1. BigQuery Data Policies for AI Data Governance


    In order to implement BigQuery Data Policies for AI Data Governance, you would need to create Data Policies in Google BigQuery that control access to datasets based on their classifications. These classifications help enforce governance and compliance policies that might be required for AI systems to ensure sensitive data is managed responsibly.

    Below is a Pulumi program in Python that demonstrates how to create a BigQuery Data Policy on Google Cloud Platform (GCP). The Data Policy includes a dataMaskingPolicy that specifies how certain types of data should be masked when accessed by users without the right permissions.

    We will be using gcp.bigquerydatapolicy.DataPolicy to create the data policy with a predefined masking expression. In addition, we will set IAM policies on the data policy using resources like gcp.bigquerydatapolicy.DataPolicyIamMember, which grants specific roles to a user or a service account.

    Here is a sample Pulumi program to accomplish this:

    import pulumi import pulumi_gcp as gcp # Initialize GCP project and location project = "my-gcp-project" # Replace with your GCP project ID location = "us-central1" # Replace with your GCP location # Create a new BigQuery Data Policy that includes a data masking policy for AI data governance data_policy = gcp.bigquerydatapolicy.DataPolicy("aiDataGovernancePolicy", project=project, location=location, policy_tag="tag/aiSensitiveData", # Replace with the appropriate policy tag data_policy_id="ai-governance-policy", # Replace with your policy ID data_policy_type="DATA_MASKING_POLICY", data_masking_policy=gcp.bigquerydatapolicy.DataPolicyDataMaskingPolicyArgs( predefined_expression="REDACT()" # Expression to specify how the data should be masked ) ) # For assigning a role to a user or service account to manage the data policy iam_member = gcp.bigquerydatapolicy.DataPolicyIamMember("aiDataPolicyAdmin", project=project, location=location, data_policy_id=data_policy.data_policy_id, role="roles/bigquery.dataOwner", # Specifies the role granted member="user:admin@example.com" # Replace with the email of the user or service account ) # Export the name and the ID of the policy created to be used by other processes or referenced in outputs pulumi.export("data_policy_name", data_policy.name) pulumi.export("data_policy_id", data_policy.data_policy_id)

    In the program:

    • We import the required modules from Pulumi, which include the general Pulumi functionality and the Google Cloud provider specifics.
    • We specify the GCP project ID and location where the Data Policy will be applied. Make sure to replace "my-gcp-project" and "us-central1" with your actual project and location.
    • We then define a gcp.bigquerydatapolicy.DataPolicy, which requires various properties matching the ResourceTypeArgs class for the policy such as the project, location, policy tag, and data masking policy to be set up.
    • The data_policy_id is a user-defined identifier for referencing this policy.
    • The data_masking_policy specifies an expression that defines how to mask data. In this case, REDACT() is a simple example where you can put your actual masking logic in practice.
    • Next, we assign a user to manage this data policy by creating a gcp.bigquerydatapolicy.DataPolicyIamMember and grant them the roles/bigquery.dataOwner role. Replace "admin@example.com" with the user or service account that should have this role.

    At the end of our program, we use pulumi.export to output the name and the ID of the Data Policy that we've created so that they can be easily accessed from the Pulumi console.

    This program sets up an elementary BigQuery Data Policy with a data masking policy for controlling access to sensitive AI data. It attaches an IAM policy to define who has the ownership role for this policy. You should tailor the data masking and IAM role assignment as per your specific organizational requirements for AI data governance.