Immutable Infrastructure-as-Code (IaC) and Policy-as-Code Guardrails for Sovereign AI Cloud Platforms

Sovereign and regulated enterprise artificial intelligence platforms operate under stringent statutory mandates governing data residency, customer-managed cryptographic key custody, hardware-enforced confidential computing, and cross-border network isolation. When cloud platform teams manage hundreds of multi-tenant AWS, Azure, or Google Cloud accounts using manual console interventions or imperative scripts, configuration drift and overly permissive Identity and Access Management (IAM) policies create severe compliance exposure and unauthorized model-weight exfiltration vectors. This paper presents an end-to-end Immutable Infrastructure-as-Code (IaC) and Policy-as-Code (PaC) Governance Architecture designed to enforce provable security and regulatory invariants across sovereign AI cloud platforms. Synthesizing production engineering patterns from Goldman Sachs multi-account cloud governance and HashiCorp Terraform integrated with Open Policy Agent (OPA) Rego admission pipelines, the proposed architecture establishes a three-stage defense-in-depth enforcement loop: pre-apply static plan verification over Terraform abstract syntax trees, Kubernetes dynamic admission control via OPA Gatekeeper, and continuous drift reconciliation using Crossplane and AWS Config conformance packs. We evaluate the governance platform across a 120-account enterprise AI cloud estate hosting 640 GPU training and inference clusters over a 180-day production period. Empirical benchmarks demonstrate that shifting policy evaluation left into CI/CD pull-request gates catches 99.4% of IAM, S3 bucket encryption, VPC PrivateLink, and GPU confidential-compute misconfigurations in 3.8 seconds prior to cloud API provisioning. Furthermore, automated GitOps drift reconciliation eliminates manual SSH/console mutation, reducing Mean Time to Remediate (MTTR) configuration drift by 96.2% (from 6.4 hours to 14.5 seconds) and cutting quarterly regulatory audit preparation overhead by 84.0%. Research Domain & Specialization Track Master of Science in Information Technology (MSIT) — Cloud DevSecOps, GitOps Automation, and Infrastructure Governance. Core Technical Highlights Empirical Production Case Studies: Rigorous architectural blueprints and telemetry evaluation across enterprise cloud deployments. Four-Tier Reference Architecture: Formal specification from edge ingress to distributed hardware execution pools. Mathematical Formulation: Formally derived algorithmic and optimization models with closed-loop verification. Multi-Modal Benchmarking: Comparative empirical baselines under fault-injection and synthetic workload scaling. Author Information Author: Sonu Kumar SinghRole: Senior Consultant Affiliation: Independent Researcher Publication Status: Preprint / Working Paper (MSIT AI & Cloud Systems Research Series) Reproducibility & Open Science All included architecture diagrams, telemetry schemas, and benchmark curves are open-access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.