Enterprise AI Governance on Azure: Entra ID, APIM, Purview, and Monitor for AI Agents

Deploying an AI agent in production at an industrial company means meeting the same security and governance standards as any other enterprise application. This isn’t optional—it’s what separates a well-run pilot from something that gets shut down by IT security six months in. Here’s the practical implementation guide for the four governance pillars on Azure.

Pillar 1: Identity with Azure Entra ID

Every AI agent needs an identity. Don’t use API keys in environment variables. Use managed identities and service principals.

# Managed identity on Azure Container App
resource "azurerm_user_assigned_identity" "agent_identity" {
  name                = "quoting-agent-identity"
  resource_group_name = azurerm_resource_group.main.name
  location            = azurerm_resource_group.main.location
}

# Grant the agent identity access to Azure OpenAI (no key needed)
resource "azurerm_role_assignment" "agent_openai" {
  scope                = azurerm_cognitive_account.openai.id
  role_definition_name = "Cognitive Services OpenAI User"
  principal_id         = azurerm_user_assigned_identity.agent_identity.principal_id
}

With a managed identity, the agent authenticates to Azure OpenAI, Azure AI Search, Cosmos DB, and other services using its Entra token—no secrets stored in code or config. Rotation is automatic. Access is auditable in Entra logs.

Pillar 2: API Gateway with Azure APIM

Put Azure API Management in front of your agent endpoints. This gives you: rate limiting (prevent runaway cost from prompt injection), authentication enforcement, request/response logging, and a single entry point for all AI services.

# APIM policy: rate limit + JWT validation
<policies>
  <inbound>
    <base />
    <validate-jwt header-name="Authorization" failed-validation-httpcode="401">
      <openid-config url="https://login.microsoftonline.com/{tenant_id}/.well-known/openid-configuration" />
      <audiences><audience>api://quoting-agent</audience></audiences>
    </validate-jwt>
    <rate-limit-by-key calls="10" renewal-period="60"
      counter-key="@(context.Request.Headers.GetValueOrDefault("Authorization",""))" />
  </inbound>
</policies>

Pillar 3: Data Governance with Microsoft Purview

Industrial AI agents process sensitive data: drawings, supplier contracts, quality records, customer RFQs. Purview provides: automatic data classification (PII, confidential, proprietary), lineage tracking (which data fed which AI decision), and policy enforcement (who can access what).

Practical first step: register your Azure Storage accounts and databases in Purview. Enable automatic scanning. Label storage containers that hold CAD files and customer data as “Confidential.” Purview will alert you when unlabeled sensitive data appears and track when AI agents access it.

Pillar 4: Observability with Azure Monitor + Application Insights

You need to know: Is the agent healthy? Is it expensive? Is it making good decisions? Application Insights instruments your agent code; Azure Monitor aggregates logs and metrics; alerts fire when something goes wrong.

from opentelemetry.sdk.trace import TracerProvider
from azure.monitor.opentelemetry import configure_azure_monitor

configure_azure_monitor(connection_string=os.environ["APPLICATIONINSIGHTS_CONNECTION_STRING"])

# Instrument agent decision points
with tracer.start_as_current_span("agent.quote_extraction") as span:
    span.set_attribute("drawing.urn", drawing_urn)
    span.set_attribute("model.version", model_version)
    bom = extract_bom(drawing_urn)
    span.set_attribute("bom.items_count", len(bom))
    span.set_attribute("agent.confidence", bom.confidence_score)

Every agent invocation is a traced span. You can see latency by step, token costs, confidence scores, and error rates—all in Azure Monitor dashboards. Set alerts for: cost/hour exceeding threshold, error rate >5%, latency >10s.