This research article presents a deeply elaborated and theoretically grounded examination of AI-augmented DevSecOps security, synthesizing findings from neural code-scanning innovations, adaptive learning systems, policy-driven architectures, cloud-native security automation, and AIOps-enabled operational intelligence. Drawing exclusively from the provided references, the study develops an expanded conceptual model illustrating how deep learning, adaptive threat modeling, automated security governance, and continuous vulnerability detection converge to create a mature, self-evolving DevSecOps ecosystem. The article emphasizes the increasing sophistication of neural code-scanning models capable of identifying complex and previously unseen vulnerabilities, along with adaptive learning mechanisms designed to cope with evolving attack surfaces in cloud-native architectures. In addition, policy-driven DevSecOps frameworks are explored as a means of enforcing compliance, providing architectural guardrails, and guaranteeing uniform security enforcement across distributed microservices. Further analysis highlights the relevance of AI safety principles, the operational challenges inherent in large-scale automation, and the implications of continuous security testing for real-world CI/CD environments. This research integrates theoretical reasoning with practical insights arising from case studies on pipeline vulnerabilities, dynamic security testing obstacles, anomaly detection within cloud-native microservice ecosystems, and the emerging importance of AIOps in enabling self-healing pipeline infrastructures. Collectively, the findings offer a detailed conceptual foundation intended to support researchers, security engineers, and DevSecOps practitioners seeking to design robust, AI-enabled security architectures capable of sustained resilience.

vulnerability detection, adaptive learning, CI/CD security

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