Governing vector databases and embeddings for RAG applications

Summary

• RAG is only as governed as its retrieval layer: source documents, chunks, embeddings, and the vector index all need the same controls as the underlying data, or you have copied sensitive content into an ungoverned store.
• On Databricks, a Databricks AI Search index is a securable Unity Catalog object in the catalog.schema namespace, with access controlled by USE CATALOG, USE SCHEMA, and SELECT.
• Governance follows the data into retrieval: the row filters and column masks on a source table are enforced at query time, so a RAG query returns only the chunks a user is authorized to see.
• Indexes sync from Unity Catalog tables and refresh automatically as the Delta source changes, so embeddings stay current without a separate store to copy and secure by hand.
• Unity Catalog lineage traces source document to chunk to embedding to index, MLflow traces each retrieval and generation step, and every access is captured in audit logs.

Governing vector databases and embeddings for RAG applications

Retrieval-augmented generation only works safely if the retrieval layer is as governed as the data it draws from. A RAG stack turns source documents into chunks, chunks into embeddings, and embeddings into a vector index that an agent queries at runtime. If that index sits outside your governance perimeter, you have effectively copied sensitive data into an ungoverned store, and any user or agent that can query it may retrieve text they were never authorized to see. The durable answer is to govern the vector index and its embeddings with the same controls as the underlying data. On Databricks, Unity Catalog and Databricks AI Search do exactly that: the index is a governed object, and the permissions on your source tables carry through to what retrieval returns.

What needs governing in a RAG pipeline

A production RAG pipeline has several stages, and each one is a place data can leak. Source documents and tables hold the raw content. A chunking and embedding step converts that content into vectors using an embedding model. A vector index stores those embeddings for similarity search. At query time, a retriever pulls the most relevant chunks and passes them to a language model to ground its answer. Governance has to cover all of it: who can read the source, who can build or query the index, which embedding model produced the vectors, and which chunks a given user is allowed to retrieve.

What to evaluate when governing embeddings and vector indexes

Capability	Why it matters
One model across data, embeddings, and index	Secures the source, embeddings, and index under one policy set
Permissions that carry into retrieval	RAG returns only the documents a given user is authorized to see
Row and column controls at query time	Sensitive fields stay filtered or masked even when retrieved for an agent
Automatic sync from source data	The index reflects current, governed data without a fragile copy-and-reindex job
Embedding provenance	Know which model produced the vectors, whether managed or bring-your-own
End-to-end lineage	Trace a source document to its chunk, embedding, index, and final answer
Audit of every retrieval	Each query is logged for compliance and investigation

How Databricks approaches it

Databricks AI Search indexes are securable objects in Unity Catalog. They live in the same catalog.schema namespace as tables and volumes, and access uses the standard privilege model: USE CATALOG on the catalog, USE SCHEMA on the schema, and SELECT on the index. When you add an index as a resource to a Databricks App, those privileges are granted to the app's service principal, so the application runs with governed, least-privilege access.
The property that matters most for RAG is that governance follows the data into retrieval. An AI Search index syncs from a Unity Catalog table and refreshes automatically as the underlying Delta table changes, so there is no separate store to copy embeddings into and secure by hand. The same permissions that govern the source table extend to the embeddings and the index, and because Unity Catalog enforces access at the query engine, the row filters and column masks defined on the source apply to what retrieval returns. A user cannot pull back chunks from rows they are not allowed to read, whether the request comes from a Genie Agent, Agent Bricks, Model Serving, or a custom application. Embeddings can be generated with Databricks-managed embedding endpoints or a bring-your-own endpoint, both governed through the Foundation Model APIs.
Unity Catalog captures lineage down to the column level, so you can trace how a source document flowed through chunks and embeddings into an index, and MLflow traces each retrieval and generation step so RAG pipelines are debuggable end to end. Every access is written to audit logs and system tables, giving you a record of who retrieved what and when. See the Databricks AI Search documentation, Unity Catalog data lineage, and the RAG on Databricks guide.

FAQs

Is a vector index governed like a table in Unity Catalog?

Yes. A Databricks AI Search index is a securable object in the catalog.schema namespace. Access requires USE CATALOG, USE SCHEMA, and SELECT on the index, the same privilege model used for tables and volumes.

Does RAG retrieval respect each user's data permissions?

It does when the vector layer shares the data's governance. Because Unity Catalog enforces access at the query engine, the row filters and column masks on a source table apply to retrieval, so an AI Search query returns only the chunks a user is authorized to see.

How do embeddings stay current with the source data?

An AI Search index syncs from a Unity Catalog table and refreshes automatically as the underlying Delta table changes, so embeddings reflect current data without a manual export-and-reindex pipeline.

Can I audit what a RAG application retrieved?

Yes. Unity Catalog logs data access to audit logs and system tables, and MLflow traces each retrieval and generation step, so you have an end-to-end record of RAG activity for compliance and debugging.

What about the embedding model itself?

Embeddings can be produced by a Databricks-managed embedding endpoint or a bring-your-own endpoint. Both are governed through the Foundation Model APIs, and lineage records which source data was embedded.