As cloud adoption accelerates and development cycles shorten, traditional security methodologies struggle to maintain pace with the agility and scale demands of modern software deployment. DevSecOps—a paradigm integrating security principles into DevOps workflows—has emerged as essential for securing cloud-native systems. This chapter provides a comprehensive examination of how Artificial Intelligence (AI) enhances DevSecOps practices to address the unique security challenges inherent in cloud environments. We begin by establishing foundational context through analysis of DevOps evolution and cloud security challenges. The core contribution explores AI-driven security automation techniques across the complete DevSecOps lifecycle, from secure code generation and automated testing in “shift-left” phases, through intelligent CI/CD pipeline monitoring, to real-time threat detection and automated incident response. Special emphasis is placed on MLSecOps—the critical practice of securing AI/ML systems themselves—including training data protection, adversarial defense mechanisms, and continuous model monitoring. Throughout the analysis, we highlight recent industry implementations and empirical research findings demonstrating AI’s measurable impact on code analysis accuracy, anomaly detection precision, identity management automation, and compliance verification. The chapter concludes by addressing practical implementation challenges, ethical considerations including bias mitigation and transparency requirements, and emerging trends in hyperautomation, AI-assisted security operations, and quantum-resistant security strategies. This synthesis provides graduate students, researchers, and practitioners with evidence-based insights and actionable frameworks for leveraging AI technologies safely and effectively within cloud DevSecOps implementations.