1. Implementing Access Policies for Sensitive AI Training Data


    To implement access policies for sensitive AI training data, one commonly used cloud service is Google Cloud's BigQuery. BigQuery is well-suited for handling large datasets often used in AI and machine learning. An important feature of BigQuery is its Row-Level Security (RLS), which allows you to control access to individual rows within a dataset table based on the user's access level.

    Let's say you have a BigQuery dataset and you want to restrict access to sensitive rows of a table. You'd use the RowAccessPolicy resource provided by Google BigQuery to define conditions under which a set of users can access certain rows within a table.

    Below is a program written in Python that uses Pulumi with the google-native provider to set up a row-level access policy in BigQuery. We'll make use of the RowAccessPolicyIamPolicy resource, which allows us to define who has access to the data controlled by the row access policy.

    First, we must define:

    • The project and dataset in BigQuery where the table exists.
    • The table ID to which the row-level access policy will apply.
    • A condition that defines the row-level access control. We'll say users with a specific "analyst" role have access to rows where the column is_sensitive is set to false.

    Here's how you could implement such a policy:

    import pulumi import pulumi_google_native.bigquery.v2 as bigquery # Configurations for our resources project_id = 'my-gcp-project' dataset_id = 'my_bigquery_dataset' table_id = 'my_sensitive_data_table' # Define the row access policy. In real-world scenarios, this should be crafted according to the actual data governance needs. row_access_policy_id = 'analyst_sensitive_data_policy' row_access_policy_conditions = { 'row_access_policy': { 'filterPredicate': 'is_sensitive = FALSE' # This is the condition; only non-sensitive data rows are accessible. } } # Create a Row Access Policy row_access_policy = bigquery.RowAccessPolicy( f"{row_access_policy_id}-row-access-policy", project=project_id, datasetId=dataset_id, tableId=table_id, row_access_policy_id=row_access_policy_id, args=row_access_policy_conditions ) # IAM policy to grant specific users the roles/bigquery.dataViewer role to view rows allowed by the policy iam_policy = bigquery.RowAccessPolicyIamPolicy( f"{row_access_policy_id}-iam-policy", project=project_id, datasetId=dataset_id, tableId=table_id, row_access_policy_id=row_access_policy_id, bindings=[{ 'role': 'roles/bigquery.dataViewer', # The role to grant 'members': [ # An array of members who will get this role. 'user:jane.doe@example.com', # Replace with the actual user email 'group:analysts@example.com', # Replace with the actual group email ], }] ) # Export the IAM policy ID so we can easily reference it later pulumi.export('iam_policy_id', iam_policy.name)

    In this program:

    • We've established a policy with an SQL-like predicate, stating that only rows where is_sensitive = FALSE can be viewed.
    • Using the IAM policy, we've specified which users and groups should have the roles/bigquery.dataViewer role that allows viewing said rows.
    • Finally, we're exporting the IAM policy's name for reference.

    To use the above code, you should replace "my-gcp-project", "my_bigquery_dataset", "my_sensitive_data_table", "jane.doe@example.com", and "analysts@example.com" with your actual project ID, dataset ID, table ID, and the actual emails of the users and groups you're setting access for.

    Ensure you've installed and configured Pulumi and the appropriate credentials for Google Cloud. Once the Pulumi program is executed, it will apply the configuration and establish the row-level security policy on your BigQuery dataset table, ensuring only authorized access to sensitive data.