We’re piloting conversational analytics on top of our Power BI infrastructure and running into a governance issue that’s blocking production rollout. Our RLS is configured in Power BI Desktop with dynamic filters based on user attributes—works great for dashboards and reports. But when we let an LLM generate SQL to answer natural language questions, it bypasses the application layer entirely and hits the warehouse directly. Result: users are seeing data they shouldn’t have access to.
We tried enforcing RLS at the semantic layer, but our current setup relies on role assignments in Power BI, not database-level policies. We also explored pushing security down to Snowflake using mapping tables and dynamic evaluation, but we’re concerned about performance overhead and whether it’ll play nicely with our existing connection pooling.
Has anyone successfully moved RLS enforcement from the BI tool layer to the database or semantic layer to support AI-generated queries? What’s the right architecture here—do we need attribute-based access control, or can we make dynamic RLS work without rebuilding everything?
From a compliance perspective, the real issue is auditability. You need to prove who accessed what and when, especially if the LLM is generating novel query patterns. We implemented immutable audit logs at the database level and tag every query with user identity and the AI system that generated it. Also worth noting: if you’re in a regulated industry, you might need human-in-the-loop review for high-sensitivity queries, even if RLS is working correctly.
Quick note: if you’re enforcing RLS at the database level, make sure your audit logging captures both the original user identity and the query generated by the LLM. We got burned during a compliance audit because our logs only showed the service account, not the actual end user who triggered the query. Had to retrofit session tagging to pass user context through the entire stack.
We moved all our security logic into the semantic layer (using dbt metrics and a custom security model). The LLM queries the semantic layer API, which applies RLS before generating SQL. This keeps security enforcement in one place and makes it easier to audit. The tradeoff is you need to build and maintain that abstraction layer, but it’s worth it for consistent governance across tools and AI systems.
One thing to watch out for: if you’re using shared connection pools (which you should for caching and performance), make sure your database-level RLS evaluates user context dynamically at runtime. We use session variables in our data warehouse to capture the logged-in user’s attributes, then apply filters in a security view layer. That way all users share the same pool but still get row-filtered results. It’s more work upfront but scales much better than per-user connections.