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Control AI model deployment with built-in policies in Azure AI Foundry portal

  • Artikkeli

Azure Policy provides built-in policy definitions that help you govern the deployment of AI models in Managed AI Services (MaaS) and Model-as-a-Platform (MaaP). You can use these policies to control what models your developers can deploy in Azure AI Foundry portal.

Prerequisites

  • An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
  • Permissions to create and assign policies. To create and assign policies, you must be an Owner or Resource Policy Contributor at the Azure subscription or resource group level.
  • Familiarity with Azure Policy. To learn more, see What is Azure Policy?.

Enable the policy

  1. From the Azure portal, select Policy from the left side of the page. You can also search for Policy in the search bar at the top of the page.

  2. From the left side of the Azure Policy Dashboard, select Authoring, Definition, and then search for "[Preview]: Azure Machine Learning Deployments should only use approved Registry Models" in the search bar within the page. You can also directly navigate to policy definition creation page.

  3. Select on Assign to assign the policy to the management group:

    • Scope: Select the scope where you want to assign the policy. The scope can be a management group, subscription, or resource group.
    • Policy definition: this section should already have a value of "[Preview]: Azure Machine Learning Deployments should only use approved Registry Models".
    • Assignment name: Enter a unique name for the assignment.

    The rest of the fields can be left as their default values or you can customize as needed for your organization.

  4. Select Next at the bottom of the page or the Parameters tab at the top of the page.

  5. In the Parameters tab, deselect Only show parameters that needs input or review to see all fields:

    • Effect: Set to Deny.

      Note

      Using the audit option allows you to configure the policy to log information to your own compliance dashboard.

    • Allowed Models Publishers: This field expects a list of publisher's name in quotation and separated by commas.

    • Allowed Asset Ids: This field expects a list of model asset ids in quotation and separated by commas.

      To get the model asset ID strings and model publishers' name use the following steps:

      1. Go to the Azure AI Foundry model catalog.

      2. For each model you want to allow, select the model to view the details. In the model detail information, copy the Model ID value. For example, the value might look like azureml://registries/azure-openai/models/gpt-35-turbo/versions/3 for GPT-3.5-Turbo model. The provided names are also Collections in model catalog. For example, the publisher for "Meta-Llama-3.1-70B-Instruct" model is Meta.

        Important

        The model ID value must be an exact match for the model. If the model ID is not an exact match, the model won't be allowed.

  6. Select Review + create tab and verify that the policy assignment is correct. When ready, select Create to assign the policy.

  7. Notify your developers that the policy is in place. They receive an error message if they try to deploy a model that isn't in the list of allowed models.

Monitor compliance

To monitor compliance with the policy, follow these steps:

  1. From the Azure portal, select Policy from the left side of the page. You can also search for Policy in the search bar at the top of the page.
  2. From the left side of the Azure Policy Dashboard, select Compliance. Each policy assignment is listed with the compliance status. To view more details, select the policy assignment.

Update the policy assignment

To update an existing policy assignment with new models, follow these steps:

  1. From the Azure portal, select Policy from the left side of the page. You can also search for Policy in the search bar at the top of the page.
  2. From the left side of the Azure Policy Dashboard, select Assignments and find the existing policy assignment. Select the ellipsis (...) next to the assignment and select Edit assignment.
  3. From the Parameters tab, update the Allowed models parameter with the new model IDs.
  4. From the Review + Save tab, select Save to update the policy assignment.

Best practices

  • Granular scoping: Assign policies at the appropriate scope to balance control and flexibility. For example, apply at the subscription level to control all resources in the subscription, or apply at the resource group level to control resources in a specific group.
  • Policy naming: Use a consistent naming convention for policy assignments to make it easier to identify the purpose of the policy. Include information such as the purpose and scope in the name.
  • Documentation: Keep records of policy assignments and configurations for auditing purposes. Document any changes made to the policy over time.
  • Regular reviews: Periodically review policy assignments to ensure they align with your organization's requirements.
  • Testing: Test policies in a nonproduction environment before applying them to production resources.
  • Communication: Make sure developers are aware of the policies in place and understand the implications for their work.

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