Your DevOps engineering team is standardizing the use of Gemini in the CLI and development environments to synthesize complex gcloud automation scripts and inspect deployment configurations across your Google Cloud estate. Your organization enforces strict data governance and enterprise security requirements: Sensitive operational context and prompts transmitted to the AI service must be protected against data exfiltration and restricted to trusted boundaries. Developers must be granted the least-privilege Identity and Access Management (IAM) permissions necessary to generate command-line code and chat completions without granting access to configure organizational AI logging or administrative settings. Developer identities must be managed securely at scale according to Google Cloud architectural best practices. Which combination of IAM roles and security controls should you implement?

Enable `cloudaicompanion.googleapis.com`, add the API to a VPC Service Controls perimeter, and assign the `roles/cloudaicompanion.user` and `roles/serviceusage.serviceUsageConsumer` roles to an application developer Google Group.: ### What the Solution Is This architecture establishes secure, governed access to Gemini for Google Cloud assistance tools (such as Gemini in the CLI and IDE integrations) by leveraging native **VPC Service Controls (VPC-SC)** perimeters alongside least-privilege **Identity and Access Management (IAM)** group-based bindings. ### How It Meets the Requirements - **Protects enterprise data & mitigates exfiltration**: Enclosing the project and adding the Gemini for Google Cloud API (`cloudaicompanion.googleapis.com`) to a **VPC Service Controls service perimeter** prevents data exfiltration by creating a strict security perimeter around the Google-managed AI backend. - **Enforces least privilege**: Granting `roles/cloudaicompanion.user` provides the minimum permissions required for developers to interact with the LLM (e.g., `cloudaicompanion.companions.generateChat` and `cloudaicompanion.companions.generateCode`) while withholding administrative privileges such as `roles/cloudaicompanion.settingsAdmin`. - **Scalable identity management**: Assigning `roles/cloudaicompanion.user` and `roles/serviceusage.serviceUsageConsumer` to a centralized **Google Group** aligns with best practices by avoiding individual role bindings and simplifying auditability. ### Key Advantages and Capabilities - **Service perimeter isolation**: Restricts API calls to authorized networks and prevents unauthorized data movement across organizational boundaries. - **Separation of duties**: Keeps administrative configuration of prompt/response logging isolated to platform security engineers. - **Standardized API enablement**: Leverages the `cloudaicompanion.googleapis.com` service endpoint required to process CLI conversations and script synthesis. ### Why This Is Optimal This solution directly satisfies enterprise governance requirements by securing data in transit and at the boundary using VPC-SC while enforcing principle-of-least-privilege access using dedicated predefined IAM roles applied to identity groups.

Gemini Cli Secure Ai Governance Iam Vpc Sc | Professional Cloud DevOps Engineer

Gemini Cli Secure Ai Governance Iam Vpc Sc

Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.

Your DevOps engineering team is standardizing the use of Gemini in the CLI and development environments to synthesize complex gcloud automation scripts and inspect deployment configurations across your Google Cloud estate.

Your organization enforces strict data governance and enterprise security requirements:

Sensitive operational context and prompts transmitted to the AI service must be protected against data exfiltration and restricted to trusted boundaries.
Developers must be granted the least-privilege Identity and Access Management (IAM) permissions necessary to generate command-line code and chat completions without granting access to configure organizational AI logging or administrative settings.
Developer identities must be managed securely at scale according to Google Cloud architectural best practices.

Which combination of IAM roles and security controls should you implement?

Enable discoveryengine.googleapis.com, assign the roles/discoveryengine.admin role to a developer Google Group, and configure Access Context Manager to allow unrestricted public IP ingress.

Enable cloudaicompanion.googleapis.com, add the API to a VPC Service Controls perimeter, and assign the roles/cloudaicompanion.user and roles/serviceusage.serviceUsageConsumer roles to an application developer Google Group.

Grant the roles/cloudaicompanion.settingsAdmin role directly to individual developer user accounts, and configure Private Google Access without a VPC Service Controls perimeter.

Grant the basic roles/editor role at the organization node to developers and disable the cloudaicompanion.googleapis.com API on development projects.

Professional Cloud DevOps Engineer

Gemini Cli Secure Ai Governance Iam Vpc Sc

Related questions