.
References
1. Sampath Kumar Konda, “Distributed AI Infrastructure Orchestration: A Hyperscale Multi-Cloud Framework for Geographic Load Balancing with Renewable Energy Optimization”, Int J Sci Res Sci Eng Technol, vol. 11, no. 4, pp. 522–533, Aug. 2024, doi: 10.32628/IJSRSET242438.
2. Ganesan, G. B. K. (2025). Fraud Detection Systems in Enterprise Integration Architecture. IJSAT-International Journal on Science and Technology, 16(1).
3. Srinivasan, V., Kondisetty, K., Gorle, S., Devi, C., Panda, M. R., & Musunuru, M. V. (2025, December). Digital Twin Enabled Deep Learning System for Predictive Monitoring of Cardiovascular Health. In 2025 International Conference on NexGen Networks and Cybernetics (IC2NC) (pp. 916-922). IEEE.
4. Parvin, A. (2025). Comparative analysis of child development approaches across different education systems globally. Journal of Humanities and Social Sciences Studies, 7(4), 95-113.
5. Kunju, S. S., & Ponnoju, S. C. (2023). Enhancing User Journey Consistency via Cross-Application Integration Using MX Bridge Algorithm in Angular Applications. American Journal of Data Science and Artificial Intelligence Innovations, 3, 120-156.
6. Balamuralidhar, S. V. (2018). Dual access control with effective cross-tenant revocation in cloud computing. IOSR Journal of Engineering (IOSRJEN), 8(9), 51–54. Retrieved from https://www.iosrjen.org/Papers/vol8_issue9/Version-2/I0809025154.pdf
7. Vimal Raja, G. (2025). Context-Aware Demand Forecasting in Grocery Retail Using Generative AI: A Multivariate Approach Incorporating Weather, Local Events, and Consumer Behaviour. International Journal of Innovative Research in Science Engineering and Technology (Ijirset), 14(1), 743-746.
8. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
9. Srinivas, S., Sura, R., Kumar, B., Kumar, M., Pandey, S. D., & Kumar, R. (2025, July). Enhancing Distributed Database Efficiency using Edge Computing. In 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (pp. 1-5). IEEE.
10. 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.
11. Sanepalli, Uttama Reddy. (2023). Distributed Multi-Cloud Data Lake Architecture for Enterprise-Scale Workplace Benefits Analytics: A Federated Approach to Heterogeneous Financial Data Integration. International Journal of Computer Engineering and Technology (IJCET), 14(1), 268-282.
12. Ahuja, D. (2025, August). Intelligent Failure Prediction in CI/CD Pipelines Using Efficient Machine Learning Techniques. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-7). IEEE.
13. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
14. Ambati, K. C. (2025). An event-driven architecture for autonomous supply chain risk detection and decision automation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(1), 1202–1211.
15. Vijayakumar, R., & Gireesh, G. (2013, July). Quantitative analysis and fracture detection of pelvic bone X-ray images. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1-7). IEEE.
16. Panda, S. S. (2023). Smart Machines, Smarter Outcomes the Rise of Self-Learning Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9004-9015.
17. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
18. Gowda, M. K. S. (2025). Driving Return on Risk-Weighted Assets Improvement via Audit, Analytics, and Advanced Modeling in Bank Portfolio Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12197-12206.
19. Akhtaruzzaman, K., MdAbulKalam, A., Mohammad Kabir, H., & KM, Z. (2024). Driving US Business Growth with AI-Driven Intelligent Automation: Building Decision-Making Infrastructure to Improve Productivity and Reduce Inefficiencies. American Journal of Engineering, Mechanics and Architecture, 2(11), 171-198. http://eprints.umsida.ac.id/16412/1/171-198%2BDriving%2BU.S.%2BBusiness%2BGrowth%2Bwith%2BAI-Driven%2BIntelligent%2BAutomation.pdf
20. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
21. Nallamothu, T. K. (2025). Optimizing Healthcare Operations and Patient Care through AI-Powered Analytics with Power BI and DAX Copilot. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12161-12169.
22. 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.
23. Grandhe, K. (2025). Innovative options to drive financial agility: Real-time reporting with SAP BW/4HANA and SAP Analytics Cloud. IJLRP–International Journal of Leading Research Publication, 6(7). https://doi.org/10.70528/IJLRP.v6.i7.1710
24. Ireddy, Ravi Kumar. (2023). API-driven interoperability framework for corporate treasury management: A financial data exchange standard implementation with secure data aggregation networks. World Journal of Advanced Research and Reviews, 19(2), 1727–1738. https://doi.org/10.30574/wjarr.2023.19.2.1609
25. Bapatla, S. K. S. (2025). FHIR 2.0: Beyond Interoperability to AI-Ready Healthcare Ecosystems. International Journal of Computing and Engineering, 7(18), 48-63.
26. 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.
27. Sarwar, J., Kumar, V., Afrin, S., & Gupta, A. B. (2025). Intelligent Cybersecurity Systems to Safeguard US National Interests Using AI and Machine Learning. Research Journal of Engineering and Medical Science, 1(2), 1-13.
28. Ande, B. R. (2024). Leveraging Azure OpenAI and Cognitive Services for Enterprise Automation: Streamlining Operations and Enhancing Decision-Making. J. Inf. Syst. Eng. Manag, 9(4s), 209-216.
29. Jaikrishna, G., & Rajendran, S. (2020). Cost-effective privacy preserving of intermediate data using group search optimisation algorithm. International Journal of Business Information Systems, 35(2), 132-151.
30. Mulla, F. (2024). Choosing the Best Architecture for Mobile Applications. International Journal Of Research In Computer Applications And Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
31. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented Reality Shopping and IoT-Enabled Virtual Try-On with Cloud Services for Interactive Product Displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880-885). IEEE.
32. Kamadi, S. (2023). Cloud-Native Analytics Platform for Governed Real-Time Streaming and Feature Engineering
33. Gadige, C. D. (2025). Building the adaptable enterprise: Trends in composable and event-driven Salesforce architectures. International Journal of Research and Applied Innovations (IJRAI), 8(6), 13119–13125.
34. Gopinathan, V. R. (2024). AI-Driven Customer Support Automation: A Hybrid Human Machine Collaboration Model for Real-Time Service Delivery. International Journal of Technology, Management and Humanities, 10(01), 67-83.
35. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566-1570). IEEE.
36. Ahuja, D. (2025, August). Intelligent Failure Prediction in CI/CD Pipelines Using Efficient Machine Learning Techniques. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1-7). IEEE.
37. Srinivasan, V., Kondisetty, K., Gorle, S., Devi, C., Panda, M. R., & Musunuru, M. V. (2025, December). Digital Twin Enabled Deep Learning System for Predictive Monitoring of Cardiovascular Health. In 2025 International Conference on NexGen Networks and Cybernetics (IC2NC) (pp. 916-922). IEEE.
38. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-8). IEEE.
39. Gurajapu, A., Anumolu, S., Garimella, V., Chundi, V. M. S. R., & Gubbala, V. S. A. P. (2025). Modernizing Mission-Critical Systems: A Hybrid-Cloud Transformation Roadmap. Journal of Computer Science and Technology Studies, 7(1), 425-430.
40. Devi, C., Musunuru, M. V., & Mohammed, A. S. (2023). Reinforcement-Learning Scheduler for Multi-Tenant Spark Clustersunder Privacy Constraints. Newark Journal of Human-Centric AI and Robotics Interaction, 3, 496-527.
41. Ramidi, M. (2025). AI integration in government mobile platforms for secure and innovative digital solutions. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14543.
42. Gangina, P. (2024). AI-enhanced DevSecOps: Automating security compliance in cloud-native pipelines. International Journal of Future Innovative Science and Technology, 7(4), 13124–13135.
