1. Custom Role Definitions for Secure AI Development Environments

    Python

    To create custom role definitions for secure AI development environments, we'll use Azure as the cloud provider. We'll be defining a custom role that can be assigned to users or groups within your Azure subscription, tailored for your AI development needs. This role will contain specific permissions that grant the ability to perform various actions, like managing AI services, resources, or datastores specific to your development workflow.

    The resource we'll use for this is azure-native.authorization.RoleDefinition. This resource allows us to define a new role with customized permissions. I'll create a role that is scoped to the subscription level, but you can modify the scope to resource groups or other scopes as needed. The permissions in this example are placeholders, and you should replace them with actual permissions required for your environment.

    Below is the complete Pulumi program written in Python to create a custom role definition in Azure focused on AI development:

    import pulumi import pulumi_azure_native as azure_native # Define the custom role for AI development custom_ai_dev_role = azure_native.authorization.RoleDefinition( "aiDevCustomRole", role_name="AI Development Custom Role", # The scope at which the role applies. Can be a subscription, resource group, or a resource. scope="/subscriptions/{subscriptionId}", # Replace {subscriptionId} with your actual subscription ID permissions=[{ "actions": [ # Define the specific actions the role has permission to perform. # These are just examples; you should replace them with suitable actions. "Microsoft.MachineLearningServices/workspaces/write", "Microsoft.MachineLearningServices/workspaces/read", "Microsoft.DataFactory/factories/write", "Microsoft.DataFactory/factories/read", # Add more actions as required ], "notActions": [ # Optionally define actions that the role does not have permission to perform. ], "dataActions": [ # Define data actions that the role has permission to perform. # These are actions for data plane operations. ], "notDataActions": [ # Optionally define data actions that the role does not have permission to perform. ], }], assignable_scopes=[ # Define scopes where this role can be assigned. "/subscriptions/{subscriptionId}", # Replace with your actual subscription ID # You can add more assignable scopes if necessary. ], description="Custom role definition for AI development teams" ) # Export the ID of the custom role definition pulumi.export('custom_ai_dev_role_id', custom_ai_dev_role.id)

    In this code, RoleDefinition is the Pulumi resource that represents an Azure custom role definition. We provide a role_name, which is a friendly name for the role definition. scope is the level at which the role can be assigned, and in this case, it's at the subscription level. Replace {subscriptionId} with your actual subscription ID.

    The permissions list contains dictionaries where you can define actions, notActions, dataActions, and notDataActions. These keys represent what the role is allowed to do with Azure resources. The actions are the management plane operations that can be performed, while dataActions represent operations on the data within those resources.

    The assignable_scopes list tells Azure where this role can be assigned. Generally, it's a best practice to limit the scope to the narrowest possible scope to follow the principle of least privilege.

    Finally, we export the id of the custom role definition so that it can be used elsewhere, such as in a script to assign the role, or in another Pulumi program that needs to reference this role definition.

    Remember to replace the placeholders such as actions, scopes, and subscription IDs with your actual values that apply to your security requirements and Azure environment.

    For more detailed information on setting up role definitions and permissions specific to Azure, refer to the RoleDefinition documentation.


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