An AI Governance Framework on Microsoft Azure That Works

An auditor asks one question: "Show me where this policy is enforced at runtime."

Most companies fail this test. They hand over 40-page documents and risk registers. Documents describe intent. They do not stop a developer from using an unapproved model. They do not stop an agent from spinning up sub-agents.

The gap between your policy binder and your running system is where breaches happen.

To fix this, you need a mandatory AI gateway. This is your universal control plane. Every model call and every agent action must pass through it.

On Microsoft Azure, you must connect three planes:

• Identity (Microsoft Entra): The gateway authenticates every caller before they reach a model. • Data (Microsoft Purview): The gateway applies PII redaction and logs events that Purview might miss. • Model (Azure AI Foundry): The gateway enforces model allowlists and token limits.

A mandatory gateway performs three tasks that documents cannot:

  • It kills shadow models: If you only allow model access through the gateway, unapproved models simply fail to connect.
  • It contains agent sprawl: Every agent action must route through a central point for authorization.
  • It produces continuous evidence: Every call becomes an audit event. You no longer manufacture evidence for reviews; you collect it constantly.

Do not treat governance as a speed tax. Treat it as architecture.

Stop treating shadow models as a detection problem. It is a routing decision. If your network only allows egress through the gateway, the problem disappears.

Stop treating prompt injection as just a security issue. It is an accountability issue. Keep high-consequence actions outside the LLM loop. Use a separate, constrained path to authorize important operations.

Build your governance in three phases:

  1. Discovery: Use audit-mode policies. Find your shadow models and lineage breaks without blocking anything.
  2. Enforced Gates: Turn on deny policies. Wire evaluation gates into your CI/CD pipeline so non-compliant models fail during development.
  3. Continuous Evidence: Use immutable logging. Replace point-in-time approvals with live data.

Governance lives in your code, not in your folders.

Source: https://dev.to/az365ai/an-ai-governance-framework-on-microsoft-azure-that-actually-works-3773

Optional learning community: https://t.me/GyaanSetuAi