The dangerous part of AI analytics is not that a model may write a bad chart title. It is that one friendly question can turn into a warehouse query your user was never supposed to run.
That risk is growing because builders are adding natural language analytics to products, dashboards, internal tools, support consoles, and agent workflows. Users want to ask, “Which accounts are slipping this month?” and get an answer.
That is useful, and it is a permissions trap.
If your AI analyst connects through one powerful service account, every customer question may inherit the same access. Your app may have perfect tenant checks in the UI, while the AI path quietly bypasses them.
This guide shows how to design AI analytics row-level security so customers can ask useful questions without leaking rows, metrics, or private business context.
