How to Stop AI Analytics Tools From Leaking Data Across Users

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As developers embed natural language analytics into products and dashboards, a critical security gap has emerged: AI assistants often connect to databases through a single powerful service account, bypassing the tenant-level access controls enforced in the regular app interface. This means a user asking a simple business question could inadvertently trigger queries that return data belonging to other customers or roles. The root problem is a broken identity chain, where the database sees the AI service's credentials rather than the individual user's permissions. A safe architecture treats the AI as an untrusted query planner, with a dedicated analytics gateway responsible for verifying user identity, enforcing row-level security, and scoping every query before it reaches the warehouse. This layered approach ensures that tenant filters, role restrictions, and audit trails remain intact regardless of whether a question comes through the standard UI or an AI-generated SQL path.

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