Business-Context SQL Query Generation for Controlled Data Access
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Solution Overview
Problem
Users without technical skills in structured query language (SQL) face challenges in accessing desired data from databases, as they may not be familiar with domain-specific data storage parameters, necessitating a secure and user-friendly interface for data retrieval.
Innovation Solution
A system utilizing a base large language model (LLM) initialized with business context-specific configurations to generate SQL queries, with post-processing to ensure security and relevance, enabling users to submit natural language requests and receive secure data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users without technical skills are allowed to access database data, then ease of operation is improved, but data security and accuracy may deteriorate
Solution Approach 1:
The patent introduces an intermediary system consisting of a query generation model and validation mechanism that stands between the user and the database. This intermediary translates natural language queries into secure SQL queries, filters out potentially harmful requests, and ensures data access rights are respected, thereby enabling easy operation while maintaining data security
Solution Approach 2:
The patent replaces the traditional mechanical approach of requiring users to manually write and execute SQL queries with an automated AI-based system. The query generation model uses natural language processing to automatically construct queries, eliminating the need for users to learn complex SQL syntax while the validation layer ensures security constraints are maintained
2Ease of operation
If users without technical skills can access data, then ease of operation is improved, but query accuracy and relevance may worsen
Solution Approach 1:
The patent implements a feedback mechanism where the validation system continuously monitors and evaluates generated queries against predefined security criteria and data access rights. When a query fails validation or returns unexpected results, the system can request clarification or adjust the query parameters to ensure accurate and relevant data retrieval while maintaining security
Solution Approach 2:
The patent performs preliminary actions by pre-defining validation criteria, security rules, and data access permissions before users submit queries. The system proactively checks queries against these pre-established guidelines to ensure accuracy and relevance before executing them, preventing inaccurate or unauthorized data access
Data Source
AI summary
Data retrieval via secure database query generation is disclosed, including: receiving, via a user interface, a user submitted request for data associated with a business context; generating a prompt to an initialized database query generation model specific to the business context based at least in part on the user submitted request; providing the prompt to the initialized database query generation model; determining a database query based at least in part on an output from the initialized database query generation model; and querying a database for matching data using the database query, wherein the matching data comprises a set of data values fetched from one or more tables of data.