Cloud-native development demands secure, scalable applications delivered at unprecedented speed. Existing DevOps approaches lack intelligent decision-making for security vulnerability prioritization, anomaly detection, and policy enforcement. This paper presents an AI-Augmented DevOps framework integrating machine learning with DevSecOps practices to create self-managing deployment pipelines. Our approach combines pre-commit security scanning, AI-powered vulnerability assessment, policy-as-code enforcement, and explainable anomaly detection, achieving 87% reduction in security incidents and 340% improvement in deployment frequency. We demonstrate effectiveness through complete implementation using GitHub Actions, Kubernetes, and custom ML models, achieving <5% false positive rates and 3.5-month ROI. Furthermore, our framework serves as an embedded tutoring system, with user studies showing a 67% improvement in developers' understanding of security practices. The open-source implementation enables reproducible research and immediate practical adoption.
Akshay Mittal (Wed,) studied this question.