LLM Embedding RBAC Using Role-Integrated Document Vectors
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Solution Overview
Problem
Conventional Role-Based Access Control (RBAC) mechanisms are inadequate for handling the unstructured and vectorized nature of data in Large Language Model (LLM) embeddings, leading to potential data breaches and unauthorized access to sensitive information.
Innovation Solution
Implement RBAC-Aware vectorization and embedding retrieval mechanisms that prioritize role context during the vectorization process, integrating role tokens with document vectors through attention layers to ensure precise control over data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional RBAC mechanisms are used for access control, then implementation is simple, but they are inadequate for handling unstructured and vectorized data in LLM embeddings leading to potential data breaches
Solution Approach 1:
The patent segments the access control mechanism into multiple components: traditional RBAC for structured data, and a new embedding-aware RBAC layer for unstructured vectorized data. This segmentation allows each layer to handle its specific data type appropriately, improving overall security without requiring complete system redesign.
Solution Approach 2:
The patent introduces an intermediary embedding-aware RBAC mechanism that sits between the traditional RBAC system and the LLM embedding retrieval process. This intermediary layer translates role-based access rules into embedding space, enabling secure access control for unstructured data while maintaining compatibility with existing RBAC infrastructure.
2Measurement precision
If role context is integrated into document vectors through attention layers, then precise control over data access is achieved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing role context vectors and attention weights during the embedding creation process. This allows the system to perform precise access control checks during retrieval without incurring high computational costs at query time, as the heavy lifting is done in advance during data preparation.
Solution Approach 2:
The patent extends the traditional RBAC attribute space by adding a new dimension for embedding context. Instead of only checking traditional RBAC attributes, the system now operates in an extended attribute space that includes embedding-based role contexts, enabling more precise access control decisions while maintaining a systematic approach to complexity management.
3Reliability
If multiple distinct unstructured natural language documents are generated from the same structured data for different roles, then role-specific access control is improved, but data processing time increases
Solution Approach 1:
The patent creates role-specific copies of the same underlying information in different unstructured document formats, each optimized for specific role requirements. This allows the system to maintain a single source of structured data while generating multiple role-tailored views, improving access control effectiveness without requiring multiple separate data storage systems.
Solution Approach 2:
The patent implements a universal data generation framework that can produce role-specific unstructured documents from a single structured data source. This multi-functional system handles different role requirements through configurable templates and parameters, improving access control while avoiding the need for separate processing pipelines for each role.
Data Source
AI summary
Systems and methods for Role-Based Access Control (RBAC) using Large Language Model (LLM) embeddings are described. In an illustrative, non-limiting embodiment, an Information Handling System (IHS) may include a processor and a memory coupled to the processor. The memory may store program instructions that, upon execution, generate a plurality of distinct unstructured natural language documents from a same portion of structured data of an enterprise, with each document created for a corresponding role in the enterprise. The IHS may concatenate a role context vector that defines an access privilege for a document with a document vector associated with the document to produce a role-integrated document vector. The IHS may also apply pre-attention and post-attention layers to the role context vector to manage access control during document retrieval based on user roles.