LLM Data Catalog Mediation for Secure, Grounded Analytics
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Solution Overview
Problem
Existing systems using large language models (LLMs) for data interaction face challenges in maintaining data security, access control, and accuracy, with risks of unauthorized access, data breaches, and hallucinations leading to flawed data analyses.
Innovation Solution
A system that restricts LLM access to metadata, implements a grounding mechanism to anchor outputs in structured data, and allows dynamic user control and real-time resource management, ensuring secure and accurate data interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs are allowed to interact directly with data catalogs for natural language queries, then user interaction ease is improved, but data security and access control are worsened
Solution Approach 1:
The patent introduces a data catalog as an intermediary layer between users and the underlying data storage systems. The data catalog provides a structured interface that mediates access to data, allowing natural language queries while enforcing access control policies. This mediator ensures that users can interact with data through convenient natural language without bypassing security mechanisms.
Solution Approach 2:
The system segments the data access pathway into distinct layers: user interface, data catalog layer, and underlying data storage. This segmentation allows the data catalog to independently manage access control and security policies while providing simplified interaction interfaces. The separation ensures that security mechanisms remain effective even as the interaction layer becomes more user-friendly.
2Adaptability or versatility
If LLMs are permitted to access data without structured restrictions, then query flexibility is improved, but access control and data privacy are worsened
Solution Approach 1:
The data catalog implements dynamic access control that adapts to different user roles, data types, and query contexts. Access policies are not static but can be adjusted based on user credentials, data sensitivity levels, and operational context. This dynamic approach maintains query flexibility while ensuring reliable access control through continuous policy evaluation.
Solution Approach 2:
The system incorporates feedback mechanisms where the data catalog continuously monitors and evaluates access requests against defined policies. When queries are executed, the system provides feedback on access permissions and can adjust subsequent queries based on observed patterns and policy requirements. This feedback loop ensures that access control remains reliable while allowing flexible query execution.
3Productivity
If LLMs process data without grounding in structured metadata, then response generation speed is improved, but accuracy and reliability are worsened due to hallucinations
Solution Approach 1:
The data catalog performs preliminary actions by pre-processing and structuring data into standardized metadata formats before queries are received. This preliminary organization of data allows the LLM to access well-structured information quickly, reducing the need for complex real-time processing while maintaining high accuracy. The pre-structured metadata serves as a reliable foundation for accurate responses.
Solution Approach 2:
The system creates a simplified copy of the data structure in the form of standardized metadata representations. Instead of processing raw data directly, the LLM works with these structured copies that capture essential data characteristics. This copying approach maintains accuracy by preserving data semantics while improving processing speed through the use of optimized data representations.
4Productivity
If the system processes all data queries without resource management, then processing throughput is improved, but system stability and resource efficiency are worsened
Solution Approach 1:
The data catalog dynamically changes processing parameters based on query characteristics, data volume, and system resource availability. For simple queries, the system uses faster but less resource-intensive processing modes. For complex queries involving large datasets, the system adjusts parameters to balance processing speed with resource consumption. This parameter adaptation maintains throughput while ensuring system stability through resource-aware processing.
Data Source
AI summary
A computer-implemented system, computer-implemented method, and computer-program product includes receiving a natural language query from a user for executing an analytical task; generating an analytical large language model (LLM) prompt based on the natural language query and, in response to generating the analytical LLM prompt, orchestrating an LLM-directed workflow for handling the natural language query by: automatically prompting, using the analytical LLM prompt, an analytical task-oriented LLM to generate a structured query for querying a data catalog application; querying the data catalog application using the structured query generated by the analytical task-oriented LLM; obtaining query results from the data catalog application, where the query results include metadata associated with at least one element accessible to the data catalog application; prompting the analytical task-oriented LLM to identify a given analytical task associated with a given analytical agent; and automatically executing, by the given analytical agent, the analytical task.