Governance Policies Azure AI Machine Learning - Azure/az-prototype GitHub Wiki

Machine Learning

Governance policies for Machine Learning

Domain: azure-ai

Patterns

Name Description
ML workspace with managed network and CMK Secure ML workspace with network isolation, CMK encryption, and associated resources

Anti-Patterns

Description Instead
Do not deploy ML workspace without associated Key Vault, Storage, and App Insights Always provision the four required dependency resources before workspace creation
Do not use workspace access keys for programmatic access Use managed identity and RBAC role assignments (AzureML Data Scientist)
Do not deploy compute with public IPs Set enableNodePublicIp=false and use managed network isolation

References

Checks (3)

Check Severity Description
AZ-ML-001 Required Deploy Azure Machine Learning workspace with managed identity, high business impact, and no public access
AZ-ML-002 Required Deploy compute instances and clusters with managed identity and no public IP
AZ-ML-003 Recommended Use managed online endpoints with managed identity for model serving

AZ-ML-001

Deploy Azure Machine Learning workspace with managed identity, high business impact, and no public access

Severity: Required
Rationale: ML workspaces handle sensitive training data and models; managed identity eliminates credential sprawl
Agents: terraform-agent, bicep-agent, cloud-architect

Targets

  • Microsoft.MachineLearningServices/workspaces

Companion Resources

Resource Name Purpose
Microsoft.Network/privateEndpoints pe-ml-workspace Private endpoint for ML workspace to eliminate public network exposure
Microsoft.Network/privateDnsZones privatelink.api.azureml.ms Private DNS zone for ML workspace API endpoint
Microsoft.Network/privateDnsZones privatelink.notebooks.azure.net Private DNS zone for ML workspace notebook endpoint
Microsoft.Insights/diagnosticSettings diag-ml-workspace Diagnostic settings to route ML workspace activity logs to Log Analytics
Microsoft.Authorization/roleAssignments AzureML Data Scientist / Compute Operator RBAC role assignments for data scientists and compute operators

AZ-ML-002

Deploy compute instances and clusters with managed identity and no public IP

Severity: Required
Rationale: Compute resources with public IPs and no identity create attack surface and credential risk
Agents: terraform-agent, bicep-agent, cloud-architect

Targets

  • Microsoft.MachineLearningServices/workspaces

AZ-ML-003

Use managed online endpoints with managed identity for model serving

Severity: Recommended
Rationale: Managed endpoints handle scaling, versioning, and traffic splitting; managed identity secures model access
Agents: terraform-agent, bicep-agent, cloud-architect, app-developer, csharp-developer, python-developer

Targets

  • Microsoft.MachineLearningServices/workspaces
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