1. Resource Hierarchy for AI Platform Access Control


    To set up a resource hierarchy for AI Platform Access Control, you need to define a structure that clearly differentiates permissions and access controls at different levels of the organization. This can include organization policies, management groups, and role-based access control (RBAC) depending on the cloud provider you're using.

    Assuming we are using Google Cloud Platform (GCP) as the cloud provider for an AI Platform, we would typically manage resource hierarchy with organization policies and RBAC roles using Google Cloud resources like gcp.orgpolicy.Policy for organization policies, and google-native.compute/beta.ServiceAttachmentIamMember for managing access control on specific resources.

    Here is a Pulumi program that creates an organization policy and sets up service attachment IAM members to define access control in a hierarchical way:

    import pulumi import pulumi_gcp as gcp import pulumi_google_native.compute.beta as compute_beta # Organization-level policy that restricts sharing of AI Platform resources # outside the organization. org_policy = gcp.orgpolicy.Policy("aiPlatformRestrictSharingPolicy", constraint="constraints/iam.allowedPolicyMemberDomains", boolean_policy=gcp.orgpolicy.PolicyBooleanPolicyArgs( enforced=True, ), parent=f"organizations/{gcp_organization_id}", ) # IAM member binding for the AI Platform's service attachment, # granting specific roles to a service account. service_attachment_iam_member = compute_beta.ServiceAttachmentIamMember("aiPlatformServiceAttachmentIamMember", service_attachment="projects/my-project/regions/us-central1/serviceAttachments/my-service-attachment", role="roles/editor", member="serviceAccount:my-service-account@my-project.iam.gserviceaccount.com", ) # Export the organization policy ID and service attachment IAM member ID pulumi.export("policy_id", org_policy.name) pulumi.export("service_attachment_iam_member_id", service_attachment_iam_member.name)

    In the code above:

    • We create an organization policy using gcp.orgpolicy.Policy (GCP docs) that enforces a restriction on sharing AI Platform resources only within the organization by setting the constraint to constraints/iam.allowedPolicyMemberDomains and making it enforced.

    • We then create a service attachment IAM member using compute_beta.ServiceAttachmentIamMember (GCP docs) that binds a serviceAccount to a role on a specific service attachment, effectively setting who has what level of access to the AI Platform resources.

    • Finally, we export both the organization policy ID and the service attachment IAM member ID using pulumi.export. These exported values can be used in other parts of your infrastructure as needed.

    This structure ensures that AI Platform resources are used in compliance with your organization's access and compliance standards. You can add more policies or IAM member bindings to create a comprehensive resource hierarchy to protect and manage your AI Platform environment effectively.