AI-Augmented DevSecOps Pipelines for Secure and Scalable Service-Oriented Architectures in Cloud-Native Systems

Abstract

Cloud-native architectures face escalating security challenges that traditional approaches cannot address at scale. This paper presents an AI-augmented DevSecOps framework integrating machine learning models into security pipelines for realtime threat detection and automated response. The framework achieves 95% attack detection rates with sub-2 second latency at 10 k events/sec. Key contributions include LSTM-based threat detection embedded in CI/CD workflows, adaptive model training with 98% accuracy retention over 6 months, and complete opensource implementation. Experimental validation across multiple attack scenarios demonstrates effectiveness while maintaining operational efficiency in hybrid Kubernetes-serverless environments.