1. Secure Access Control to AI Datasets with Azure AD


    To achieve secure access control to AI datasets with Azure Active Directory (Azure AD), you'll want to integrate Azure AD authentication with your cloud resources that store the datasets. This typically involves creating and configuring an Azure AD service principal, which is an identity created for use with applications, hosted services, and automated tools to access Azure resources. You'll also want to manage permissions for Azure resources, potentially by using roles and policies.

    To illustrate how to set this up using Pulumi, we'll write a program that does the following:

    1. Creates a new Azure AD application. Applications in Azure AD represent a globally unique application object.
    2. Creates a service principal for the application. A service principal is needed to delegate permissions to the application to allow it to access Azure resources.
    3. Sets permissions to restrict the service principal to only have access to what is necessary.

    For example, let's say we have an Azure AI dataset that we want to control access to. We would:

    • Create an Azure AD application and service principal.
    • Assign the service principal to a role with the appropriate permissions to access the AI dataset.
    • Optionally, we can assign policies that define further claims or restrictions.

    Here is a Pulumi program in Python that establishes such a configuration:

    import pulumi import pulumi_azuread as azuread # Creating a new Azure AD application app = azuread.Application("aiDatasetApp", display_name="AI Dataset Application") # Creating a service principal for the Azure AD application sp = azuread.ServicePrincipal("aiDatasetServicePrincipal", application_id=app.application_id) # (Optional) Define and assign a Claims Mapping Policy if needed for advanced claims control claims_map_policy = azuread.ClaimsMappingPolicy("aiDatasetClaimsMappingPolicy", display_name="AI Dataset Claims Mapping Policy", definitions=[ """{ "ClaimsMappingPolicy": { "Version": 1, "IncludeBasicClaimSet": "true", "ClaimsSchema": [ { "ClaimType": "http://example.org/claims/datasetaccess", "ValueType": "string", "Value": "true", "Source": "Transformation", "TransformationId": "LookupTransform", "TransformationClaimType": "user.objectid" } ] } }""" ]) # (Optional) Assign the Claims Mapping Policy to the service principal claims_map_policy_assignment = azuread.ServicePrincipalClaimsMappingPolicyAssignment("aiDatasetClaimsMappingPolicyAssignment", service_principal_id=sp.id, claims_mapping_policy_id=claims_map_policy.id) # Note: Additional steps such as assigning the service principal to a role on a specific Azure resource (like AI Dataset) # would be necessary to complete the secure access control configuration. This is typically performed in the resource setup, # which is not shown here due to the focus on Azure AD entities. # Export the app and service principal information pulumi.export("application_id", app.application_id) pulumi.export("service_principal_id", sp.id)


    1. Azure AD Application: We start by creating an Azure AD application using azuread.Application. This represents the identity of your application within Azure AD and acts as a template from which service principals can be derived for use in actual authentication scenarios.

    2. Service Principal: We then create a service principal using azuread.ServicePrincipal, which is derived from the application object we created earlier. The service principal embodies the instance of an application within a directory or tenant.

    3. Claims Mapping Policy (Optional): If needed, a azuread.ClaimsMappingPolicy resource can be defined to control the claims presented in tokens issued by Azure AD. This is an advanced scenario where you might need to transform or restrict claims based on your requirements.

    4. Assigning Policy: Finally, we associate the service principal with the claims mapping policy through the azuread.ServicePrincipalClaimsMappingPolicyAssignment resource.

    This program sets the groundwork for secure access management to Azure resources like AI Datasets but does not include the specifics of the resources that host the datasets, such as Azure Storage Accounts, Azure SQL Databases, etc. For a complete setup, you would enhance this Pulumi program to include resources that host your datasets and assign appropriate roles to the service principal to access those resources. The roles and policies will depend on your specific use case and which Azure services you are using to host the datasets.