AI Response Permission Caching for Context-Aware Access Control
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Solution Overview
Problem
Current systems lack efficient ways to cache and reuse responses from AI agents while maintaining access permissions across synthesized data responses, leading to resource-intensive and inefficient querying of multiple data sources with varying access controls.
Innovation Solution
A system that derives and applies dynamic permissions to AI agent outputs, caching them with source chunks and using a rules engine to ensure access is granted based on user context and document sensitivity, allowing for permission-aware caching and reuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the system caches and reuses AI agent outputs, then processing costs and resource consumption are reduced, but maintaining access permissions across synthesized data responses becomes complex and difficult to manage
Solution Approach 1:
The system performs preliminary permission derivation when caching AI agent outputs, storing the derived permissions alongside the cached results. This preliminary action eliminates the need to re-evaluate permissions during reuse, resolving the contradiction by preparing permission information in advance rather than managing it dynamically during each access.
Solution Approach 2:
The system introduces an execution engine as an intermediary layer between the cache storage and the AI agent outputs. This intermediary automatically derives and manages permissions for cached results, handling the complexity of permission management internally while allowing the caching mechanism to function efficiently without direct user intervention.
2Reliability
If the system queries multiple data sources to ensure access control based on data sensitivity, then security and compliance are maintained, but resource intensity and processing time increase significantly
Solution Approach 1:
The system performs preliminary permission checks and derives access permissions when data is first cached, rather than repeating these checks during each subsequent query. This preliminary evaluation of access controls maintains security requirements while eliminating redundant permission verification during cache reuse operations.
Solution Approach 2:
The system creates cached copies of AI agent outputs with embedded permission information, allowing these copies to be reused without querying the original multiple data sources. The copied results include sufficient permission metadata to ensure access control is maintained without requiring re-verification of the source data permissions.
3Measurement precision
If the system derives permissions dynamically based on user context and document sensitivity, then access control accuracy is improved, but the complexity of permission derivation and validation increases
Solution Approach 1:
The execution engine serves as an intermediary that automatically performs the complex task of deriving permissions based on user context and document sensitivity. This intermediary handles the sophisticated permission derivation logic internally, providing accurate permission determination without exposing the complexity to users or requiring manual intervention.
Solution Approach 2:
The system implements self-service permission derivation where the execution engine automatically evaluates user context, analyzes document sensitivity, and determines appropriate permissions without external input. This automated self-service approach maintains high permission accuracy while managing derivation complexity within the system's automated processes.
Data Source
AI summary
The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions level. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of resources that are ingested for use in the artificial intelligence (AI) agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.