Secure Explainable Machine Learning Architecture for Smart Enterprise Systems and Real Time Risk Governance
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805033Keywords:
Explainable AI, Machine Learning Security, Smart Enterprise Systems, Real-Time Risk Governance, Artificial Intelligence, Federated Learning, Blockchain Security, Enterprise Risk Management, Cybersecurity Analytics, Transparent AI, Risk Prediction, Secure ArchitectureAbstract
The rapid digital transformation of enterprise systems has increased the dependence on Artificial Intelligence (AI) and Machine Learning (ML) for decision-making, automation, cybersecurity, and operational risk management. However, the widespread deployment of intelligent systems introduces critical concerns regarding data privacy, model transparency, adversarial attacks, and governance compliance. This research proposes a Secure Explainable Machine Learning Architecture (SEMLA) designed for smart enterprise systems and real-time risk governance. The proposed architecture integrates explainable AI (XAI), secure data processing, federated learning, blockchain-enabled audit trails, and real-time risk analytics to ensure trustworthy and transparent decision-making. The framework focuses on improving model interpretability while maintaining security, scalability, and compliance with organizational regulations. Furthermore, the architecture supports continuous monitoring and adaptive risk governance by utilizing automated anomaly detection and explainability dashboards. The study evaluates the effectiveness of the proposed model through enterprise risk scenarios including fraud detection, cybersecurity threat monitoring, and financial risk assessment. Results indicate that integrating explainability and security mechanisms significantly enhances stakeholder trust, decision accuracy, and governance efficiency. The proposed architecture contributes to modern enterprise intelligence systems by providing a reliable and transparent ML ecosystem capable of supporting secure automation and accountable AI-driven governance in dynamic business environments.
References
1. Panyala, V. R. (2024). Designing self-healing cloud architectures for mission-critical distributed systems. International Journal of Science, Research and Technology, 7(2), 11717–11721.
2. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
3. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Artificial Intelligence & Machine Learning, 2(1), 356–370. https://doi.org/10.34218/IJAIML_02_01_029
4. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.
5. Narayanan, S. (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
6. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8452-8459.
7. Kunadi, S. K. (2024). Improving Data Quality and Deduplication Using Similarity Scoring and Confidence Models. International Journal of Computer Technology and Electronics Communication, 7(4), 9200-9211.
8. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
9. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
10. Sarabu, V. B. (2024). Architecting controlled international platform rollouts: Data governance, validation, and risk mitigation in retail modernization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 306–328.
11. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
12. Mallireddy, S. (2023). Servicenow & Generative AI: Improving Infant Mortality Rate. International Journal of Computer Technology and Electronics Communication, 6(5), 1-7.
13. Adepu, R. (2024). Secure cloud migration strategies for enterprise data center modernization. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 239–258.
14. Devineni, A. (2025). Cognitive Load Reduction in On-Call Rotations via Predictive Alert Severity Scoring Using Machine Learning in Financial Cloud Operations. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 268-273.
15. Kasireddy, J. R. (2025). Leveraging big data analytics for enhanced commercial vehicle safety: FMCSA's data engineering journey. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 3203–3222. https://doi.org/10.32628/CSEIT25112796
16. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.
17. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
18. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
19. Joyce, S. (2024). Automated enterprise system reliability: Integrating AI-driven monitoring with cloud-based SAP deployment pipelines. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10474–10482. https://doi.org/10.15662/IJRAI.2024.0702010
20. Adepu, G. (2023). Intelligent digital government platforms: Leveraging machine learning and cloud architecture for social service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 75–92.
21. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.
