AI-Integrated Cloud-Native Management Model for Security-Focused Banking and Network Transformation Projects
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The rapid evolution of digital banking and network modernization has accelerated the demand for intelligent, secure, and cloud-native management frameworks capable of supporting large-scale transformation initiatives. This study introduces an AI-integrated cloud-native management model designed to enhance project governance, strengthen security controls, and streamline operational workflows across modern banking ecosystems. The proposed model leverages artificial intelligence for predictive analytics, automated decision-making, and adaptive resource coordination, enabling proactive risk mitigation and improved project execution efficiency. Security is embedded into every architectural layer through continuous monitoring, anomaly detection, and policy-driven compliance mechanisms aligned with industry regulations. Additionally, the model promotes seamless integration across distributed network environments, ensuring resilience, scalability, and consistent performance during transformation activities. Experimental analysis demonstrates that the framework significantly enhances security posture, reduces operational friction, and supports end-to-end governance of complex banking and network transformation projects. This work provides a robust foundation for advancing secure, data-driven digital transformation in financial institutions.
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1. Bernstein, D. (2014). Containers and cloud: From LXC to Docker to Kubernetes. IEEE Cloud Computing.
2. 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
3. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
4. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
5. Newman, S. (2015). Building microservices: Designing fine-grained systems. O’Reilly Media.
6. Gill, S. S., Garraghan, P., Stankovski, V., Casale, G., Thulasiram, R. K., Ghosh, S., Ramamohanarao, K., & Buyya, R. (2019). Holistic resource management for sustainable and reliable cloud computing: An innovative solution to a global challenge. Journal of Systems & Software, 155, 104–129.
7. Konda, S. K. (2022). ENGINEERING RESILIENT INFRASTRUCTURE FOR BUILDING MANAGEMENT SYSTEMS: NETWORK RE-ARCHITECTURE AND DATABASE UPGRADE AT NESTLÉ PHX. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6186-6201.
8. Tuli, S., Gill, S. S., Xu, M., Garraghan, P., Bahsoon, R., Dustdar, S., Sakellariou, R., Rana, O., Buyya, R., Casale, G., & Jennings, N. R. (2021). HUNTER: AI-based holistic resource management for sustainable cloud computing.
9. Ravipudi, S., Thangavelu, K., & Ramalingam, S. (2021). Automating Enterprise Security: Integrating DevSecOps into CI/CD Pipelines. American Journal of Data Science and Artificial Intelligence Innovations, 1, 31-68.
10. IBM Institute for Business Value. (2020). Banking on open hybrid multicloud.
11. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
12. Kotapati, V. B. R., Pachyappan, R., & Mani, K. (2021). Optimizing Serverless Deployment Pipelines with Azure DevOps and GitHub: A Model-Driven Approach. Newark Journal of Human-Centric AI and Robotics Interaction, 1, 71-107.
13. McKinsey & Company. (2021). Building the AI bank of the future. McKinsey Global Banking Practice.
14. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
15. Organisation for Economic Co-operation and Development (OECD). (2021). Artificial intelligence, machine learning and big data in finance. OECD Publishing.
16. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(6), 4305-4311.
17. 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.
18. Sugumar, R. (2016). An effective encryption algorithm for multi-keyword-based top-K retrieval on cloud data.
19. Guerra, P., et al. (2021). Machine learning applied to banking supervision: A review. Risks, 9(7), 136.