1. ACL Managed Access to AI Training Data in GCP


    To manage access to AI training data in GCP with an Access Control List (ACL), we use Pulumi, which lets us define infrastructure using code. The Pulumi Google Cloud provider offers resources that can help us set up managed access to training data, like datasets and feature stores, and secure them using IAM policies.

    For AI training data, we'll be focusing on two primary resources:

    • AiDataset: This represents a dataset for training models in Google Cloud's AI Platform. We use it to create and manage datasets.
    • AiFeatureStoreIamBinding: This binds a set of members to a specific role for an AI Feature Store, which is a machine learning operational database. Feature stores are used for storing, serving, and managing machine learning features.

    When dealing with ACLs for these resources, you define IAM policies to control who has what kind of access. IAM policies grant specific roles to members (like a user, a group, a service account, or the entire domain) for a particular resource.

    Let's set up a program in Pulumi to create an AI dataset and manage its ACL for a GOOGLE CLOUD PROJECT. You'll need to replace PROJECT_ID and REGION with your Google Cloud project ID and the region you are working in, respectively.

    Below is a Pulumi program in Python that demonstrates how to:

    • Create an AI Dataset.
    • Set up an IAM Policy to manage access to that dataset.
    import pulumi import pulumi_gcp as gcp # Replace these variables with your specific information project_id = 'your-gcp-project-id' region = 'your-region' # Create an AI Dataset ai_dataset = gcp.vertex.AiDataset("aiDataset", project=project_id, region=region, display_name="my-ai-dataset", metadata_schema_uri="gs://google-cloud-aiplatform/schema/dataset/metadata/your_schema.json" ) # IAM Policy Binding for the AI Dataset # This grants the 'roles/aiplatform.user' role to the specified member(s) for the dataset ai_dataset_iam_binding = gcp.vertex.AiFeatureStoreIamBinding("aiDatasetIamBinding", project=project_id, region=region, featurestore=ai_dataset.name, role="roles/aiplatform.user", members=["user:example-user@gmail.com"] ) # Export the AI dataset id pulumi.export("ai_dataset_id", ai_dataset.id)

    In the above code:

    • We define a dataset with AiDataset. Here, metadata_schema_uri should be the Google Cloud Storage URI pointing to the schema of your dataset.
    • We use AiFeatureStoreIamBinding to bind a role to a member for the given AI Feature Store. The featurestore parameter takes the name of the feature store, which we get from the ai_dataset in this case, ensuring we're applying the IAM policy to the correct resource.

    Roles such as roles/aiplatform.user define what actions the members can perform on the dataset. Here, 'user:example-user@gmail.com' represents the user that will get access to the dataset.

    The pulumi.export at the end will output the ID of the created AI dataset, which can be useful for interacting with the dataset in subsequent Pulumi code or via the Google Cloud Console or CLI.

    You'll want to ensure you have the proper permissions to assign roles, and you should replace the members with the actual emails or identifiers for your Google Cloud environment.

    Be mindful that managing access to sensitive data such as AI training data should always be done with great care, following your organization's security policies and best practices for IAM.