Cyber-Resilient AI Architecture for SAP Digital Banking on AWS Enabling Real-Time Predictive Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2024.0605008Keywords:
Cyber-Resilient Architecture, SAP Digital Banking, AWS Cloud, Artificial Intelligence, Real-Time Predictive Intelligence, Automated Cloud Resource Optimization, Cyber Defense, Digital Transformation, Enterprise Cloud Security, Large-Scale Technology SystemsAbstract
This paper presents an AI-driven, cyber-resilient architecture for SAP-based digital banking platforms deployed on AWS, designed to address the growing challenges of security, scalability, and operational efficiency in large-scale financial systems. The proposed architecture integrates real-time predictive intelligence and advanced analytics to proactively detect threats, anticipate system anomalies, and enhance cyber defense capabilities across SAP landscapes. Leveraging machine learning and deep learning models, the framework supports intelligent decision-making while enabling continuous monitoring and adaptive response to evolving risk scenarios. Automated cloud resource optimization mechanisms are incorporated to dynamically manage compute, storage, and network resources, ensuring high availability, cost efficiency, and performance resilience. The architecture also emphasizes cross-functional integration across security, cloud operations, and business teams, supporting multi-year digital transformation initiatives. By transitioning from traditional perimeter-based protection to an intelligent, predictive cyber defense model, the proposed approach demonstrates significant impact on the reliability, security, and scalability of modern digital banking ecosystems.References
1. Basel Committee on Banking Supervision. (2018). Principles for operational resilience. BIS.
2. Malarkodi, K. P., Sugumar, R., Baswaraj, D., Hasan, A., & Kousalya, A. (2023, March). Cyber Physical Systems: Security Technologies, Application and Defense. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2536-2546). IEEE.
3. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
4. Kabade, S., Sharma, A., & Kagalkar, A. (2024). Securing Pension Systems with AI-Driven Risk Analytics and Cloud-Native Machine Learning Architectures. International Journal of Emerging Research in Engineering and Technology, 5(2), 52-64.
5. Kusumba, S. (2023). A Unified Data Strategy and Architecture for Financial Mastery: AI, Cloud, and Business Intelligence in Healthcare. International Journal of Computer Technology and Electronics Communication, 6(3), 6974-6981.
6. Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602–613.
7. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235–249.
8. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–58.
9. Ghafir, I., et al. (2016). A machine learning approach for detecting cyber attacks on industrial control systems. Computer Networks, 122, 143–157.
10. 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.
11. 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.
12. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
13. Meka, S. (2022). Streamlining Financial Operations: Developing Multi-Interface Contract Transfer Systems for Efficiency and Security. International Journal of Computer Technology and Electronics Communication, 5(2), 4821-4829
14. Vijayaboopathy, V., Kalyanasundaram, P. D., & Surampudi, Y. (2022). Optimizing Cloud Resources through Automated Frameworks: Impact on Large-Scale Technology Projects. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 2, 168-203.
15. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
16. Gonzalez, H., et al. (2016). Big data analytics for predictive cyber security. IEEE Cloud Computing, 3(1), 64–71.
17. Gupta, A., & Kumar, V. (2014). ERP security: Risks and mitigation in SAP environments. Journal of Information Security, 5(2), 87–99.
18. Rajurkar, P. (2021). Deep Learning Models for Predicting Effluent Quality Under Variable Industrial Load Conditions. International Journal of Research and Applied Innovations, 4(5), 5826-5832.
19. Gujjala, Praveen Kumar Reddy. (2023). Autonomous Healthcare Diagnostics : A MultiModal AI Framework Using AWS SageMaker, Lambda, and Deep Learning Orchestration for Real-Time Medical Image Analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 760-772. 10.32628/CSEIT23564527.
20. Heinlein, J., & Mühlhäuser, M. (2016). Real time event processing for cyber security analytics. Computers & Security, 59, 93–113.
21. Kumar, R., Christadoss, J., & Soni, V. K. (2024). Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 351-366.
22. Sudhakara Reddy Peram, Praveen Kumar Kanumarlapudi, Sridhar Reddy Kakulavaram. (2023). Cypress Performance Insights: Predicting UI Test Execution Time Using Complexity Metrics. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 6(1), 167-190.
23. Kumar, R. K. (2023). AI‑integrated cloud‑native management model for security‑focused banking and network transformation projects. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9321–9329. https://doi.org/10.15662/IJRPETM.2023.0605006
24. Nagarajan, G. (2024). Cloud-Integrated AI Models for Enhanced Financial Compliance and Audit Automation in SAP with Secure Firewall Protection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(1), 9692-9699.
25. Venkatachalam, D., Paul, D., & Selvaraj, A. (2022). AI/ML powered predictive analytics in cloud-based enterprise systems: A framework for scalable data-driven decision making. Journal of Artificial Intelligence Research, 2(2), 142–182.
26. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
27. Chandra Sekhar Oleti, " Real-Time Feature Engineering and Model Serving Architecture using Databricks Delta Live Tables" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.746-758, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT23906203
28. Jones, A., et al. (2013). Security information and event management (SIEM) for financial services. Journal of Financial Crime, 20(4), 444–457.
29. 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.
30. 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.
31. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
32. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
33. Rahman, T., Islam, M. M., Zerine, I., Pranto, M. R. H., & Akter, M. (2023). Artificial Intelligence and Business Analytics for Sustainable Tourism: Enhancing Environmental and Economic Resilience in the US Industry. Journal of Primeasia, 4(1), 1-12.





