With the rapid proliferation of DevOps practices across software-intensive organizations, Continuous Integration and Continuous Deployment (CI/CD) pipelines have become the linchpin of software delivery processes. However, as deployment frequency and pipeline complexity grow — particularly when incorporating AI-enabled applications — reliability challenges and security vulnerabilities have concurrently escalated. This paper synthesizes and extends existing research to propose a unified framework that integrates AI-driven predictive modeling for failure prevention and continuous security testing within CI/CD workflows. Drawing on industry data from the 2023 state of DevOps, academic studies on predictive failure models (Patel, 2019; Enemosah, 2025), research on AI-enabled DevOps challenges (Crnkovic et al., 2020; Joseph et al., 2024), and established DevSecOps best practices (Shajadi, 2019; Brás, 2021; Jammeh, 2020; Rangnau et al., 2020; Deegan, 2020), the framework offers a detailed architecture and methodological roadmap for organizations aiming to achieve both speed and safety. Through a structured literature synthesis, thematic analysis, and conceptual modeling, findings reveal that combining predictive analytics with automated security checks can significantly enhance pipeline stability, mitigate failure-related downtime, and reduce vulnerability exposure. The discussion explores theoretical implications, practical constraints, and future research directions. This integration is posited as essential for next‑generation DevOps environments, especially in enterprises deploying AI-intensive applications.

CI/CD, AI-driven predictive models, pipeline reliability

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