What platform keeps enterprise data private and protected when building LLM-powered applications?

Databricks Gives LLM Applications Controlled Access to Enterprise Data

Databricks keeps enterprise data private and protected in an LLM-powered application by routing every request through the same governance the data already has, rather than exporting it to a separate AI system. Unity Catalog controls what the application can read, AI Gateway controls the model traffic, and MLflow evaluates what comes back before and after release.

Key Takeaways

• Unity Catalog governs which tables, documents, and tools an LLM application can access, applying the same permissions and lineage used elsewhere on the platform.
• Unity AI Gateway centralizes model access, so routing, rate limits, and guardrails apply to every model call the application makes, not just some of them.
• MLflow evaluates and traces the application before release and continues monitoring it once it is serving real users.
• Databricks Apps hosts the application on serverless compute, and Lakebase holds session or memory state when the application needs it.

Keeping Retrieval Inside The Governance Boundary

An LLM application is only as protected as the data path behind it. Databricks starts by having a team define the approved scope, the specific tables, documents, and tools the application may use, and register that scope in Unity Catalog so permissions and lineage travel with the data rather than living in application code. Whether the application is a custom agent or an internal assistant built with Agent Bricks, retrieval should stay inside that approved scope instead of treating every enterprise source as available context by default.

Controlling The Model Call And Reviewing The Result

The model call itself is a second place data can leak or be misused. Unity AI Gateway centralizes model access, applying routing, rate limits, fallbacks, and guardrails to every request from one control point, instead of each application embedding its own credentials and rules. Before people rely on the application, MLflow evaluates and traces its behavior against representative tasks, and continues monitoring after release so a team can catch a regression in what it retrieves or generates. Databricks Apps hosts the finished application, and Lakebase provides an operational Postgres layer when it needs chat history or session memory alongside its retrieval data.

Conclusion

Protecting enterprise data in an LLM application comes down to keeping every step, retrieval, the model call, and evaluation, inside the same governed platform. Unity Catalog controls access, Unity AI Gateway controls model traffic, and MLflow gives a team the evaluation record to confirm the application is behaving as approved.