mlsanityData to 7 Oct 2026

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

arXiv 2407.06718 ↗First posted 9 Jul 2024cs.AIcited by 0

On the map: Retrieval-augmented generation → Privacy and security of RAG

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 26 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

9 of 26 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  3. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  4. 2023LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Pluginscited by 49
  5. 2024Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Modelscited by 42
  6. 2024A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directionscited by 33
  7. 2023A Security Risk Taxonomy for Prompt-Based Interaction With Large Language Modelscited by 12
  8. 2024Mapping LLM Security Landscapes: A Comprehensive Stakeholder Risk Assessment Proposalcited by 8
  9. 2016Sparsity-driven weighted ensemble classifiercited by 1

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  1. 2025Secure Multifaceted-RAG for Enterprise: Hybrid Knowledge Retrieval with Security Filtering
  2. 2024Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
  3. 2026Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
  4. 2024RAG Does Not Work for Enterprises
  5. 2024Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases
  6. 2025RAG Security and Privacy: Formalizing the Threat Model and Attack Surface
  7. 2025Enterprise AI Must Enforce Participant-Aware Access Control
  8. 2026SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems