Machine Learning–Enabled Security and Governance Framework for SAP-Based Cloud-Native Enterprise Systems
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706025Keywords:
Machine Learning, SAP Security, Cloud-Native Architecture, Enterprise Governance, Cybersecurity, Intelligent Threat Detection, Digital TransformationAbstract
The rapid digital transformation of enterprises has accelerated the adoption of cloud-native technologies and SAP-based enterprise platforms. Organizations increasingly rely on SAP systems deployed on cloud infrastructures to manage critical business operations such as finance, supply chain management, human resources, and customer engagement. However, the growing complexity of cloud-native architectures introduces significant challenges in ensuring security, governance, compliance, and risk management. Traditional rule-based security mechanisms often struggle to detect sophisticated cyber threats and anomalous activities in dynamic enterprise environments. This research proposes a Machine Learning–Enabled Security and Governance Framework designed specifically for SAP-based cloud-native enterprise systems. The framework integrates machine learning models with cloud-native security mechanisms to enable intelligent threat detection, automated governance monitoring, compliance enforcement, and predictive risk analysis. The architecture incorporates multiple layers including data acquisition, machine learning analytics, security orchestration, and governance automation. By leveraging anomaly detection algorithms, predictive analytics, and real-time monitoring, the proposed framework enhances enterprise resilience against cyber threats while maintaining regulatory compliance. The study demonstrates how machine learning techniques can improve visibility across SAP environments, detect insider threats, prevent unauthorized access, and optimize governance policies. The proposed framework provides a scalable and adaptive security model that aligns with modern enterprise digital transformation strategies and cloud-native architectures.
References
1. Sampath Kumar Konda. (2024). Distributed AI infrastructure orchestration: A hyperscale multi-cloud framework for geographic load balancing with renewable energy optimization. International Journal of Scientific Research in Science Engineering and Technology, 11(4), 522–533. https://doi.org/10.32628/IJSRSET242438
2. Ponnoju, S. C., & Paul, D. (2023). Hybridizing Apache Camel and Spring Boot for next-generation microservices in financial data integration. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 209–244.
3. Gangina, P. (2023). Edge computing architectures for IoT data aggregation in industrial manufacturing. International Journal of Humanities and Information Technology, 5(01), 48–67.
4. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IoT-based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
5. Vijayaboopathy, V., Yakkanti, B., & Surampudi, Y. (2023). Agile-driven quality assurance framework using ScalaTest and JUnit for scalable big data applications. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 245–285.
6. 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.
7. Sheta, S. V. (2022). An overview of object-oriented programming (OOP) and its impact on software design. Educational Administration: Theory and Practice, 28(4), 409–419.
8. Sanepalli, U. R. (2023). Cognitive goal-driven financial infrastructure: A cloud-native AI-orchestrated architecture for investment trade settlement and risk management systems. World Journal of Advanced Research and Reviews, 19(1), 1659–1667. https://doi.org/10.30574/wjarr.2023.19.1.1358
9. Sarraf, G., & Swetha, M. S. (2019, December). Intrusion prediction and detection with deep sequence modeling. In International Symposium on Security in Computing and Communication (pp. 11–25). Singapore: Springer Singapore.
10. Anitha, K., Vijayakumar, R., Jeslin, J. G., Elangovan, K., Jagadeeswaran, M., & Srinivasan, C. (2024, March). Marine propulsion health monitoring: Integrating neural networks and IoT sensor fusion in predictive maintenance. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 1–6). IEEE.
11. Indurthy, V. S. K. (2024). Streamlining ROP Metrics and Reporting through Cloud Migration and Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10703-10712.
12. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
13. Anumula, S. R. (2024). Ethical design frameworks for automated decision-making platforms. International Journal of Future Innovative Science and Technology, 7(1), 12035–12047.
14. 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.
15. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
16. 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.
17. Ravi Kumar Ireddy. (2023). AI driven predictive vulnerability intelligence for cloud-native ecosystems. International Journal of Scientific Research in Computer Science Engineering and Information Technology (IJSRCSEIT), 9(2), 894–903. https://doi.org/10.32628/CSEIT2342438
18. 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.
19. Ponnoju, S. C., & Paul, D. (2023). Hybridizing Apache Camel and Spring Boot for next-generation microservices in financial data integration. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 209–244.
20. Sampath Kumar Konda. (2024). Distributed AI infrastructure orchestration: A hyperscale multi-cloud framework for geographic load balancing with renewable energy optimization. International Journal of Scientific Research in Science Engineering and Technology, 11(4), 522–533. https://doi.org/10.32628/IJSRSET242438
21. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
22. Sarraf, G., & Swetha, M. S. (2019, December). Intrusion prediction and detection with deep sequence modeling. In International Symposium on Security in Computing and Communication (pp. 11–25). Singapore: Springer Singapore.
23. Vijayaboopathy, V., Yakkanti, B., & Surampudi, Y. (2023). Agile-driven quality assurance framework using ScalaTest and JUnit for scalable big data applications. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 245–285.
24. Gopinathan, V. R. (2024). AI-driven customer support automation: A hybrid human–machine collaboration model for real-time service delivery. International Journal of Technology Management and Humanities, 10(01), 67–83.
25. Potel, R. (2022). AI-Driven Security Graphs for Real-Time Breach Containment in Hybrid Cloud Environments. International Journal of AI, BigData, Computational and Management Studies, 3(4), 123-131.
26. 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.
27. Bheemisetty, N. (2024). From Fragmentation to Agility: Nautilus Architecture for Risk Management Modernization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10673-10682.
28. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
29. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IoT-based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
30. Ambalakannu, M. (2024). Driving Operational Efficiency and Clinical Insights via Unified Care Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10693-10702.
31. Anand, P. V., & Anand, L. (2023, December). An Enhanced Breast Cancer Diagnosis using RESNET50. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-5). IEEE.
32. Gangina, P. (2023). Edge computing architectures for IoT data aggregation in industrial manufacturing. International Journal of Humanities and Information Technology, 5(01), 48–67.
33. Anumula, S. R. (2024). Ethical design frameworks for automated decision-making platforms. International Journal of Future Innovative Science and Technology, 7(1), 12035–12047.
34. Sheta, S. V. (2022). An overview of object-oriented programming (OOP) and its impact on software design. Educational Administration: Theory and Practice, 28(4), 409–419.
35. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
36. Anitha, K., Vijayakumar, R., Jeslin, J. G., Elangovan, K., Jagadeeswaran, M., & Srinivasan, C. (2024, March). Marine propulsion health monitoring: Integrating neural networks and IoT sensor fusion in predictive maintenance. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 1–6). IEEE.
37. Kesavan, E., & Srinivasulu, S. (2024). Security challenges in smart IoT systems and their solutions. Journal of Information Technology, 14(2). https://doi.org/10.26634/jit.14.2.22000
