Your organization is using Model Garden to maintain a collection of models in a single location and to deploy different types of models in a consistent way. You must ensure that your users can only access the approved models. What should you do?
A.
Configure IAM permissions on individual Model Garden to restrict access to specific models.
B.
Regularly audit user activity logs in Vertex AI to identify and revoke access to unapproved models.
C.
Train custom models within your Vertex AI project, and restrict user access to these models.
D.
Implement an organization policy that restricts the vertexai.allowedModels constraint.
To centrally govern and restrict which AI models (from the Model Garden) can be used within an organization, Google Cloud provides a specific Organization Policy Constraint.9
According to Google Cloud Documentation (Vertex AI Organization Policy Constraints):
"The constraints/vertexai.allowedModels constraint allows you to define a list of allowed models that can be deployed or used within your organization. This includes Google first-party models, open-source models, and third-party models available in the Model Garden.10 By using this policy, you can prevent users from using unvetted or non-compliant models even if they have IAM permissions to use Vertex AI."
How it works:
You define an "Allowlist" of model IDs.
When a user attempts to deploy a model or call an API for a model not on the list, the request is blocked.
This is the most scalable and compliant way to manage AI governance.
Why other options are incorrect:
A is incorrect: Model Garden is a catalog; you cannot apply granular IAM permissions to individual entries within the public catalog itself in the same way you can restrict API usage via Org Policy.
B is incorrect: Auditing is reactive. The requirement is to ensure users can only access approved models (prevention).
[Reference:, Google Cloud Documentation: "Vertex AI organization policy constraints" (https://cloud.google.com/vertex-ai/docs/general/org-policies)., , ]
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