Practical Integration of Large Language Models into Enterprise CI/CD Pipelines for Security Policy Validation: An Industry-Focused Evaluation

Manual security policy validation of Infrastructure-as-Code (IaC) creates bottlenecks in enterprise CI/CD pipelines, with 90% of cloud breaches involving misconfigured IaC. Traditional static analyzers struggle with evolving cloud services and custom policies. We propose a production-ready framework augmenting conventional scans with Large Language Models (LLMs) for Kubernetes, IAM, and Terraform validation. Our evaluation on 500 synthetic IaC cases shows ensemble methods achieve F1 = 0.95 at 3.1s latency. LLMs detect complex violations missed by rule-based tools, reducing manual review by 60% and maintenance by 70%. Real-world testing in Jenkins and Bamboo confirms cross-platform compatibility. We provide: (i) privacy-preserving CI/CD architecture, (ii) safeguards against prompt injection and hallucination, and (iii) phased rollout strategy for regulated enterprises balancing security and velocity.