Effective cybersecurity in critical infrastructure systems has become an urgent global necessity. As industries increasingly deploy continuous integration/continuous deployment (CI/CD) pipelines, surge in interconnected systems, and pervasive use of artificial intelligence (AI), vulnerabilities proliferate in both software and operational layers. This paper presents a novel, integrated framework that synthesizes established cybersecurity standards with cutting‑edge AI-based vulnerability detection and demand forecasting models, aiming to secure CI/CD-powered critical infrastructure systems. Leveraging the principles of the National Institute of Standards and Technology (NIST) cybersecurity framework (NIST, 2018) as a foundational scaffold, the proposed methodology incorporates AI-driven code analysis, anomaly detection, resource demand forecasting, and continuous vulnerability management. We discuss theoretical underpinnings, detail a comprehensive methodology, and enumerate expected results. Our discussion explores potential limitations, ethical considerations, and a roadmap for future research. This work contributes to bridging the gap between standardized cybersecurity guidelines and dynamic, AI-enhanced defense in modern CI/CD and critical infrastructure environments.

CI/CD security, AI-based vulnerability detection, NIST cybersecurity framework

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