LLM Data Escrow Service for Automated Dataset Access
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Solution Overview
Problem
Current data access processes for large language models are cumbersome, requiring users to manually request access to multiple datasets, which leads to increased friction and long approval wait times as enterprises implement stricter access controls.
Innovation Solution
A data escrow service utilizing a large language model (LLM) to interpret natural language prompts, determine required datasets, and generate access requests, thereby streamlining the data access process and reducing the need for broad, preemptive access requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data access requests are used with stricter access controls, then data security is improved, but access time and operational complexity increase
Solution Approach 1:
The system enables self-service by having the LLM automatically interpret natural language queries, identify required datasets, generate access requests, and track approval status without requiring manual intervention from data administrators. Users can independently complete the entire data access workflow.
Solution Approach 2:
The LLM acts as an intermediary between users and the data access control system. It translates user-friendly natural language queries into formal access requests, monitors approval status, and provides updates, thereby mediating the interaction between users and strict access controls.
2Reliability
If manual data access requests are used with stricter access controls, then data security is improved, but ease of operation deteriorates
Solution Approach 1:
The system enables self-service by having the LLM automatically interpret natural language queries, identify required datasets, generate access requests, and track approval status without requiring manual intervention from data administrators. Users can independently complete the entire data access workflow.
Solution Approach 2:
The patent replaces the mechanical manual process of data access requests with an automated LLM-based system. The LLM handles query interpretation, dataset identification, access request generation, and status tracking, eliminating the need for manual administrative intervention and simplifying the operation.
3Speed
If broad preemptive access requests are used, then access speed is improved, but data security and precision deteriorate
Solution Approach 1:
Instead of requesting broad preemptive access to all datasets, the system uses partial action by having the LLM analyze the specific natural language query and identify only the minimal required datasets needed to answer the question. This targeted approach maintains security while enabling rapid access.
Solution Approach 2:
The system implements feedback loops where the LLM continuously monitors the approval status of access requests and provides updates to users. The LLM also iteratively refines dataset selections based on query analysis, ensuring that only necessary datasets are accessed while maintaining fast retrieval speeds.
Data Source
AI summary
A method for a data escrow service includes receiving, from a user device, an access query requesting generation of an access request for allowing a user associated with the user device access to one or more datasets of a plurality of datasets. The access query includes natural language text describing information associated with the one or more datasets of the plurality of datasets. The method includes determining, using a large language model (LLM) and the access query, the one or more datasets. The method includes generating the access request requesting the user gain temporary access to the one or more datasets. The method also includes providing, to the user device, a notification of the one or more datasets and the access request. The notification does not include any data from the one or more datasets.