Explainable AI for Risk-Aware Enterprise Decision Intelligence in Sovereign Cloud Infrastructure with Compliance Assurance
DOI:
https://doi.org/10.15680/IJCTECE.2024.0705014Keywords:
Explainable Artificial Intelligence, Enterprise Decision Intelligence, Risk Management, Sovereign Cloud, Compliance Assurance, Responsible AI, Data Sovereignty, Cloud Security, Regulatory Compliance, Artificial Intelligence Governance, Auditability, Decision SupportAbstract
The rapid adoption of sovereign cloud infrastructure has created a need for enterprise decision intelligence systems that can combine artificial intelligence, regulatory compliance, cybersecurity, operational resilience, and organizational risk management within a controlled digital environment. Conventional artificial intelligence solutions frequently provide highly accurate predictions but offer limited transparency regarding how decisions are generated, creating challenges for regulated organizations that must demonstrate accountability, traceability, and compliance. This paper proposes an explainable artificial intelligence (XAI) framework for risk-aware enterprise decision intelligence in sovereign cloud environments. The proposed approach integrates explainable machine learning, risk assessment, policy-aware decision analytics, compliance monitoring, data sovereignty controls, and auditable decision workflows. The framework is designed to identify operational and regulatory risks while providing human-interpretable explanations for predictions, recommendations, and automated actions. A research methodology based on a mixed-method and design-science approach is proposed to evaluate predictive performance, explanation quality, compliance assurance, security, and decision usefulness. The study considers dimensions including explainability, model reliability, data governance, regulatory alignment, cloud sovereignty, auditability, and human oversight. The expected contribution is a structured decision-intelligence architecture that enables enterprises to obtain actionable AI-driven insights without sacrificing transparency or jurisdictional control. The proposed framework can support responsible AI adoption in highly regulated sectors requiring trustworthy, accountable, and sovereign digital infrastructure
References
1. Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021). A survey on security and privacy of federated learning. Future Generation Computer Systems, 115, 619–640. https://doi.org/10.1016/j.future.2020.10.007
2. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
3. Manda, P. (2024). Oracle E-Business Suite modernization: The transition from 12.1.3 to 12.2.8. International Journal of Research and Applied Innovations, 7(3), 10838–10842.
4. Gummadi, V. P. K. (2021). Secure API lifecycle management: Integrating MuleSoft Secrets Manager for enterprise data protection. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 537-540.
5. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
6. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
7. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
8. Jain, R. (2018). Beyond stateless: A production architecture for running distributed databases on Kubernetes at scale. International Journal of Emerging Trends in Engineering and Management Research, 3(5), 4165–4172.
9. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology (IJSRAT), 5(3), 7777–7783. https://www.ijsrat.com/index.php/ijsrat/issue/view/23
10. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
11. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
12. Rahul Reddy Bandhela, RamMohan Reddy Kundavaram. (2023). A Comparative Study on Neural Network Architectures for Image Recognition Applications. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1334–1342. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3566
13. Khare, A., Khare, S., Goel, O., & Goel, P. (2024). Strategies for successful organizational change management in large digital transformation. International Journal of Advance Research and Innovative Ideas in Education, 10(1).
14. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
15. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
16. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
17. Padmanabham, S. (2023). Secure API gateway architecture for open banking. International Journal of Computer Technology and Electronics Communication, 6(6), 8166–8174.
18. Vedula, J. (2023). Enterprise release assurance for interconnected energy data, cloud, and application platforms: A governance framework for coordinated, multi-team release delivery. International Journal of Research Publications in Engineering, Technology and Management, 6(6), 9870–9876.
19. Anand, L. (2023). Machine Learning Enabled Enterprise Integration through Intelligent API Governance Secure Cloud Infrastructure and Automated Operations. International Journal of Research and Applied Innovations, 6(3), 5972-5979.
20. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
21. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
22. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
23. Md Sajedul Karim Chy, Salman Mohammad Abdullah, Mahbub Ahmed Nabil, Abidul Alam, Md Himeluzzaman, Tofayel Ahmed Onik, Shaown Mahamud Shakil, Hafiz Aziz Khan (2022). QShield-NS: A Variational Quantum Machine Learning Model for Zero-Day Cyber Threat Detection in National Security Systems. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 9266-9283. https://doi.org/10.15662/IJFIST.2022.0505009
24. 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.
25. Selvarajan, K. (2022). Cloud-native architectures for high-throughput distributed data processing. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(5), 12538–12548.
26. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
27. Kondapalli, K. K., & Gunupudi, C. (2023). Artificial intelligence ethics and principles in public sector healthcare: A governance framework for responsible AI adoption in local and sub-national health services. Lex localis-Journal of Local Self-Government, 21 (S2), 61–81.
28. Gowda, M. K. S. (2024). Leveraging machine learning to enhance accuracy and efficiency in regulatory compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683-10692.
29. Sandhu, Y. S. (2024). Real time ETL optimization using change data capture incremental. International Journal of Emerging Trends in Engineering and Management Research, 9(3), 15711–15721.
30. Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yin, Q., & Yang, Z. (2022). Threats, attacks and defenses to federated learning: Issues, taxonomy and perspectives. Cybersecurity, 5, Article 4. https://doi.org/10.1186/s42400-021-00105-6
