Enhancing CI/CD Security with Artificial Intelligence: State of the Art, Challenges, and Integrated Approaches

Abstract

Continuous Integration and Continuous Delivery (CI/CD) pipelines have become a core component of modern software engineering, enabling rapid and automated deployment of applications. While these practices significantly improve development efficiency, they also introduce new and complex security risks, including supply chain attacks, configuration drift, and large-scale propagation of vulnerabilities through automated pipelines [6, 7, 8]. Traditional security mechanisms, which rely primarily on static rules and signature-based detection, are increasingly inadequate in highly dynamic DevSecOps environments [3, 12]. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the security of CI/CD systems. The proposed approach integrates anomaly detection, supervised classification, Explainable AI (XAI), federated learning, and adversarial robustness mechanisms into a unified analytical architecture [2, 4, 5]. The study argues that explainability and privacy-preserving learning are critical for adoption in regulated and mission-critical environments [2, 4, 7]. A conceptual and architectural framework is presented, together with an experimental evaluation strategy, demonstrating how AI-driven security analytics can provide proactive, transparent, and scalable protection for modern CI/CD pipelines.