Blockchain Enabled Governance Model for Enterprise Cloud Based AI and Machine Learning in Healthcare
Abstract
The rapid adoption of enterprise cloud infrastructures to deploy artificial intelligence (AI) and machine learning (ML) in healthcare has introduced unprecedented capabilities in predictive analytics, personalized medicine, and operational optimization. However, the integration of cloud-native AI systems within healthcare ecosystems presents critical governance challenges related to data privacy, algorithmic transparency, regulatory compliance, accountability, and trust. A blockchain-enabled governance model offers a decentralized, immutable, and auditable framework to address these concerns while enhancing interoperability and secure data exchange. By leveraging distributed ledger technology, smart contracts, and cryptographic validation mechanisms, healthcare enterprises can establish transparent consent management, secure data provenance tracking, automated regulatory enforcement, and trustworthy AI lifecycle management. This paper proposes a blockchain-enabled governance architecture tailored for enterprise cloud-based AI and ML systems in healthcare. It explores existing governance limitations, analyzes technological integration mechanisms, and presents a structured research methodology for implementation and evaluation. The model emphasizes compliance with healthcare regulations, ethical AI standards, and scalable enterprise deployment. Ultimately, this governance framework aims to foster trust among stakeholders—including patients, clinicians, regulators, and technology providers—while supporting innovation, accountability, and clinical excellence in cloud-driven healthcare ecosystems.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
14936-14945 |
Published |
September 21, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Andrea Marrella (%2025). Blockchain Enabled Governance Model for Enterprise Cloud Based AI and Machine Learning in Healthcare. International Journal of Science, Research and Technology , Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14936-14945. https://doi.org/10.15662/IJSRAT.2025.0805003 |
References
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
10. 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.
11. 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.
12. 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.
13. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325–330). IEEE.
14. 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).
15. 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.
16. 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.
17. 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.
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. 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.
20. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1–6). IEEE.
21. N. Lokiny. (2020). The Role of AI and Machine Learning in DevOps Automation, 7(2), 328–333.
22. 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.
23. 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.
24. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
25. 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.
26. 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.
27. 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.
28. 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.
29. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
30. 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.
31. 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.
32. 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.
33. Kamadi, S. (January 2025). Machine learning and AI architecture: A comprehensive framework for production-grade intelligent systems. World Journal of Advanced Research and Reviews, 27(1), 2789–2799. https://www.researchgate.net/profile/Sandeep-Kamadi/publication/398922844_Machine_Learning_and_AI_Architecture_A_Comprehensive_Framework_for_Production-Grade_Intelligent_Systems/links/6948e4529aa6b4649dc30185/Machine-Learning-and-AI-Architecture-A-Comprehensive-Framework-for-Production-Grade-Intelligent-Systems.pdf
34. 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.
35. 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.
36. 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.
37. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483-523.