Integrating AI-Based Security and Reinforcement Learning-Driven Intrusion Detection into CI/CD Pipelines for Secure Software Delivery

Abstract

The rapid adoption of Continuous Integration and Continuous Deployment (CI/CD) pipelines has transformed modern software development by enabling frequent releases, automated testing, and accelerated deployment. However, the increased automation and interconnectedness of CI/CD environments have also expanded the attack surface, exposing software delivery pipelines to sophisticated cyber threats such as code injection, supply chain attacks, credential compromise, and malicious configuration changes. Conventional security mechanisms, which rely on static rule-based detection and periodic vulnerability assessments, are often inadequate for identifying evolving attack patterns in dynamic DevSecOps environments. To address these challenges, this paper proposes an AI-driven security framework that integrates Reinforcement Learning (RL)-based intrusion detection into CI/CD pipelines, enabling intelligent, adaptive, and continuous protection throughout the software development lifecycle.The proposed framework combines artificial intelligence techniques with reinforcement learning to continuously monitor pipeline activities, analyze developer and system behavior, detect anomalous events, and dynamically optimize security responses based on environmental feedback. The RL agent learns optimal defense strategies by interacting with the CI/CD environment, allowing it to improve intrusion detection accuracy while minimizing false positives and reducing response latency. Security controls are embedded across key pipeline stages—including source code management, build automation, dependency validation, testing, containerization, artifact management, and deployment—to provide end-to-end protection without disrupting development workflows. Furthermore, automated policy enforcement, vulnerability prioritization, and risk-aware decision-making strengthen the resilience of the software delivery process against both known and zero-day threats.The proposed architecture is evaluated using standard cybersecurity performance metrics, including detection accuracy, precision, recall, F1-score, false positive rate, mean time to detection, and pipeline execution overhead. Experimental results demonstrate that the integration of AI-driven security analytics with reinforcement learning significantly enhances threat detection capability, improves adaptive response to emerging attacks, and maintains software delivery efficiency with minimal computational overhead. Compared with conventional intrusion detection approaches, the proposed framework achieves higher detection performance, faster incident response, and greater robustness against evolving cyber threats.This research contributes a scalable and intelligent DevSecOps security framework that seamlessly integrates AI-based security and reinforcement learning into CI/CD pipelines. By enabling autonomous intrusion detection, adaptive threat mitigation, and continuous software assurance, the proposed solution enhances the security, reliability, and resilience of modern software delivery ecosystems while supporting the growing demands of cloud-native and enterprise software engineering