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Privacy, security, and responsible use of Copilot for Real-Time Intelligence (preview)

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In this article, learn how Copilot for Real-Time Intelligence (preview) works, how it keeps your business data secure and adheres to privacy requirements, and how to use generative AI responsibly. For an overview of these topics for Copilot in Fabric, see Privacy, security, and responsible use for Copilot (preview).

With Copilot for Real-Time Intelligence in Microsoft Fabric and other generative AI features in preview, Microsoft Fabric brings a new way to transform and analyze data, generate insights, and create visualizations and reports in Real-Time Intelligence and the other workloads.

Data use of Copilot for Real-Time Intelligence

Copilot for Real-Time Intelligence has access to data, for example the database schema, that is accessible to the Copilot user. The Copilot refers to whichever database is currently connected to the KQL queryset. The Copilot doesn't store any data, and doesn't have access to data that isn't accessible to the Copilot user.

Evaluation of Copilot for Real-Time Intelligence

Following a thorough research period in which several configurations and methods have been tested, the OpenAI integration method had been proven to generate highest accuracy KQL queries. Copilot doesn't automatically run the generated KQL query, and users are advised to run the queries at their own discretion.

Tips for working with Copilot for Real-Time Intelligence

Copilot translates natural language business questions into KQL queries, based on the underlying dataset column names or schema. We recommend that you provide detailed and relevant requests to the copilot to avoid inaccurate or misleading suggested KQL queries. For example, if you're asking about a specific column, provide the column name and the type of data it contains. If you want to use specific operators or functions, this will also help. The more information you provide, the better the Copilot answer will be. You should also restrict questions to databases that are KQL Database tables or materialized views.

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