Autonomous AI-Powered Cloud-Native Platforms for Continuous Authentication Intrusion Detection Digital Banking Analytics and Secure DevSecOps
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Abstract
The rapid evolution of digital banking ecosystems demands intelligent, autonomous, and resilient security infrastructures capable of defending against sophisticated cyber threats while ensuring seamless customer experiences. Traditional perimeter-based defenses and static authentication models are inadequate in modern cloud-native environments characterized by distributed microservices, API-driven integrations, and real-time financial transactions. This paper proposes an Autonomous AI-Powered Cloud-Native Platform integrating Continuous Authentication, Intrusion Detection Systems (IDS), Digital Banking Analytics, and Secure DevSecOps automation. The framework leverages machine learning models for behavioral biometrics, anomaly detection, fraud prediction, and adaptive risk scoring. Cloud-native principles such as containerization, orchestration, service mesh security, and API gateways ensure scalable and secure deployment. DevSecOps pipelines embed automated vulnerability scanning, compliance validation, and policy-as-code enforcement into continuous integration and delivery workflows. The proposed architecture enables real-time threat detection, intelligent response orchestration, and privacy-preserving analytics across distributed banking systems. Experimental modeling demonstrates improved fraud detection accuracy, reduced incident response time, and enhanced regulatory compliance. This research contributes a comprehensive autonomous security framework tailored for next-generation digital banking platforms operating in dynamic multi-cloud environments.
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