Enterprise AI governance increasingly relies on policies covering model use, data protection, human oversight, security, risk classification, and regulatory obligations. Manual review alone cannot scale with rapidly changing AI portfolios and autonomous systems. This paper proposes an Enterprise AI Policy-as-Code and Automated Compliance Architecture that transforms governance requirements into machine-readable rules enforced across development, deployment, and runtime. The architecture defines policy sources, control mappings, rule engines, decision points, compliance evidence, exception handling, continuous monitoring, and audit reporting. It supports model registries, AI gateways, CI/CD pipelines, identity systems, data platforms, agent tools, and observability services. The framework distinguishes preventive, detective, corrective, and compensating controls and includes versioning, testing, approval, and rollback of policy logic. The proposed approach enables enterprises to scale AI governance consistently while reducing manual effort, improving auditability, and enforcing controls close to the point of AI action.

Sanjeeve Kumar Gajadi, "Enterprise AI Policy-as-Code and Automated Compliance Architecture" International Journal of Scientific Research and Engineering Development, V9(5): Page(1917-1918) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.