1. Conditional IAM Policies for AI Workload Segmentation

    Python

    To apply conditional IAM policies for AI workload segmentation, we'll need to create and configure IAM policies that include conditions to control access based on certain attributes such as resource tags, request status, or other context-specific details. These conditional policies ensure that permissions are granted only when the specified conditions are met, helping to segment AI workloads and improve security.

    In Google Cloud, IAM conditions can be applied using google-native.iam/v1 resources. Specifically, when working with AI workloads, you might be dealing with resources like AI Platform's Workforce Pools or Workload Identity Pools where such conditions can be particularly useful.

    The example below demonstrates how to create a conditional IAM policy for a Workload Identity Pool in the Google Cloud environment using Pulumi with Python. This policy will specify conditions that must be satisfied for the policy to apply. Remember to replace YOUR_PROJECT, YOUR_LOCATION, and YOUR_WORKLOAD_IDENTITY_POOL_ID with the appropriate values for your Google Cloud project, location, and Workload Identity Pool ID.

    Here is a breakdown of the procedure:

    • Import the necessary Pulumi and Google Cloud modules.
    • Create a Workload Identity Pool IAM policy with a condition.
    • Export outputs such as the policy ID for reference.

    Below is the complete Pulumi program in Python:

    import pulumi import pulumi_google_native as google_native # Define the IAM policy for a Workload Identity Pool with conditions workload_identity_pool_iam_policy = google_native.iam.v1.WorkloadIdentityPoolIamPolicy( "workload_identity_pool_iam_policy", workload_identity_pool_id="YOUR_WORKLOAD_IDENTITY_POOL_ID", project="YOUR_PROJECT", location="YOUR_LOCATION", bindings=[{ "member": "serviceAccount:service-account-email@YOUR_PROJECT.iam.gserviceaccount.com", "role": "roles/iam.workloadIdentityUser", "condition": { "title": "Condition title", "description": "Description of the condition", "expression": "request.time < timestamp('2022-12-31T23:59:59Z')" # Replace with your condition }, }] ) # Export the ID of the policy to be used as a reference pulumi.export('workload_identity_pool_iam_policy_id', workload_identity_pool_iam_policy.id)

    In this program, the IAM policy is attached to a specific service account and is conditional on the provided expression. The condition utilizes the Common Expression Language (CEL) and in this case, it checks if the current time is before December 31, 2022.

    This is a basic example and your conditions can be more complex depending on your requirements for workload segmentation. Conditions can help enforce fine-grained access control rules, ensuring that resources are accessed only in predefined scenarios, which is particularly important for AI workloads that may be handling sensitive data.

    For more information on administrating IAM policies and using conditions, you can review the Google Cloud documentation on IAM Conditions:

    Remember to replace placeholders with your specific project, pool identifiers, and your condition. Ensure you have the correct permissions to manage IAM policies for the specified resources in your Google Cloud project.


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