Applying Zero Trust Architecture and AI-Based Anomaly Detection in CI/CD Pipelines

Continuous Integration and Continuous Delivery (CI/CD) pipelines have become essential to modern software engineering by enabling rapid, automated, and scalable deployment of applications. However, the same interconnected automation introduces significant security challenges, including credential compromise, malicious code injection, secrets leakage, artifact tampering, and software supply chain attacks. Traditional perimeter-based security approaches are insufficient to address these evolving threats within dynamic DevOps environments. This article presents a practical security framework that integrates Zero Trust Architecture (ZTA) with AI-driven anomaly detection to enhance the resilience of CI/CD pipelines. Zero Trust principles enforce continuous identity verification, least privilege access, and micro-segmentation across pipeline stages, while machine learning–based User and Entity Behavior Analytics (UEBA) enables early detection of abnormal activity and insider misuse. Additionally, centralized monitoring through SIEM correlation and automated containment via SOAR response playbooks significantly reduce detection and remediation timelines. By combining continuous verification with intelligent behavioral monitoring, the proposed approach strengthens CI/CD security without compromising delivery speed, providing a scalable strategy for preventing pipeline compromise and protecting the software supply chain.