A Simple Architecture for Enterprise Large Language Model Applications based on Role based security and Clearance Levels using Retrieval-Augmented Generation or Mixture of Experts

TL;DR

This paper introduces a straightforward architecture for enterprise LLM applications that incorporates role-based security and clearance levels, utilizing Retrieval-Augmented Generation and Mixture of Experts to prevent information leakage.

Contribution

It presents a novel architecture combining RAG and MoE with role-based security for secure enterprise LLM deployment.

Findings

Effective filtering of documents and experts based on user roles and clearance levels

Flexible architecture supporting RAG, MoE, or both

Enhanced security in enterprise LLM applications

Abstract

This study proposes a simple architecture for Enterprise application for Large Language Models (LLMs) for role based security and NATO clearance levels. Our proposal aims to address the limitations of current LLMs in handling security and information access. The proposed architecture could be used while utilizing Retrieval-Augmented Generation (RAG) and fine tuning of Mixture of experts models (MoE). It could be used only with RAG, or only with MoE or with both of them. Using roles and security clearance level of the user, documents in RAG and experts in MoE are filtered. This way information leakage is prevented.