Articles Open Access
Enterprise Scale AI Architecture for Secure Cloud Migration Intelligent APIs and Autonomous Financial Platforms
DevOps Engineer, Sopra Steria, France
Abstract
The rapid evolution of financial technologies demands a paradigm shift from legacy infrastructure to highly secure, scalable, and intelligent cloud environments. This paper proposes a novel Enterprise Scale AI Architecture specifically engineered for Secure Cloud Migration, Intelligent APIs, and Autonomous Financial Platforms. As financial institutions face unprecedented data volumes and sophisticated cyber threats, traditional migration frameworks fall short. Our proposed architecture integrates real-time machine learning, zero-trust security paradigms, and self-healing autonomous financial agents to ensure zero-downtime migrations and intelligent API orchestration. By leveraging advanced telemetry and distributed ledger validation, the framework automates compliance monitoring and predictive anomaly detection during the data transit phase. Furthermore, the architecture introduces decentralized autonomous components that optimize high-frequency financial operations and asset allocation without human intervention. The paper evaluates the system’s performance across multi-cloud deployments, demonstrating a 40% reduction in latency for intelligent API routing and a 95% improvement in proactive threat mitigation compared to heuristic-based models. Ultimately, this research provides a scalable, compliant, and highly resilient blueprint for global financial institutions seeking to achieve digital transformation while maintaining rigid security standards and driving operational efficiency through autonomous intelligence.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 6 |
| Number | 6 |
| Pages | 11418-11425 |
| Published | 2022-11-08 |
| Issue | Vol. 6 No. 6 (2022): International Research Journal of Innovative Engineering (IRJIE) |
References
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2. Berson, A., & Smith, S. J. (1997). Data warehousing, data mining, and OLAP. McGraw-Hill.
3. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25(6), 599–616.
4. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.
5. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
6. Alex Roney Mathew. (2019). Malware analysis of API calls using FPGA hardware level security. International Journal for Research in Applied Science & Engineering Technology, 7(3), 898–900.
7. Polamreddy, V. R. (2021). Engineering Reversible Enterprise Data Migrations: A Phased Rollout Framework for Financially Critical Retail Platforms. International Journal of Computer Technology and Electronics Communication, 4(2), 3414-3427.
8. Kotla, Mutha Ravi Tej (2021). Machine learning-based predictive observability for enterprise integration platforms. Journal of Computer Engineering and Technology, 4(3), 1–24. https://doi.org/10.34218/JCET_04_03_001
9. Navandar, P. (2022). Adaptive SAP security control framework for ML driven anomaly detection, role based access hardening, and continuous compliance monitoring in SAP S/4HANA environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(3), 4939–4952. https://doi.org/10.15662/IJEETR.2022.0403005
10. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
11. Anand, L., & Syed Ibrahim, S. P. (2018). HANN: a hybrid model for liver syndrome classification by feature assortment optimization. Journal of medical systems, 42(11), 211.
12. 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
13. Parasa, M. (2020). Control-mapped AI governance for high-risk HR decisions in SAP SuccessFactors: Audit-ready metrics for recruiting, performance calibration, and internal mobility. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 12(2), 153–168. https://doi.org/10.18090/samriddhi.v12i02.15
14. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
15. Gopisetty, S. (2022). Teaching Machines to Walk the Tightrope: Using AI Digital Twins to Balance Process Speed and Regulatory Safety in Cloud-Banked Finance. Journal of Scientific and Engineering Research, 9(8), 183-226.
16. Manda, P. (2022). Implementing hybrid cloud architectures with Oracle and AWS: Lessons from mission-critical database migrations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7111–7122.
17. Mathew, A. (2021). Obfuscation Techniques for Magecart Detection and Prevention. International Journal of Computer Science and Mobile Computing, 10(2), 39-44.
18. Fielding, R. T. (2000). Architectural styles and the design of network-based software architectures (Doctoral dissertation). University of California, Irvine.
19. Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud computing and grid computing 360-degree view. Proceedings of the 2008 Grid Computing Environments Workshop, 1–10.
20. He, W., Da Xu, L., & Xie, M. (2014). Transforming business intelligence to big data analytics: The evolution and future trends. Industrial Management & Data Systems, 114(9), 1410–1423.
21. Hohpe, G., & Woolf, B. (2003). Enterprise integration patterns: Designing, building, and deploying messaging solutions. Addison-Wesley.
22. Inmon, W. H. (2005). Building the data warehouse (4th ed.). John Wiley & Sons.
23. Kambhampati, S. (2008). Model-based approaches for data integration. IEEE Intelligent Systems, 23(5), 78–81.
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