Articles Open Access
Transparent Enterprise Risk Prediction in Cloud Environments Using Explainable AI and Multidimensional Risk Scoring
Department of Computer Science, Faculty of Computer and Information Technology, Jordan University of Science and Technology, Jordan
Abstract
Enterprise cloud environments support critical business operations, distributed applications, financial transactions, and sensitive data management. However, their increasing complexity introduces risks associated with cybersecurity threats, regulatory noncompliance, operational disruptions, data exposure, and resource misconfiguration. Traditional risk assessment approaches frequently rely on static rules, isolated indicators, and opaque machine learning models, limiting their ability to identify emerging threats and explain predictions effectively. This research proposes a transparent enterprise risk prediction framework that integrates Explainable Artificial Intelligence (XAI) with multidimensional risk scoring to improve cloud security, operational reliability, and governance. The proposed framework combines security logs, identity and access records, configuration assessments, vulnerability information, workload metrics, and compliance indicators to construct a comprehensive enterprise risk profile. Machine learning models identify potential risk patterns, while explainability techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), clarify the contribution of individual features to each prediction. A multidimensional scoring mechanism evaluates security, operational, compliance, financial, and data privacy risks through normalized indicators and configurable organizational weights. The framework also incorporates continuous monitoring, risk prioritization, and human-supervised mitigation recommendations. Its evaluation methodology considers predictive performance, interpretability, calibration, detection latency, and computational overhead. By connecting predictive intelligence with understandable explanations and actionable risk scores, the proposed approach aims to strengthen enterprise decision-making, improve accountability, and support proactive risk management across dynamic cloud infrastructures.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 7 |
| Number | 6 |
| Pages | 13646-13654 |
| Published | 2023-12-14 |
| Issue | Vol. 7 No. 6 (2023): International Research Journal of Innovative Engineering (IRJIE) |
References
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32. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
33. Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33-48.
34. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
35. Padmanabham, S. (2022). Secure enterprise architecture for pharmacy benefit management platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5381-5386.
36. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
37. Nawrocki, P., & Osypanka, P. (2021). Cloud resource demand prediction using machine learning in the context of QoS parameters. Journal of Grid Computing, 19, Article 20. https://doi.org/10.1007/s10723-021-09561-3
2. Neela, S. (2023). Resilient middleware ecosystems: Integrating fault tolerance, recovery-oriented design, and observability for real-time enterprise integration. International Journal of Emerging Research in Engineering and Technology, 4(1), 186–189. https://doi.org/10.63282/3050-922X.IJERET-V4I1P120
3. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
4. Gurram, S. (2023). Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9028-9036.
5. Chinthamani, R. (2022). Event-driven microservices for scalable retail fulfillment optimization. International Journal of Research and Applied Innovations, 5(2), 6790–6795.
6. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
7. Kannan, K. S., Sunitha, G., Deepa, S. N., Babu, D. V., & Avanija, J. (2022). A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Computers and Electrical Engineering, 102, 108225.
8. Kalabhavi, V. (2023). Enterprise quote management using AI for product configuration, pricing and approval workflows in SAP CPQ. International Journal of Science, Research and Technology, 6(1), 9231-9242.
9. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
10. Polamarasetty, V. K. (2023). Modern enterprise HR technology through Workday-based benefits and absence management. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6908-6913.
11. 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.
12. Yadavalli, V. R. (2023). Cutover intelligence mesh: A dependency-aware architecture for enterprise cloud ERP migration. International Journal of Future Innovative Science and Technology, 6(4), 11015–11031.
13. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
14. Madhan, E. S., Kannan, K. S., Rani, P. S., Rani, J. V., & Anguraj, D. K. (2021). A distributed submerged object detection and classification enhancement with deep learning. Distrib. Parallel Databases, 41, 1-17.
15. Gadige, C. D. (2023). API-led connectivity in enterprise CRM: A reference architecture for Salesforce and heterogeneous enterprise systems. International Research Journal of Innovative Engineering, 7(2), 12125–12135.
16. Kopparthi, G. S. (2022). AI in healthcare: Unlocking the potential of predictive analytics. Journal of Computational Analysis and Applications, 30(2), 1183–1193. https://eudoxuspress.com/index.php/pub/article/view/5508
17. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
18. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9092-9101.
19. Ponugoti, M. (2022). Integrating API-first architecture with experience-centric design for seamless insurance platform modernization. International Journal of Humanities and Information Technology, 4(01-03), 117-136.
20. Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426-11432.
21. Gunasekaran, R. M. N. (2023). Operational Challenges in Basel IV Credit Risk Compliance. The Eastasouth Journal of Information System and Computer Science, 1(01), 169-178.
22. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
23. 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.
24. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
25. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
26. Kundavaram, R. R., Bandhela, R. R., & Onteddu, A. R. (2022). AI-driven predictive modeling in healthcare: A data science perspective on U.S. healthcare data. South Eastern European Journal of Public Health. https://doi.org/10.70135/seejph.vi.6691
27. Shaik, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985-9996.
28. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162-8169.
29. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
30. Kalakoti, M. K. R. (2022). Dependency-aware migration and resilience validation of stateful applications to enterprise cloud. International Research Journal of Innovative Engineering, 6(2), 9997–10005.
31. Nittur, K., Chippagiri, S., & Zhidko, M. (2020). Evolving web application development frameworks: A survey of Ruby on Rails, Python, and cloud-based architectures. International Journal of New Media Studies, 7(1), 28–34. https://ijnms.com/index.php/ijnms/article/view/296
32. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
33. Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33-48.
34. Vimal Raja, G. (2022). Leveraging machine learning for real-time short-term snowfall forecasting using multisource atmospheric and terrain data integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
35. Padmanabham, S. (2022). Secure enterprise architecture for pharmacy benefit management platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5381-5386.
36. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
37. Nawrocki, P., & Osypanka, P. (2021). Cloud resource demand prediction using machine learning in the context of QoS parameters. Journal of Grid Computing, 19, Article 20. https://doi.org/10.1007/s10723-021-09561-3
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