AI-Enhanced CICD Governance for Regulated Cloud Applications: A Compliance-Aware DevOps Framework

AI-supported CI/CD for regulated cloud applications has become an important area of research because modern delivery pipelines now function not only as engineering infrastructure, but also as compliance-producing systems. Release automation has to produce defensible evidence, as opposed to just making deployment faster, in sectors where safety, privacy, security, and accountability are required. Related aspects of this issue have been discussed in recent journal literature on DevSecOps automation, MLOps lifecycle discipline, AI-based anomaly detection, algorithm auditing, and assurance of AI-intensive systems. Direct journal evidence integrating all dimensions of the topic remains limited, but the existing body of literature highlights several components relevant to regulated cloud delivery: policy-as-code, test orchestration, traceable approval workflow, and machine-assisted risk triage. Persistent shortcomings in the reviewed literature include weak explainability for AI-assisted gates, inconsistent support for audit-readable evidence, limited integration between infrastructure and application controls, and insufficient validation in highly regulated production environments. This article develops a compliance-aware DevOps framework in which AI acts as an amplifier of governance rather than a substitute for formal control design. This field is important because machine-readable regulation, verifiable delivery evidence, and human-accountable automation are likely to become foundational to future cloud assurance.