Enterprise RAG Access Control With RBAC-Filtered Retrieval
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Solution Overview
Problem
Existing RAG applications in network controllers do not consider role-based access controls (RBACs) when retrieving vector embeddings or generating outputs, leading to security risks and inaccurate responses in enterprise networks.
Innovation Solution
Implement a system that integrates fine-granular RBAC by generating vector embeddings with associated RBAC metadata, applying metadata-based filters during searches, and tailoring outputs based on user access levels, ensuring accurate and secure responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If RAG applications retrieve vector embeddings without RBAC filters, then response coverage and completeness improve, but security risks increase
Solution Approach 1:
The system performs preliminary RBAC validation by generating embeddings for user queries and comparing them against authorized resource embeddings before retrieving actual data. This pre-filtering mechanism ensures that only authorized information is accessed, preventing security violations while maintaining complete response coverage within permission boundaries.
Solution Approach 2:
The patent introduces an intermediary RBAC validation layer between the user query and the vector database retrieval process. This intermediary component validates authorization by comparing query embeddings with authorized resource embeddings, acting as a mediator that filters access based on RBAC policies without blocking legitimate information retrieval.
2Reliability
If RAG applications apply fine-granular RBAC filters during vector embedding retrieval, then security posture improves, but response accuracy and relevance may deteriorate
Solution Approach 1:
The system performs preliminary RBAC validation by generating embeddings for user queries and comparing them against authorized resource embeddings before retrieving actual data. This pre-filtering mechanism ensures that only authorized information is accessed, preventing security violations while maintaining complete response coverage within permission boundaries.
Solution Approach 2:
The system incorporates feedback loops where retrieval results are validated against RBAC policies, and the validation outcomes feed back into the retrieval process. This feedback mechanism ensures that security constraints are continuously enforced while allowing the system to learn and adapt to provide accurate responses within authorized boundaries.
Data Source
AI summary
The techniques described herein enable fine-granular role-based access controls for retrieval augmented generation based large language models in enterprise systems. Existing techniques for implementing LLMs in network controller and network management software do not offer differentiated access to data input to the LLM, resulting in the users of the LLM receiving the same results, including results the user may not be authorized to access. This introduces security risks to the enterprise network. The techniques described herein provide mechanisms that remove the security risks and ensure that an LLM receives context data that is filtered and tailored based on the level of access the user has within the enterprise network.