# Policy-aware Vector Search: A Vision for Fine Grained Access Control in Vector Databases

arXiv: https://arxiv.org/abs/2606.19803 · HTML: https://mlsanity.com/paper/2606.19803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 3 of 3)

- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [Survey of Filtered Approximate Nearest Neighbor Search over the Vector-Scalar Hybrid Data](https://mlsanity.com/paper/2505.06501/) (2025)

## Abstract (arXiv)

Vector databases are increasingly used in security sensitive contexts with Retrieval Augmented Generation and organizational AI pipelines; however, their security capabilities remain limited. Specifically, Fine-grained Access Control (FGAC) which is required to ensure that data access adheres to user-specific policies is not fully supported in modern vector databases. Unlike relational databases, vector databases combine structured and unstructured attributes to provide semantic, approximate query results, which complicates FGAC implementation. This creates an inherent tension between enforcing FGAC policies correctly, achieving high ANN search recall and maintaining low query latency. In this paper, we present a vision for Policy-aware Vector Search by formalizing the FGAC policy model in vector databases as well as the enforcement problem. We compare various enforcement strategies, present preliminary findings, and identify key open challenges for future research in policy-aware vector search.