Autonomous DevSecOps Optimization with Reinforcement Learning and Intelligent Observability for Enterprise CI CD Pipelines

Abstract

Enterprise software delivery increasingly depends on complex Continuous Integration and Continuous Delivery (CI/CD) pipelines that must simultaneously satisfy requirements for speed, reliability, security, compliance, and cost efficiency. Conventional DevSecOps practices generally depend on predefined rules, static thresholds, manual intervention, and isolated monitoring tools, limiting their ability to respond effectively to rapidly changing workloads and evolving security conditions. This study proposes an autonomous DevSecOps optimization framework that combines Reinforcement Learning (RL) with intelligent observability to dynamically optimize enterprise CI/CD pipelines. The proposed approach integrates telemetry from application performance monitoring, infrastructure monitoring, logs, traces, security scanners, test systems, deployment platforms, and pipeline execution histories into a unified observability layer. Reinforcement learning is then employed to learn adaptive deployment and pipeline optimization policies based on multidimensional operational states and reward functions incorporating execution time, failure rate, security risk, resource consumption, recovery performance, and service reliability. The framework emphasizes safe exploration, policy constraints, explainability, continuous feedback, and human oversight for security-critical decisions. Experimental evaluation can compare the proposed autonomous approach with rule-based DevSecOps and conventional pipeline optimization using metrics such as deployment lead time, change failure rate, mean time to recovery, security detection efficiency, resource utilization, and pipeline cost. The study aims to establish a practical foundation for intelligent, adaptive, and secure enterprise software delivery.