1. Granular Permissions for AI Analytics Dashboards


    To create granular permissions for AI analytics dashboards within a cloud environment, you would typically use a combination of cloud IAM (Identity and Access Management) policies and specific dashboard services that provide permission settings.

    For the purpose of this walkthrough, I will assume we want to set up granular permissions for dashboards in an environment that may include services like AWS QuickSight or Google Cloud AI Platform, or for tools like Grafana that can run on any cloud service. We'll manage access by defining who can view or edit these dashboards.

    Here's a Python program using Pulumi that assigns permissions to a QuickSight dashboard as an example (since Pulumi Registry Results included aws-native.quicksight.DataSet). In this example, we will use the aws.quicksight.IamPolicyAssignment resource to assign IAM policies to a QuickSight dashboard. Please note that for different environments or tools (like Google Cloud or Grafana), the specifics of the Pulumi resource used will differ but the overarching approach should be similar.

    import pulumi import pulumi_aws as aws # Assuming the AWS account and QuickSight namespace have been set up already. # Replace 'your-account-id' with your AWS Account ID and 'default' with your QuickSight namespace. aws_account_id = 'your-account-id' quicksight_namespace = 'default' # Define an IAM Policy that outlines the permissions granted. This IAM policy should # be crafted according to the specific permissions you desire to grant. # For more information on QuickSight actions: https://docs.aws.amazon.com/quicksight/latest/APIReference/API_Operations.html quicksight_policy = aws.iam.Policy("quicksightPolicy", description="IAM Policy for QuickSight dashboard permissions", policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "quicksight:DescribeDashboard", "quicksight:ListDashboards", "quicksight:DescribeDataSet", "quicksight:DescribeDataSource", "quicksight:ListDataSets", "quicksight:PassDataSet", "quicksight:PassDataSource" ], "Resource": "*" }] }""" ) # Define an IAM policy assignment in QuickSight that applies the defined policy to users or groups quicksight_policy_assignment = aws.quicksight.IamPolicyAssignment("quicksightPolicyAssignment", aws_account_id=aws_account_id, namespace=quicksight_namespace, assignment_name="dashboard-read-only-permissions", assignment_status="ENABLED", policy_arn=quicksight_policy.arn, identities={ "users": [ "user1@example.com", "user2@example.com" ] } ) # Export the assignment name to access it easily after deployment pulumi.export('policy_assignment_name', quicksight_policy_assignment.assignment_name)

    In this code:

    • We create an IAM Policy (quicksightPolicy) that specifies the actions that our users are allowed to perform, such as viewing dashboards and datasets within QuickSight.
    • We use the aws.quicksight.IamPolicyAssignment resource to apply the policy to specific users within our QuickSight namespace. You can specify users by their email associated with the QuickSight account.
    • The policy ARN (policy_arn) created above is referenced in the policy assignment for users 'user1@example.com' and 'user2@example.com'.
    • Finally, we use pulumi.export to output the name of our policy assignment so we can reference it later if needed.

    To adapt this example to other environments, replace the AWS-specific resource with the relevant Pulumi resource for your cloud provider or the dashboard tool you're using.

    Please ensure your Pulumi CLI and AWS provider are configured with the appropriate credentials and settings to run this program. This code should work as a template and can be run directly after replacing placeholder values with your actual account information and desired user emails.

    Remember to always follow your organization's policy regarding the principle of least privilege when assigning permissions. This means granting only the permissions necessary to perform a given task, no more, no less.