Google Cloud has outlined a way for organisations to analyse and govern Gemini Enterprise deployments through BigQuery by moving application telemetry and audit data into Google's data warehouse. The setup is aimed at administrators overseeing large Gemini Enterprise roll-outs, where prompt logs, model responses, user activity and audit records can create a heavy monitoring burden. It combines continuous log routing for detailed records with batch exports for aggregate usage metrics. Under this model, telemetry is split across five BigQuery tables: user prompts, model outputs, user activity records, cloud audit activity logs and cloud audit data access logs. A separate batch export table holds aggregated seat and engagement metrics. The proposed ingestion pipeline uses Cloud Logging log router sinks to stream conversational data into BigQuery, while a separate audit logging path captures administrative actions and data access activity tied to Discovery Engine services. Admin Activity logs are enabled by default, while Data Access logs must be turned on separately in Google Cloud IAM settings for the Discovery Engine API. Prompt and response logging must also be enabled in the Gemini Enterprise admin console before conversational telemetry can be collected. Google positions its built-in AI features, including Conversational Analytics in BigQuery Studio, as the main way to interrogate those records.