AI-Driven Cloud-Native Enterprise Architecture for Secure SAP Platforms: Intelligent Data Integration and Autonomous Digital Transformation

Prema Veerapaneni; Prema Veerapaneni

doi:10.15662/IJSRAT.2026.0902003

AI-Driven Cloud-Native Enterprise Architecture for Secure SAP Platforms: Intelligent Data Integration and Autonomous Digital Transformation

Prema Veerapaneni

Abstract

Modern enterprises increasingly depend on advanced digital infrastructures to support large-scale business operations, global data management, and real-time decision-making. Enterprise Resource Planning (ERP) platforms such as SAP play a critical role in managing financial systems, supply chain operations, procurement processes, and customer engagement. However, traditional enterprise architectures face significant limitations when handling modern digital workloads, including scalability challenges, cybersecurity threats, integration complexity, and real-time analytics requirements. The rapid evolution of cloud computing and artificial intelligence technologies has created opportunities for developing intelligent enterprise architectures capable of supporting secure and autonomous digital transformation.

Cloud-native computing environments provide scalable infrastructure, container-based deployment models, and microservices architectures that enable organizations to build flexible and resilient digital ecosystems. When integrated with artificial intelligence technologies, these architectures can deliver predictive analytics, intelligent data integration, automated system optimization, and proactive cybersecurity monitoring. AI-driven enterprise systems are capable of continuously analyzing operational data, identifying anomalies, and dynamically adjusting system performance to maintain efficiency and security.

This research presents an AI-driven cloud-native enterprise architecture designed specifically for secure SAP platforms. The proposed framework integrates intelligent data pipelines, machine learning–based analytics engines, autonomous infrastructure orchestration, and zero-trust cybersecurity models. The architecture enables real-time data integration across enterprise systems while maintaining high levels of security, governance, and operational reliability.

The study explores how artificial intelligence technologies can enhance SAP ecosystem management through predictive analytics, automated resource allocation, and intelligent threat detection. Furthermore, the research highlights the importance of cloud-native technologies such as container orchestration, microservices, and distributed computing frameworks in enabling scalable enterprise infrastructures. The findings demonstrate that AI-driven enterprise architectures can significantly improve system resilience, operational efficiency, and cybersecurity readiness while enabling organizations to accelerate digital transformation initiatives.

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2026.0902003
Pages	423-434
Published	March 7, 2026
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Prema Veerapaneni (%2026). AI-Driven Cloud-Native Enterprise Architecture for Secure SAP Platforms: Intelligent Data Integration and Autonomous Digital Transformation. International Journal of Science, Research and Technology , Vol. 9 No. 2 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 423-434. https://doi.org/10.15662/IJSRAT.2026.0902003

References

1. Thirumal, L., & Umasankar, P., Precision muscle segmentation and classification for knee osteoarthritis with dual attention networks and GAO-optimized CNN, Biomedical Signal Processing and Control, vol. 111, 108244, 2026.
2. Gopinathan, V. R., Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking, International Journal of Computer Technology and Electronics Communication, vol. 7, no. 6, pp. 9837-9845, 2024.
3. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI and generative AI driven automation for multi cloud and hybrid cloud architectures enhancing security performance and operational efficiency, in Proc. IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), pp. 784–793, IEEE, 2025. https://doi.org/10.1109/CCWC62904.2025.10903928
4. Poornachandar, T., Latha, A., Nisha, K., Revathi, K., & Sathishkumar, V. E., Cloud-Based Extreme Learning Machines for Mining Waste Detoxification Efficiency, in 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 1348-1353, IEEE, Sept. 2025.
5. Jagadeesh, S., & Sugumar, R., Optimal knowledge extraction system based on GSA and AANN, International Journal of Control Theory and Applications, vol. 10, no. 12, pp. 153–162, 2017.
6. Ratra, K. K., Seth, D. K., & Uppuluri, S., Energy efficient microservices architecture for large scale e commerce platforms, in Proc. 2025 IEEE Conference on Technologies for Sustainability (SusTech), IEEE, 2025. (Conference paper listing via publication record)
7. Suddala, V. R. A. K., FADL-DP and CNN-GRU Driven Cloud Framework for Secure Healthcare E-Commerce Platform, in 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pp. 991-996, IEEE, Nov. 2025.
8. Ravi Kumar Ireddy, AI Driven Predictive Vulnerability Intelligence for Cloud-Native Ecosystems, International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), vol. 9, no. 2, pp. 894-903, 2023. https://doi.org/10.32628/CSEIT2342438
9. Kumar, R., Mohammed, A. S., & Murthy, C. J., Cash Management Forecasting Using Long Short-Term Memory (LSTM) Networks, American Journal of Cognitive Computing and AI Systems, vol. 7, pp. 123-155, 2023.
10. Ande, B. R., Leveraging Azure OpenAI and Cognitive Services for Enterprise Automation: Streamlining Operations and Enhancing Decision-Making, J. Inf. Syst. Eng. Manag, vol. 9, no. 4s, pp. 209-216, 2024.
11. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI driven hybrid edge cloud architecture for real time big data analytics and scalable communication in retail supply chains, in Proc. IEEE SoutheastCon 2025, IEEE, 2025. (Indexed conference paper)
12. Ambati, K. C., An event-driven architecture for autonomous supply chain risk detection and decision automation, International Journal of Computer Technology and Electronics Communication (IJCTEC), vol. 8, no. 1, pp. 1202–1211, 2025.
13. Thumala, S. R., Mane, V., Patil, T., Tambe, P., & Inamdar, C., Full Stack Video Conferencing App using TypeScript and NextJS, in 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 1285-1291, IEEE, June 2025.
14. Anumula, S. R., Intelligent Microservices in Regulated Industries: Crew Scheduling and Retail Claims, Journal of Computer Science and Technology Studies, vol. 7, no. 6, pp. 1084-1089, 2025.
15. Konda, S. K., Sustainable energy optimization through cloud-native building automation and predictive analytics integration, World Journal of Advanced Research and Reviews, vol. 24, no. 3, pp. 3619–3628, 2024. https://doi.org/10.30574/wjarr.2024.24.3.3803
16. Suddala, V. R. A. K., FADL-DP and CNN-GRU Driven Cloud Framework for Secure Healthcare E-Commerce Platform, in 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), pp. 991-996, IEEE, Nov. 2025.
17. Panda, S. S., Delivering Scalable Cloud Services in China: Microsoft and 21Vianet Collaboration, International Journal of Advanced Research in Computer Science & Technology (IJARCST), vol. 7, no. 6, pp. 11325-11333, 2024.
18. Karnam, A., Rolling Upgrades, Zero Downtime: Modernizing SAP Infrastructure with Intelligent Automation, International Journal of Engineering & Extended Technologies Research, vol. 7, no. 6, pp. 11036–11045, 2025. https://doi.org/10.15662/IJEETR.2025.0706022
19. Kumar, R., Mohammed, A. S., & Murthy, C. J., Cash Management Forecasting Using Long Short-Term Memory (LSTM) Networks, American Journal of Cognitive Computing and AI Systems, vol. 7, pp. 123-155, 2023.
20. Kubam, C. S., Duggirala, J., VishnubhaiSheta, S., Mogali, S. K., Lakhina, U., & Kaur, H., AI-Driven Credit Risk Assessment in Digital Finance Using Feature Optimization Deep Q Learning, in 2025 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 210-216, IEEE, Nov. 2025.
21. Gowda, M. K. S., Comprehensive Audit Data Pipeline Architecture-Strategies for Modern Banking Audit, Compliance and Risk Management, International Journal of Advanced Research in Computer Science & Technology (IJARCST), vol. 8, no. 1, pp. 11590-11597, 2025.
22. Ambati, K. C., An event-driven architecture for autonomous supply chain risk detection and decision automation, International Journal of Computer Technology and Electronics Communication (IJCTEC), vol. 8, no. 1, pp. 1202–1211, 2025.
23. Adepu, R. (2025). Morphic Cryptographic Orchestration and Tokenization Strategies for Advanced Cyber Defense. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(1), 542-551.
24. Namdeo, A. (2025). Zero-shot transfer learning for cross-industry BI models. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(4), 11119–11128. https://doi.org/10.15680/IJCTECE.2025.0804016
25. Nunna, R. (2024). Cloud security with OWASP and Azure RBAC. International Journal for Multidisciplinary Research (IJFMR), 6(4), 1–6.
26. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
27. Kandula, S. T. R. (2025, July). Comparison and Performance Assessment of Intelligent ML Models for Forecasting Cardiovascular Disease Risks in Healthcare. In 2025 International Conference on Sensors and Related Networks (SENNET) Special Focus on Digital Healthcare (64220) (pp. 1-6). IEEE.
28. Katta, T. B. (2023). Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration. International Journal of Science, Research and Technology, 6(6), 11080-11084.
29. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
30. Kavuri, S. (2025). Critical Review of Software Testing Problems in the Current Decade. IJSAT-International Journal on Science and Technology, 16(2).
31. Shewale, V. (2024). Ransomware Resilience for Pipeline Operators. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7863-7868.
32. Parasa, M. (2023). A structured recruitment analytics framework for candidate screening and talent pool utilization in SAP SuccessFactors Recruiting. Global Journal of Engineering and Technology, 2(11), 29–39. https://gsarpublishers.com/gjet-vol-2-issue-11-november-2023/
33. Pothuri, M. K. (2026). Predicting Very High-Cost Claimants Using Symmetry ETG/PEG Feature Engineering Combined with Advanced Machine Learning. International Journal of AI, BigData, Computational and Management Studies, 352-357.
34. Panyala, V. R. (2024). Pioneering architectures for resilient multi-region cloud platforms supporting mission-critical internet services. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 1041–1058. https://doi.org/10.15662/410
35. Adepu, G. (2023). Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7744-7748.
36. Sarabu, V. B. (2018). Architecting Financially Compliant Enterprise Point-of-Sale Systems: A Scalable Data Integrity and Revenue Recognition Framework for Global Retail Platforms. International Journal of Computer Technology and Electronics Communication, 1(2), 329-341.
37. Subramanyam, S. P. (2025). AI-driven CI/CD pipeline automation for secure .NET applications in Azure Kubernetes Services. International Journal of Science, Research and Technology (IJSRAT), 8(1), 13505–13512. https://doi.org/10.15662/IJSRAT.2025.0801003
38. Seth, D. K., Ratra, K. K., & Sundareswaran, A. P., AI driven hybrid edge cloud architecture for real time big data analytics and scalable communication in retail supply chains, in Proc. IEEE SoutheastCon 2025, IEEE, 2025. (Indexed conference paper)