AI Driven Enterprise Cloud Architecture with Blockchain Governance for Proactive Healthcare Risk Mitigation
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0106005Keywords:
Artificial Intelligence, Machine Learning, Cloud Computing, Enterprise Architecture, Blockchain Technology, Digital Governance, Healthcare Informatics, Cybersecurity and Threat Detection, Edge to Cloud Systems, Privacy Preserving Data AnalyticsAbstract
The rapid digital transformation of healthcare systems has introduced unprecedented opportunities for proactive risk mitigation through artificial intelligence (AI), cloud computing, and blockchain governance. However, fragmentation of health data, cybersecurity vulnerabilities, regulatory complexities, and lack of interoperability continue to hinder predictive and preventive healthcare strategies. This paper proposes an AI-driven enterprise cloud architecture integrated with blockchain governance to enable secure, scalable, and intelligent healthcare risk mitigation. The architecture leverages machine learning models for predictive analytics, cloud-native microservices for scalability, and blockchain-based smart contracts for trust, transparency, and compliance enforcement. AI algorithms process multimodal healthcare data—including electronic health records (EHRs), wearable sensor data, imaging systems, and genomics—to predict clinical risks, operational inefficiencies, and financial fraud. Cloud infrastructure ensures elastic resource allocation, real-time analytics, and distributed system resilience. Blockchain technology introduces immutable audit trails, decentralized identity management, and automated regulatory compliance mechanisms. This integrated framework enhances interoperability, strengthens cybersecurity, reduces fraud, and improves patient-centric care delivery. The study explores design principles, literature foundations, and a comprehensive research methodology for implementation and validation. The proposed model supports predictive healthcare governance and sustainable digital health ecosystems, contributing to resilient, transparent, and data-driven healthcare enterprises.
References
1. Chennamsetty, C. S. (2022). Hardware-Software Co-Design for Sparse and Long-Context AI Models: Architectural Strategies and Platforms. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 5(5), 7121-7133.
2. Kamadi, S. (2022). Adaptive Federated Data Science & MLOps Architecture: A Comprehensive Framework for Distributed Machine Learning Systems. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 8(6), 745-755.
3. 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.
4. 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.
5. Lokiny, N. (2020). The Role of AI and Machine Learning in DevOps Automation, 7(2), 328–333.
6. Inampudi, R. K., Surampudi, Y., & Kondaveeti, D. (2023). AI-driven real-time risk assessment for financial transactions: leveraging deep learning models to minimize fraud and improve payment compliance. Journal of Artificial Intelligence Research and Applications, 3(1), 716-758.
7. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
8. Kusumba, S. (2025). Modernizing Healthcare Finance: An Integrated Budget Analytics Data Warehouse for Transparency and Performance. Journal of Computer Science and Technology Studies, 7(7), 567-573.
9. Mudunuri, P. R. (2023). Governance-aware infrastructure-as-code for regulated research environments. International Journal of Research in Engineering, Project Management and Technology (IJRPETM), 6(4), 9017–9028.
10. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive Analysis of Artificial Intelligence Applications for Early Detection of Ovarian Tumours: Current Trends and Future Directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-9). IEEE.
11. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
12. 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.
13. Keezhadath, A. A., Sethuraman, S., & Das, D. (2021). Cost-Efficient Cloud Data Processing: Strategies for Enterprise-Wide Cost Optimization. American Journal of Data Science and Artificial Intelligence Innovations, 1, 135-168.
14. Koka, H., Karunanithi, G., Renganathan, R., Pradhan, C., & Singh, A. (2025). Towards a unified framework for serverless microservices in cloud-native environments. International Journal on Recent and Innovation Trends in Computing and Communication, 13(1), 318–324. https://www.researchgate.net/profile/Chittaranjan-Pradhan-4/publication/398969359_Towards_a_Unified_Framework_for_Serverless_Microservices_in_Cloud-Native_Environments/links/6949a5079aa6b4649dc35799/Towards-a-Unified-Framework-for-Serverless-Microservices-in-Cloud-Native-Environments.pdf
15. 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.
16. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
17. Surisetty, L. S. (2023). Proactive Threat Mitigation in API Ecosystems through AI-Powered Anomaly Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(1), 7633-7642.
18. Anumula, S. R. (2022). Governance frameworks for automated enterprise decision systems. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 137–157.
19. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
20. Thakran, V. (2025, June). An Analysis of Machine Learning Solutions for Precise Forecasting of Oil and Gas Pipeline. In 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE) (pp. 1-6). IEEE.
21. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
22. Nandhini, T., Babu, M. R., Natarajan, B., Subramaniam, K., & Prasanna, D. (2024). A NOVEL HYBRID ALGORITHM COMBINING NEURAL NETWORKS AND GENETIC PROGRAMMING FOR CLOUD RESOURCE MANAGEMENT. Frontiers in Health Informatics, 13(8).
23. Gurajapu, A., & Garimella, V. (2025). Edge-to-cloud workflows for low-latency telecom services: Optimizing offload decisions. International Journal of Research and Applied Innovations (IJRAI), 8(4), 12638–12641.
24. Kondisetty, K., Mohammed, A. S., & Muthusamy, P. (2024). Omni-Channel Customer Onboarding with NLP-Powered Document Intelligence. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124-157.
25. 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), 14532–14543.
26. Panda, M. R., & Chinthalapelly, P. R. (2023). Banking Sandbox Evaluation for Open Banking Ecosystems Using Agent-Based Modeling. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.
27. Ananth, S., Radha, D. K., Prema, D. S., & Nirajan, K. (2019). Fake news detection using convolution neural network in deep learning. International Journal of Innovative Research in Computer and Communication Engineering, 7(1), 49-63.
28. 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.
29. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
30. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12-24.
31. Sriramoju, S. (2024). Designing scalable and fault-tolerant architectures for cloud-based integration platforms. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13839–13851.
32. Genne, S. (2023). Improving enterprise web responsiveness through server-side rendering in Next.js. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(4), 7313–7323.
33. Keezhadath, A. A., Sethuraman, S., & Das, D. (2021). Cost-Efficient Cloud Data Processing: Strategies for Enterprise-Wide Cost Optimization. American Journal of Data Science and Artificial Intelligence Innovations, 1, 135-168.
34. Ponugoti, M. (2024). AI-Driven Microservice Architectures: Enhancing Compliance and Decision Intelligence in Cloud Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14880.
35. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
36. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
37. Prasanna, D., Ahamed, N. A., Abinesh, S., Karthikeyan, G., & Inbatamilan, R. (2024, November). Cloud based automatically human document authentication processes for secured system. In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-7). IEEE.
38. Kalyanasundaram, P. D., Devi, C., & Pachyappan, R. (2024). Autoencoder-Based Anomaly Detection on Metadata Metrics for Privacy Enforcement Monitoring. Journal of Artificial Intelligence & Machine Learning Studies, 8, 124-155.
39. Sikarwar, V. (2025). AI-Powered Process Mining for Intelligent, Personalized Customer Experience in the Insurance Sector. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12418-12428.
40. Meshram, A. K. (2025). Secure and scalable financial intelligence systems using big data analytics in hybrid cloud environments. International Journal of Research and Applied Innovations (IJRAI), 8(6), 13083–13095.
41. Kanikanti, V. S. N., Tiwari, S. K., Nayan, V., Suryawanshi, S., & Chauhan, R. (2025, November). Deep Q-Learning Driven Dynamic Optimal Task Scheduling for Cloud Computing Using Optimal Queuing. In 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 217-222). IEEE.
