Authors - Uphaar Goyal, Chirag Patadia, Nitinkumar Leuva Abstract - Modern software development environments depend on cloudnative infrastructure, automated CI/CD pipelines, and distributed DevSecOps workflows. These environments improve delivery speed but expand the attack surface through privilege escalation, credential compromise, insider threats, and misconfigured pipeline permissions. Traditional Role-Based Access Control (RBAC) improves least-privilege enforcement, but static RBAC does not adequately respond to changing runtime context such as business hours, network trust, multi-factor authentication status, and deployment pipeline state. This paper proposes an enhanced cybersecurity framework that integrates RBAC, contextaware policy enforcement, and Agentic AI-based autonomous auditing. The proposed agent observes access events, learns behavioral patterns, detects anomalous requests, and generates explainable audit evidence without replacing deterministic access control. Experimental evaluation in a simulated CI/CD environment shows that the RBAC with Agentic AI framework achieves 98.21% accuracy, improves anomaly detection compared with traditional access-control models, and maintains low enforcement latency under increasing concurrency. The results indicate that Agentic AI can strengthen secure software development by adding adaptive audit intelligence while preserving the predictability and administrative clarity of RBAC.
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