AI-Driven Cloud Architecture for Healthcare Data Governance with Financial and Risk Integration
DOI:
https://doi.org/10.15662/IJEETR.2025.0705006Keywords:
Artificial intelligence, Cloud architecture, Healthcare data governance, Financial systems, Risk management, Data security, API integrationAbstract
The growing convergence of artificial intelligence (AI), cloud computing, healthcare information systems, and financial platforms has intensified the need for robust data governance frameworks capable of managing sensitive and high-risk data. This paper presents an AI-driven cloud architecture designed to support healthcare data governance with integrated financial and risk management capabilities. The proposed architecture leverages cloud-native services to enable scalable data processing while enforcing governance policies related to data privacy, access control, and regulatory compliance. AI techniques are employed to automate data classification, policy enforcement, and risk assessment across heterogeneous healthcare and financial datasets. Secure network design, encryption mechanisms, and API-based interoperability are incorporated to facilitate controlled data exchange among stakeholders without compromising confidentiality. The architecture also integrates risk analytics to identify operational, financial, and cybersecurity threats in real time. The proposed solution demonstrates how AI-driven cloud architectures can enhance governance, transparency, and trust in healthcare data ecosystems that increasingly interact with financial systems.
References
1. Lokeshkumar Madabathula, “AI- Driven Risk Management in Finance: Predictive Models for Market Volatility, International Journal of Information Technology and Management Information Systems 16 ( 2 ): 293–302.
2. Meka, S. (2025). Redefining Data Access: A Decentralized SDK for Unified and Secure Data Retrieval. Journal Code, 1325, 7624.
3. 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.
4. Rajurkar, P. (2023). Waste-to-Resource Networks for Inorganic Chemical Manufacturing A Case Study. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5944-5953.
5. Mahajan, N. (2023). A predictive framework for adaptive resources allocation and risk-adjusted performance in engineering programs. Int. J. Intell. Syst. Appl. Eng, 11(11s), 866.
6. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
7. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.
8. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY- PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology, 3(4), 3400–3405.
9. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
10. Kasireddy, J. R. (2023). Operationalizing lakehouse table formats: A comparative study of Iceberg, Delta, and Hudi workloads. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8371–8381. https://doi.org/10.15662/IJRPETM.2023.0602002
11. Thambireddy, S. (2022). SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems. International Journal of Humanities and Information Technology, 4(01-03), 53-66.
12. Bussu, V. R. R. (2024). End-to-End Architecture and Implementation of a Unified Lakehouse Platform for Multi-ERP Data Integration using Azure Data Lake and the Databricks Lakehouse Governance Framework. International Journal of Computer Technology and Electronics Communication, 7(4), 9128-9136.
13. Sharma, A., Kabade, S., & Kagalkar, A. (2024). AI-Driven and Cloud-Enabled System for Automated Reconciliation and Regulatory Compliance in Pension Fund Management. International Journal of Emerging Research in Engineering and Technology, 5(2), 65-73.
14. Paul, D., Sudharsanam, S. R., & Surampudi, Y. (2021). Implementing Continuous Integration and Continuous Deployment Pipelines in Hybrid Cloud Environments: Challenges and Solutions. Journal of Science & Technology, 2(1), 275-318.
15. 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.
16. Kusumba, S. (2024). Delivering the Power of Data-Driven Decisions: An AI-Enabled Data Strategy Framework for Healthcare Financial Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7799-7806.
17. Singh, A. Evaluating Reliability in Mission-Critical Communication: Methods and Metrics. https://www.researchgate.net/profile/Abhishek-Singh-679/publication/393844208_Evaluating_Reliability_in_Mission-Critical_Communication_Methods_and_Metrics/links/687d001a1a77b36b5b0439e6/Evaluating-Reliability-in-Mission-Critical-Communication-Methods-and-Metrics.pdf
18. Rahman, M. A., Rathore, S., & Khan, S. (2020). Policy-as-Code for Cloud Governance. IEEE Cloud Computing, 7(2), 34–42.
19. Karnam, A. (2021). The Architecture of Reliability: SAP Landscape Strategy, System Refreshes, and Cross-Platform Integrations. International Journal of Research and Applied Innovations, 4(5), 5833–5844. https://doi.org/10.15662/IJRAI.2021.0405005
20. 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.
21. 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.
22. Thota, S. K., & Anumula, S. K. (2024). Quantum-Enabled Drones for Battlefield Information Dominance: Integrating Sensing, Computing, and Secure Communications. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 147-150.
23. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
24. Gupta, S., Barigidad, S., Hussain, S., Dubey, S., & Kanaujia, S. (2025, February). Hybrid Machine Learning for Feature-Based Spam Detection. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 801-806). IEEE.
25. 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.
26. 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.
27. Manda, P. (2023). Migrating Oracle Databases to the Cloud: Best Practices for Performance, Uptime, and Risk Mitigation. International Journal of Humanities and Information Technology, 5(02), 1-7.
28. 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).
29. Gollapudi, R. (2025). Data-Driven Risk Scoring For Grid Assets Using Centralized Production Databases. International Journal Of Advances In Signal And Image Sciences, 50-87.
30. 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.
31. Boddupally, H. L. (2021). A telemetry-centric approach to identifying recurrent defect structures in software systems. Available at SSRN 6270478.
32. Anand, P. V., & Anand, L. (2023, December). An Enhanced Breast Cancer Diagnosis using RESNET50. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-5). IEEE.
33. 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.
34. Sakhawat Hussain, T., Rahanuma, T., & Md Manarat Uddin, M. (2023). Privacy-Preserving Behavior Analytics for Workforce Retention Approach. American Journal of Engineering, Mechanics and Architecture, 1(9), 188-215.
35. 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.
36. Thumala, S. R., Mane, V., Patil, T., Tambe, P., & Inamdar, C. (2025, June). Full Stack Video Conferencing App using TypeScript and NextJS. In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1285-1291). IEEE.
37. Raghupathi, W., & Tan, J. (2008). Health Care IT: A Framework for Data Governance. Journal of Medical Systems, 32(5), 407–414.
