Enterprise AI Governance Platform With Policy As Code Guardrails

Capability 3 — Enterprise AI Governance Platform with Policy-as-Code Guardrails

• Centralized governance for AI workloads across AWS, Azure, and GCP.

• Covered:

  • IAM / RBAC
  • Approved AI services
  • Approved models
  • Data access
  • Data residency
  • Encryption
  • Network isolation
  • Infrastructure configuration
  • Compliance
  • Cost controls

• Implemented controls using Policy-as-Code.

• Policies were integrated directly into engineering workflows.

• Examples:

  • Prevent use of an unapproved model.
  • Prevent deployment into a prohibited region.
  • Require encryption.
  • Restrict access to sensitive enterprise data.
  • Enforce approved cloud services.
  • Enforce infrastructure security standards.
  • Enforce AI cost limits.

• Governance applied to:

  • Raw LLM applications.
  • RAG applications.
  • Custom models.
  • AI agents.
  • Infrastructure generated through AI.

• Key principle:

  • AI could recommend or generate changes, but policy determined what was allowed.