Explanation:
The question requires preventing and detecting security policy violations across Google Cloud environments specifically for Gemini in Vertex AI integration, with a small security team (5 people) managing 200 developers. Option A (Apply organization policy constraints with Security Health Analytics) provides proactive prevention through policy enforcement and automated detection/monitoring of drifts, which is scalable for the team size. Option D (Apply predefined AI-recommended security posture template for Gemini in Vertex AI in Security Command Center) directly addresses the Gemini/Vertex AI context with Google's specialized, automated security templates, ensuring best practices for that specific service. While E (least privilege IAM) is a good general practice, it's more about access control than comprehensive policy enforcement and detection. B (publish policies) lacks automated enforcement/detection. C (Cloud Logging with Cloud Run) is reactive rather than preventive. The community consensus (86% for AD) and detailed reasoning support A and D as the optimal combination for this scenario.
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Your organization aims to be a market leader in software innovation by providing numerous Google Cloud environments for 200 developers to integrate Gemini in Vertex AI into applications. With a five-person security team, you need to prevent and detect security policy violations across these environments. What should you do? (Choose two.)
A
Apply organization policy constraints. Detect and monitor drifts by using Security Health Analytics.
B
Publish internal policies and clear guidelines to securely develop applications.
C
Use Cloud Logging to create log filters to detect misconfigurations. Trigger Cloud Run functions to remediate misconfigurations.
D
Apply a predefined AI-recommended security posture template for Gemini in Vertex AI in Security Command Center Enterprise or Premium tiers.
E
Implement the least privileged access Identity and Access Management roles to prevent misconfigurations.
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