# ARBITER: AI-Driven Filtering for Role-Based Access Control

arXiv: https://arxiv.org/abs/2512.20535 · HTML: https://mlsanity.com/paper/2512.20535/

## 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 4 of its 13 references that are in the map.
- First posted on arXiv 23 Dec 2025.

## Placed in

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

## Builds on (most cited first, 4 of 4)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [NeMo Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails](https://mlsanity.com/paper/2310.10501/) (2023)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [Permissioned LLMs: Enforcing Access Control in Large Language Models](https://mlsanity.com/paper/2505.22860/) (2025)

## Abstract (arXiv)

Role-Based Access Control (RBAC) struggles to adapt to dynamic enterprise environments with documents that contain information that cannot be disclosed to specific user groups. As these documents are used by LLM-driven systems (e.g., in RAG) the problem is exacerbated as LLMs can leak sensitive data due to prompt truncation, classification errors, or loss of system context. We introduce our, a system designed to provide RBAC in RAG systems. our implements layered input/output validation, role-aware retrieval, and post-generation fact-checking. Unlike traditional RBAC approaches that rely on fine-tuned classifiers, our uses LLMs operating in few-shot settings with prompt-based steering for rapid deployment and role updates. We evaluate the approach on 389 queries using a synthetic dataset. Experimental results show 85% accuracy and 89% F1-score in query filtering, close to traditional RBAC solutions. Results suggest that practical RBAC deployment on RAG systems is approaching the maturity level needed for dynamic enterprise environments.