Best Practices to Restrict Gen AI Model Deployment Using IAM Conditions
Exhibit
Refer to the exhibit.
```json
{
"bindings": [
{
"role": "roles/aiplatform.user",
"members": ["user:admin@example.com"]
}
]
}
```A company wants to ensure only authorized users can deploy gen AI models. The current policy allows all users in the domain. What is the best practice to restrict deployment?
Quick Answer
The best practice to restrict generative AI model deployment is to add an IAM condition that limits deployment to authorized users based on attributes like user role, project, or resource tags. This approach is correct because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the need to restrict deployment while preserving current user access. On the Google Cloud Generative AI Leader exam, this concept tests your understanding of attribute-based access control (ABAC) versus broad IAM policies—a common trap is assuming you must revoke domain-wide permissions or assign a new role, when a conditional constraint is the precise, least-privilege solution. For memory, think of the “Condition, Not Removal” rule: you never delete access; you simply gate it with a condition.
⚠ Common exam trap
Google Cloud often tests the misconception that organizational policies (Option D) are the catch-all for access control, but they are designed for resource-level governance (e.g., disabling service creation), not for user-specific deployment restrictions, which require IAM conditions.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add condition to restrict deployment
Adding a condition to restrict deployment (e.g., using IAM conditions in Google Cloud's attribute-based access control) allows you to limit model deployment to only authorized users based on attributes like user role, project, or resource tags. This is the best practice because it enforces fine-grained access control without removing existing permissions or adding unnecessary roles, directly addressing the requirement to restrict deployment while maintaining existing user access.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the binding
Why it's wrong here
Removing the binding removes all access, which is too restrictive.
- ✗
Add more roles
Why it's wrong here
Adding roles does not restrict; it grants additional permissions.
- ✓
Add condition to restrict deployment
Why this is correct
Conditions in IAM allow policies like requiring a specific IP range or MFA for deployment actions.
- ✗
Use organizational policies
Why it's wrong here
Organizational policies set constraints but are not as granular as IAM conditions for specific actions.
Go deeper
Related to this question
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Same concept, more angles
1 more way this is tested on Generative AI Leader
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An organization uses an IAM policy for Vertex AI as shown. A security audit reveals that engineer@example.com deployed a model that inadvertently exposed sensitive data. What is the most likely reason this happened?
medium- A.Audit logging is not enabled for DATA_WRITE events.
- B.The admin user did not review the deployment.
- ✓ C.The engineer had the aiplatform.user role, which includes permissions to deploy models without additional review.
- D.The policy does not include a separation of duties between development and production.
Why C: The `aiplatform.user` role in Vertex AI includes the `aiplatform.model.deploy` permission, which allows any user with that role to deploy models without requiring additional approvals or administrative review. This lack of a secondary authorization step means the engineer could deploy a model that exposed sensitive data, even if the model had not been properly vetted for data leakage.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.