Infrastructure as Code for AI Compliance: Making Regulation Repeatable

If your AI infrastructure is provisioned manually, you can't prove compliance. You can claim it. You can document it. But you can't prove it. Infrastructure as Code changes this — every configuration is versioned, reviewed, and auditable.

IaC as Compliance Evidence

• Configuration drift prevention — GitOps ensures the running state matches the declared state. No "someone changed a setting and didn't document it"
• Change audit trail — Every infrastructure change has a git commit, a reviewer, and a timestamp. Regulators love this
• Reproducibility — "Build it again exactly the same way" becomes trivial. Essential for disaster recovery and regulatory testing
• Policy enforcement — OPA, Kyverno, or Sentinel policies checked at the PR level. Non-compliant infrastructure can't be deployed

IaC Stack for Regulated AI

• Terraform/OpenTofu — Cloud infrastructure provisioning with state management
• Kubernetes manifests + Helm/Kustomize — Application and ML workload definitions
• ArgoCD/Flux — GitOps continuous delivery with drift detection
• OPA/Gatekeeper — Policy enforcement (no GPU workload without resource limits, no model deployment without governance approval)
• Crossplane — Kubernetes-native cloud resource management

Compliance-Specific Patterns

• Encryption-at-rest enforced at the Terraform module level — you can't provision unencrypted storage
• Network policies requiring mTLS between all AI platform components
• Mandatory labels/tags for cost attribution, data classification, and regulatory scope
• Automated CIS benchmark scanning in the CI pipeline

The Audit-Ready State

When a regulator asks "show me how your AI infrastructure is configured and secured," you point to the git repository. Every resource defined, every policy documented, every change tracked. That's the power of IaC for compliance.