AI-Optimized Multi-Cloud Resource Management Architecture for Secure Banking and Network Environments
DOI:
https://doi.org/10.15662/IJRAI.2022.0504004Keywords:
Multi-cloud resource management, AI optimization, secure banking systems, network environments, workload prediction, cloud orchestration, anomaly detection, compliance enforcementAbstract
The rapid adoption of multi-cloud ecosystems in the banking sector has intensified the need for intelligent, secure, and highly efficient resource management frameworks. This study proposes an AI-optimized multi-cloud resource management architecture designed to enhance operational efficiency, strengthen security posture, and support dynamic network environments. The architecture integrates machine learning–driven workload prediction, automated resource orchestration, and policy-based compliance enforcement to address the complex requirements of regulated financial systems. Advanced anomaly detection mechanisms ensure continuous monitoring of network traffic, enabling real-time threat mitigation and adaptive security control. The model also incorporates cross-cloud interoperability, cost-aware optimization, and data-governance alignment to ensure seamless performance across heterogeneous cloud infrastructures. Experimental evaluations demonstrate the framework’s ability to reduce resource wastage, improve system reliability, and enhance security resilience in multi-cloud banking environments. The proposed architecture provides a scalable foundation for next-generation secure digital banking operations and network management systems.
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