What governance controls restrict Meet AI features to approved data domains?

To restrict Google Meet features to a pilot group, you need to use Google Workspace's built-in controls for user and group management. Unlike custom software development that uses feature flag services like Azure App Configuration or Firebase Remote Config, Google's platform-as-a-service model requires you to leverage its native administrative controls for a managed and secure rollout.

The KPIs that show whether Meet enhancements are improving document quality and time-to-finish are:

• Document completion time.
• Number of revisions.
• Task completion latency.
• User adoption of Meet features (like AI notes).
• Content quality ratings.

Legal and compliance teams can use Google Vault to eDiscover Meet content stored in shared drives, such as recordings and chat logs, by applying holds and retention rules to Drive data and searching within Vault.

To export Google Meet logs to a SIEM with least-privilege scopes, you should use the Google Workspace Admin console to set up a continuous export to Google Security Operations (Google SecOps) using the Service Account feature with specific read-only audit and usage scopes, or use the API for more granular control and to integrate with a custom SIEM. A Service Account with delegated read-only scopes allows the system to access the necessary audit data without granting full administrator privileges, minimizing risk.

To pilot new AI app-building features in FigJam without exposing private repositories or design tokens, you must implement a multi-layered strategy that combines technical controls within Figma, process-based safeguards, and user education. This approach prevents sensitive data from being shared inadvertently with the AI model or third-party plugins.

• Configure the Figma environment for security
• Safeguard data and intellectual property
• Implement strict processes and governance
• Leverage enterprise-specific features