AI/ML-based security in cloud DevOps pipelines

DevOps convergence with cloud has shocked modern software development by accelerating deployment cycles and streamlining the operations. Speed, however, typically comes at the cost of increased security vulnerabilities in continuous integration (CI)/continuous deployment (CD) flows. Traditional security controls cannot deal with the dynamic and autonomous characteristics of DevOps environments. This chapter outlines a vision for the future of onboarding artificial intelligence (AI) and machine learning (ML) to proactively predict, detect, and prevent cloud-native DevOps pipeline security vulnerabilities. It presents a detailed analysis of AI/ML-based approaches—spanning static code checking and anomaly detection to predictive modeling and automated response. Based on a firm literature review, real-world application examples, and a description of available tools and platforms, the chapter outlines an implementation framework for injecting intelligent security into the DevOps pipeline. It also discusses significant challenges, best practices, and future trends such as explainable AI and federated learning, before ultimately outlining a roadmap to robust, self-protecting software delivery ecosystems.