We’re in the middle of scaling out a conversational analytics pilot and running into governance questions that our traditional BI setup wasn’t designed to answer. The core issue is that natural language queries bypass all the menu-driven access patterns we built our security model around. Users can now ask anything in plain English, which means we’re generating SQL we’ve never seen before, hitting tables in combinations we never anticipated.
Our current row-level security is enforced in the BI tool’s presentation layer, not at the database or semantic layer. That worked fine when users clicked through predefined dashboards, but now an LLM is generating queries that go straight to raw tables. We’re evaluating whether to move RLS enforcement down to the database layer or implement attribute-based access control at the semantic layer. The tradeoff seems to be between centralized policy management and performance at scale.
Another tension point is metric definitions. Different teams have been calculating the same KPIs slightly differently across tools for years—Finance’s revenue number doesn’t quite match Sales’ number. We tolerated this in static reporting because humans could reconcile differences in quarterly reviews. But when an AI system returns conflicting answers to the same question depending on which path it takes through our data, users lose trust immediately.
Curious how others have approached this—especially teams that have moved from POC to production with AI-powered analytics. Are you enforcing security at the database, semantic layer, or both? And have you been able to centralize metric definitions in a way that actually sticks across tools?
One thing that caught us off guard: row-level security doesn’t apply to users with admin or contributor permissions in most BI platforms. We had analysts with elevated access for dashboard development, and they could see everything regardless of RLS rules. Had to do a full audit of who had what permissions and strip down access to viewer-only for most users. Worth checking your own permission model before you assume RLS is protecting everything.
The metric definition problem is real. We ended up treating our semantic layer as version-controlled infrastructure—every metric is defined once in code, goes through pull request review, and gets deployed to production with full lineage. It’s extra overhead upfront, but now when Finance changes a calculation, that change propagates everywhere automatically. No more quarterly reconciliation meetings to figure out why numbers don’t match. The key was getting stakeholders to agree on canonical definitions before we built the layer, not after.
We went through this exact transition last year. Moved RLS enforcement from our BI tool down to the data warehouse layer and it was the right call. Query performance stayed consistent because we’re using shared connection pools, and we don’t have to worry about the LLM finding creative ways around application-level filters. The downside is you need tight coordination between your data team and security team—policies have to be defined and tested in the database before analysts can even build reports on top of them.
The audit and monitoring piece is critical and often underestimated. Once you deploy conversational analytics, your LLM is going to generate query patterns you’ve never seen before. We set up real-time anomaly detection on data access logs to catch unusual behavior early—things like a user suddenly querying tables they’ve never touched, or queries hitting restricted datasets outside normal hours. This also helps with compliance, because regulators want proof of who accessed what and when. Immutable audit trails are non-negotiable in regulated environments.
One more consideration: if you’re using Single Sign-On or scheduled deliveries with impersonation, you’ll hit limitations with certain RLS implementations. We had to switch to shared service account logons at the connection pool level for some data sources because the end user’s password wasn’t available to the BI server at execution time. This isn’t obvious until you try to deploy and suddenly scheduled reports start failing. Test your authentication flows early, especially if you’re integrating with enterprise identity providers.