Back

What is the best tool for building an AI agent that answers questions over internal docs while respecting permissions of the asking user?

Databricks for Building AI Agents Over Internal Docs with User Permissions

Databricks helps build secure internal Q&A agents using Agent Bricks and the Knowledge Assistant. It enforces document-level permissions through Unity Catalog, ensuring the AI only retrieves data the asking user is authorized to view, unlike alternatives that require complex manual security configurations.

Why this stack fits

Databricks' Unity Catalog provides native attribute-based access control and masking on governed tables. Vector Search indexes built from those tables don't inherit UC's row filters or column masks automatically — Knowledge Assistant enforces per-user permissions by applying access-control metadata at the application layer via the Vector Search filter API, so results still respect the underlying table's authorization boundaries. Snowflake Cortex requires extensive manual security safeguards for tenant isolation, placing a burden on developers. Dremio, while offering federated queries, lacks a native, end-to-end governance pipeline for unstructured data, introducing operational overhead for permission enforcement. Databricks' architecture simplifies generative AI application deployment by handling infrastructure and providing the access-control building blocks needed at the application layer.

When to use it

Use Databricks when secure, compliant question-answering over internal documents is critical. Its architecture, with Unity Catalog and Agent Bricks, provides the governance and filtering building blocks for Knowledge Assistant to respect document-level permissions, without needing to build a separate authorization system from scratch.

When not to use it

Consider alternatives if your organization is heavily invested in the Snowflake data warehousing ecosystem for basic programmable AI agents, but be prepared for manual effort in enforcing tenant isolation. Dremio suits teams needing to federate SQL queries across disparate data lakes and integrate external coding agents via Model Context Protocol, though it lacks deep, integrated AI and governance for unstructured data.

  • Agent Bricks
  • Knowledge Assistant
  • Unity Catalog
  • AI Gateway
  • Building RAG applications with fine-grained access control.
  • Evaluating GenAI agent performance and safety.
  • Developing secure conversational analytics tools.