Integrating Gray Relational Analysis with AI-Augmented Automation and Ethical Governance in SAP Cloud: A Machine Learning Framework for Security, Risk, and Software Maintenance Optimization
DOI:
https://doi.org/10.15662/IJRAI.2023.0606015Keywords:
Gray Relational Analysis (GRA), AI-Augmented Automation, SAP Cloud, Machine Learning, Ethical Governance, Security Optimization, Risk Management, Software Maintenance, Predictive Analytics, Responsible AI, Cloud Compliance, Enterprise Automation, Decision IntelligenceAbstract
As enterprises increasingly migrate critical operations to SAP Cloud platforms, ensuring robust security, effective risk management, and efficient software maintenance has become vital. This study introduces a comprehensive framework that integrates Gray Relational Analysis (GRA) with AI-augmented automation and ethical governance principles to optimize system reliability and compliance at scale. By combining machine learning (ML) models with GRA-based multi-factor evaluation, the proposed framework enables precise correlation analysis between operational parameters, security indicators, and maintenance efficiency metrics within SAP Cloud environments. The approach supports data-driven prioritization of risk factors and automation of remediation strategies through intelligent policy orchestration. Ethical governance mechanisms—rooted in transparency, accountability, and fairness—are embedded to ensure that AI-driven decision-making aligns with global regulatory and corporate responsibility standards. Leveraging SAP-native technologies such as SAP AI Core, SAP GRC, and SAP Build Process Automation, the framework enhances anomaly detection, predictive maintenance, and compliance monitoring. Empirical results demonstrate that integrating GRA with AI automation improves accuracy in risk prediction, reduces system downtime, and strengthens ethical oversight in automated processes. This research contributes a scalable and explainable model for responsible AI governance in cloud ecosystems, advancing both the theoretical and practical understanding of secure, ethical, and intelligent enterprise automation.References
1. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2021). The evolution of software maintenance. Journal of Computer Science Applications and Information Technology, 6(1), 1–8. https://doi.org/10.15226/2474-9257/6/1/00150
2. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105–5111
3. Anbalagan, B., & Pasumarthi, A. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication, 5(4), 5423-5441.
4. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
5. Sudha, N., Kumar, S. S., Rengarajan, A., & Rao, K. B. (2021). Scrum Based Scaling Using Agile Method to Test Software Projects Using Artificial Neural Networks for Block Chain. Annals of the Romanian Society for Cell Biology, 25(4), 3711-3727.
6. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
7. Sugumar R., et.al IMPROVED PARTICLE SWARM OPTIMIZATION WITH DEEP LEARNING-BASED MUNICIPAL SOLID WASTE MANAGEMENT IN SMART CITIES, Revista de Gestao Social e Ambiental, V-17, I-4, 2023.
8. Sivaraju, P. S. (2023). Global Network Migrations & IPv4 Externalization: Balancing Scalability, Security, and Risk in Large-Scale Deployments. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS (ISCSITR-IJCA), 4(1), 7-34.
9. Cloud Security Alliance. (2021). Cloud Controls Matrix (CCM) 4.0.
10. Sridhar Kakulavaram. (2022). Life Insurance Customer Prediction and Sustainbility Analysis Using Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 390 –.Retrieved from https://ijisae.org/index.php/IJISAE/article/view/764
11. Ponnoju, S. C., Kotapati, V. B. R., & Mani, K. (2022). Enhancing Cloud Deployment Efficiency: A Novel Kubernetes-Starling Hybrid Model for Financial Applications. American Journal of Autonomous Systems and Robotics Engineering, 2, 203-240.
12. Kroll, J. A., et al. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705.
13. Kandula N (2023). Gray Relational Analysis of Tuberculosis Drug Interactions A Multi-Parameter Evaluation of Treatment Efficacy. J Comp Sci Appl Inform Technol. 8(2): 1-10.
14. 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.
15. Murugamani, C., Saravanakumar, S., Prabakaran, S., & Kalaiselvan, S. A. (2015). Needle insertion on soft tissue using set of dedicated complementarily constraints. Advances in Environmental Biology, 9(22 S3), 144-149.
16. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
17. Thambireddy, S., Bussu, V. R. R., & Joyce, S. (2023). Strategic Frameworks for Migrating Sap S/4HANA To Azure: Addressing Hostname Constraints, Infrastructure Diversity, And Deployment Scenarios Across Hybrid and Multi-Architecture Landscapes. Journal ID, 9471, 1297. https://www.researchgate.net/publication/396446597_Strategic_Frameworks_for_Migrating_Sap_S4HANA_To_Azure_Addressing_Hostname_Constraints_Infrastructure_Diversity_And_Deployment_Scenarios_Across_Hybrid_and_Multi-Architecture_Landscapes
18. Girdhar, P., Virmani, D., & Saravana Kumar, S. (2019). A hybrid fuzzy framework for face detection and recognition using behavioral traits. Journal of Statistics and Management Systems, 22(2), 271-287.
19. Anand, L., & Neelanarayanan, V. (2019). Feature Selection for Liver Disease using Particle Swarm Optimization Algorithm. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 6434-6439.
20. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal ofComputer Science Applications and Information Technology, 5(1), 1-8.
21. R., Sugumar (2023). Real-time Migration Risk Analysis Model for Improved Immigrant Development Using Psychological Factors. Migration Letters 20 (4):33-42.
22. Zhou, J., & Kapoor, G. (2020). Ethical principles of AI: A comprehensive survey. AI Ethics Review, 5(2), 141–155.
