Secure and Resilient DevOps Automation Using AI-Based Threat Detection

DevOps is now a popular software development model, which encourages the use of continuous integration, deployment, and automation. Nevertheless, the fast and interconnected nature of the DevOps pipelines have introduced security loopholes that cannot be easily controlled using the conventional rule-based frameworks. The current paper introduces an AI-driven DevSecOps framework that is designed to ensure better security during the DevOps lifecycle. It is implemented with the methodology combining machine and deep learning models at sensitive phases, which include the code analysis, build processes, deployment, and runtime monitoring to identify and address threats real-time. The framework operates under the supervised and unsupervised model of learning to detect known and unknown security threats to proactively defend and resilience of the systems. The experiment findings indicate that AI-based system is capable of significantly shortening detection and response time, increasing service availability, and enhancing resilience of the system without influencing the speed of deployment. This publication helps advance the sphere of safe and robust DevOps through integrating AI-powered threat detection and automated reaction systems in the continuous delivery streams.