Cloud Native Enterprise Intelligence for Intelligent SAP Governance Financial Assurance and AI Powered Compliance
Abstract
This paper introduces "Cloud Native Enterprise Intelligence" (CNEI), a novel conceptual framework designed to revolutionize intelligent SAP governance, financial assurance, and AI-powered compliance. In today's hyper-complex regulatory environment, traditional static auditing and decentralized governance mechanisms fail to address real-time operational risks. By leveraging microservices architectures, containerization, and advanced artificial intelligence, CNEI integrates directly with modern SAP ecosystems (such as SAP S/4HANA Cloud) to establish an automated, continuous, and autonomous oversight system. The framework embeds machine learning algorithms, natural language processing, and deep learning anomaly detection models directly into the financial data pipelines. This enables proactive risk mitigation, automated fraud detection, and continuous adherence to evolving international compliance standards like Sarbanes-Oxley (SOX) and GDPR. Ultimately, CNEI bridges the historical gap between enterprise resource planning (ERP) management and advanced cloud-native computing. The result is an adaptive system that minimizes financial leakage, eliminates human error in compliance auditing, and empowers executives with real-time, data-driven, and auditable insights. This study outlines the architecture, algorithmic approach, and operational benefits of CNEI, offering a strategic roadmap for next-generation digital corporate governance.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13292-13299 |
Published |
December 17, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Akinde Michael Ogunmolu (%2024). Cloud Native Enterprise Intelligence for Intelligent SAP Governance Financial Assurance and AI Powered Compliance. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13292-13299. https://doi.org/10.15662/IJSRAT.2024.0706015 |
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2. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
3. Denning, D. E. (1987). An intrusion-detection model. IEEE Transactions on Software Engineering, 13(2), 222–232.
4. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Journal of Privacy and Confidentiality, 7(3), 17-51.
5. 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.
6. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
7. 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.
8. 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.
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. Kotla, Mutha Ravi Tej (2022). Intelligent cloud-native banking: Leveraging machine learning for secure and scalable digital financial services. International Journal of Engineering and Technology Research, 7(1), 58–81. https://doi.org/10.34218/IJETR_07_01_005
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12. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
13. Prasanna Kumar Natta. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969. https://doi.org/10.15662/IJSRAT.2022.0506009
14. Wadhwa, R. (2023). Designing scalable enterprise systems using event-driven microservices and distributed data architecture for high-throughput business applications. International Journal of Computer Engineering and Technology, 14(3), 323-337.
15. Gollapudi, R. (2023). Operational drift and risk-bounded decision-making in production database systems. Journal of International Crisis and Risk Communication Research, 6(S3), 132–147.
16. Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9597–9604.
17. Gurram, S. K. (2023). Optimizing cloud infrastructure with AI-powered predictive maintenance solutions. International Journal of Science, Research and Technology (IJSRAT), 6(4), 10354–10363.
18. Samiuddin Mohammed (2022). AI-driven IT operations and AIOps enablement across hybrid cloud platforms. International Journal of Innovative Research in Science, Engineering and Technology, 11(3), 2826–2836. https://doi.org/10.15680/IJIRSET.2022.1103134
19. Joyce, S. (2023). Accelerating Enterprise SAP Workload Performance and Automation Using Microsoft Azure Center for SAP Solutions Through Cloud Native Architecture Intelligent Orchestration and Infrastructure as Code. IACSE-International Journal of Information Technology (IACSE-IJIT), 4(1), 8-30.
20. Mathew, A., & Alex, H. (2023, January). Hyper automation and augmented intelligence. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1230-1234). IEEE.
21. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
22. Gandhi, S. T. (2024). Enhancing Software Security with AI-Powered SDKs: A Framework for Proactive Threat Mitigation. International Journal of Computer Technology and Electronics Communication, 7(2), 8507-8514.
23. 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.
24. 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.
25. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
26. Gollapudi R. Backup integrity and recovery readiness assessment for high-availability databases. Computer Fraud and Security. 2024;23.
27. Kavuri, S. (2023). Machine learning approaches for security vulnerability detection in software testing. Computer Fraud & Security, 21-31.
28. Shewale, V. (2022). Third-Party and Supply Chain Risk in Oil & Gas. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9596.
29. Lanka, S. (2022). Building smarter security systems with AI: Inside Citrix analytics for security. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(4), 93-109.
30. Parupalli, A., & Pandya, S. (2022). Compliance-Driven Data Governance: A Survey on GDPR, and HIPAA in Cloud Databases. vol, 12, 828-836.
31. Kunadi, S. K. (2023). Entity resolution at scale: Advanced fuzzy matching techniques for company and project data. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8014–8022.
32. 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.
33. 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
34. 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
35. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
36. Gopinathan, V. R. (2024). Secure explainable AI on Databricks–SAP cloud for risk-sensitive healthcare analytics and swarm-based QoS control. International Journal of Engineering & Extended Technologies Research, 6(4), 8452-8459.