Seamless BMS Modernization and AI-Powered Cybersecurity Integration for Real-Time ERP Platforms: Oracle Cloud Applications

Hanna Elżbieta Kowalska

doi:10.15662/IJRAI.2024.0703003

Authors

Hanna Elżbieta Kowalska Lead Cloud Engineer, United Kingdom Author

DOI:

https://doi.org/10.15662/IJRAI.2024.0703003

Keywords:

BMS modernization, AI-powered cybersecurity, Oracle Cloud Applications, real-time ERP, zero-downtime migration, predictive maintenance, intelligent threat detection, cloud automation, secure integration, smart facility management

Abstract

The increasing convergence of Building Management Systems (BMS) and enterprise digital infrastructure necessitates a new paradigm that ensures uninterrupted operations, adaptive intelligence, and robust cybersecurity. This paper presents an integrated framework for seamless BMS modernization leveraging AI-powered cybersecurity and real-time automation within Oracle Cloud–based ERP platforms. The proposed architecture emphasizes zero-downtime migration, predictive maintenance, and intelligent threat detection through machine learning–driven anomaly analysis. By aligning BMS data flows with Oracle Cloud Applications’ ERP modules, organizations can achieve unified visibility across operational and financial layers while enhancing resilience against evolving cyber threats. A case-driven evaluation demonstrates improvements in system uptime, response latency, and data protection efficiency. The study highlights how AI-enabled orchestration, automated patch management, and secure API integration foster sustainable, compliant, and future-ready enterprise environments for smart facility management.

References

1. Bakumenko, A., & Aivazian, V. (2022). Detecting anomalies in financial data using machine learning. Systems, 10(5), 130.

2. Sangannagari, S. R. (2023). Smart Roofing Decisions: An AI-Based Recommender System Integrated into RoofNav. International Journal of Humanities and Information Technology, 5(02), 8-16.

3. Bandara, F., et al. (2023). Enhancing ERP responsiveness through big data technologies. International Journal of Information Management Systems, 2023.

4. G Jaikrishna, Sugumar Rajendran, Cost-effective privacy preserving of intermediate data using group search optimisation algorithm, International Journal of Business Information Systems, Volume 35, Issue 2, September 2020, pp.132-151.

5. Grabski, S. V., Leech, S. A., & Schmidt, P. J. (2011). A review of ERP research: A future agenda for accounting information systems. Journal of Information Systems, 25(1), 37–78.

6. ISACA. (2021). ERP security and controls (ISACA Professional Practices Paper).

7. NIST. (2020). Rose, S., Borchert, O., Mitchell, S., & Connelly, S. Zero Trust Architecture (NIST SP 800-207). National Institute of Standards and Technology.

8. Oracle Corporation. (2019). Secure critical data with Oracle Data Safe (White paper).

9. Chunduru, V. K., Gonepally, S., Amuda, K. K., Kumbum, P. K., & Adari, V. K. (2022). Evaluation of human information processing: An overview for human-computer interaction using the EDAS method. SOJ Materials Science & Engineering, 9(1), 1–9.

10. Oracle Corporation. (2023). Cybersecurity solutions and best practices to protect your organization (Oracle white paper).

11. Gandhi, S. T. (2023). AI-Driven Compliance Audits: Enhancing Regulatory Adherence in Financial and| Legal Sectors. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 8981-8988.

12. Peng, G., Xiao, X., Li, D., et al. (2018). SAQL: A stream-based query system for real-time abnormal system behavior detection. arXiv preprint.

13. SANS Institute. (2019). ERP security: Understanding and mitigating risks (SANS white paper).

14. Subramanian, G. H. (2017). Cloud ERP implementation and the impact of cloud computing on ERP. International Journal of Enterprise Information Systems, 13(4), 21–34.

15. Vaidya, S., & Seetharaman, P. (2020). Artificial intelligence applications in ERP systems. Information Systems Frontiers, 22(2), 475–491.

16. Yu, J., Kim, M., Oh, H., & Yang, J. (2021). Real-time abnormal insider event detection on enterprise resource planning systems via predictive auto-regression model. IEEE Access, 9, 62276–62284.

17. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2023). Navigating digital privacy and security effects on student financial behavior, academic performance, and well-being. Data Analytics and Artificial Intelligence, 3(2), 235–246.

18. Yu, J., Oh, H., Kim, M., & Jung, S. (2022). Unusual insider behavior detection framework on enterprise resource planning systems using adversarial recurrent autoencoder. IEEE Transactions on Industrial Informatics, 18(3), 1541–1551.

19. Badmus, A., & Adebayo, M. (2020). Compliance-Aware Devops for Generative AI: Integrating Legal Risk Management, Data Controls, and Model Governance to Mitigate Deepfake and Data Privacy Risks in Synthetic Media Deployment.

20. Zwilling, M., Lesjak, D., & Kovačič, A. (2020). Cyber security threats and vulnerabilities in ERP systems. Procedia Computer Science, 176, 2242–2250.

21. K. Anbazhagan, R. Sugumar (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud. Indian Journal of Science and Technology 9 (48):1-5.

22. Nallamothu, T. K. (2024). Real-Time Location Insights: Leveraging Bright Diagnostics for Superior User Engagement. International Journal of Technology, Management and Humanities, 10(01), 13-23.

23. Bin Sarhan, B., et al. (2022). Insider threat detection using machine learning approaches. Applied Sciences, 13(1), 259.