Cloud Native Enterprise Transformation through Machine Learning AIOps and Intelligent Security Governance
Abstract
The rapid adoption of cloud-native technologies has transformed enterprise IT environments by enabling scalability, agility, and continuous innovation. However, increasing infrastructure complexity, distributed applications, cybersecurity threats, and operational challenges require intelligent approaches for effective management. This research presents a Cloud Native Enterprise Transformation framework that integrates machine learning, Artificial Intelligence for IT Operations (AIOps), and intelligent security governance to enhance enterprise reliability, automation, and protection. The proposed framework leverages machine learning algorithms for predictive analytics, anomaly detection, workload optimization, and automated operational decision-making. AIOps capabilities enable real-time monitoring, intelligent event correlation, and proactive incident management across cloud-native ecosystems based on containers, microservices, Kubernetes, and hybrid cloud platforms. Intelligent security governance integrates AI-driven threat detection, automated compliance monitoring, risk assessment, and adaptive security controls to protect enterprise assets. The framework aims to overcome limitations of traditional IT management approaches by providing autonomous, predictive, and security-aware enterprise operations. The research highlights how the combination of cloud-native architectures, machine intelligence, and governance mechanisms can improve operational efficiency, reduce downtime, strengthen cybersecurity resilience, and support digital transformation initiatives. The proposed model provides a foundation for future intelligent enterprises capable of achieving continuous optimization, automated management, and secure cloud transformation.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 5 No. 4 (2022): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
8140-8147 |
Published |
August 9, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Agim Takon (%2022). Cloud Native Enterprise Transformation through Machine Learning AIOps and Intelligent Security Governance. International Journal of Science, Research and Technology , Vol. 5 No. 4 (2022): International Journal of Science, Research and Technology (IJSRAT) , pp. 8140-8147. https://doi.org/10.15662/IJSRAT.2022.0504003 |
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2. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. National Institute of Standards and Technology.
3. Buyya, R., Broberg, J., & Goscinski, A. (2011). Cloud Computing: Principles and Paradigms. Wiley.
4. 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
5. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.
6. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
7. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.
8. 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.
9. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
10. Mohammed, S. (2021). Hybrid cloud architecture strategy for global infrastructure operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(6), 4078–4081.
11. 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
12. Adepu, G. (2021). Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud-Native Service Architecture. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3089-3093.
13. 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
14. Konakalla, K. (2020). Automated commission calculation and sales quota management in Salesforce: A code-driven approach for sales efficiency. International Journal, 7, 125-127.
15. 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.
16. 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
17. Vayyasi, N. K. (2019). Reimagining financial compliance automation: Using Java microservices and generative AI on AWS Bedrock for regulatory intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 2(3), 1992–1210.
18. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014
19. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.
20. Vankayala, S. C. (2019). Establishing Auditable and Privacy-Respectful Test Data Systems through Synthetic Data Engineering and Governance-Driven Anonymization. International Journal of Computer Technology and Electronics Communication, 2(6), 1809-1821.
21. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
22. Tamilvizhi, T., Surendran, R., Anbazhagan, K., & Rajkumar, K. (2022). Quantum Behaved Particle Swarm Optimization‐Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification. Mathematical Problems in Engineering, 2022(1), 3452413.
23. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
24. Yamsani, N. (2018). Operationalizing regulatory governance through enterprise master data design: A practical examination of OFAC, KYC, and GDPR controls at Elavon. International Journal of Scientific Research & Engineering Trends, 4(6). https://doi.org/10.5281/zenodo.18196005
25. 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.
26. Mathew, A. R. (2022). Threats and protection on E-sim: a prospective study. Novel Perspectives of Engineering Research, 8, 76-81.
27. 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.
28. Vimal, V. R., Anandan, P., & Kumaratharan, N. (2022). Heart Disease Diagnosis Using Electrocardiography (ECG) Signals. Intelligent Automation & Soft Computing, 32(1).
29. Vanitha, C., Sanmugam, A., Yogananth, A., Rajasekar, M., Kuppusamy, P. G., & Devasagayam, G. (2022). A facile synthesis of polyaniline-WO3 hybrid nanocomposite for enhanced dopamine detection. Materials Letters, 328, 133149.
30. Ajith, G., Sudarsaun, J., Arvind, S. D., & Sugumar, R. (2018). IoT based fire deduction and safety navigation system. Int. J. Innov. Res. Sci. Eng. Technol, 7(2).
31. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
32. Li, X., Zhang, X., & Wang, Z. (2020). Machine learning based intelligent cloud resource management. IEEE Access, 8, 123000–123015.
33. Zhang, Y., Zheng, L., & Chen, X. (2021). AI-driven automation and security governance for cloud-native enterprise systems. Future Internet, 13(4), 102.