AI-Enhanced DevSecOps Architecture for Cloud-Native Banking Secure Distributed Systems with Deep Neural Networks and Automated Risk Analytics
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Abstract
The architecture employs distributed DNN models for anomaly detection, fraud identification, behavioral analytics, and risk scoring across large-scale banking workloads. Automated risk analytics powered by AI and data mining enable early detection of security deviations and operational failures. The framework leverages cloud-native technologies—containerized microservices, service meshes, scalable data pipelines, and distributed storage systems—to ensure elasticity, high availability, and fault tolerance. NLP-driven threat intelligence modules further enhance situational awareness by mining logs, alerts, and communication channels for contextual insights.
Experimental evaluation indicates improvements in detection accuracy, reduction in security response time, and enhanced resilience under distributed workloads. The integrated AI–DevSecOps architecture provides a robust and scalable foundation for secure next-generation banking platforms, ensuring continuous protection, operational agility, and intelligent risk governance in complex cloud-native environments.
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