Text-to-SQL Chat With Access-Level Enforcement and Data Obfuscation
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Solution Overview
Problem
Interacting with SQL databases can be challenging due to complex query requirements and access control issues, especially for users without programming knowledge, and there is a need to enforce access levels based on user permissions when retrieving sensitive data.
Innovation Solution
A computing system using a generative artificial intelligence chat interface that processes natural language questions to generate SQL queries, identifies relevant database tables, executes the queries, and provides obfuscated results based on user access levels, without requiring users to write SQL code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users without programming knowledge interact with SQL databases, then data retrieval accessibility is improved, but access control security deteriorates
Solution Approach 1:
The system introduces a natural language processing intermediary layer between users and the SQL database. This mediator translates user-friendly natural language queries into SQL commands while automatically enforcing access control policies, thus maintaining security without compromising ease of use.
Solution Approach 2:
The system segments the data access process into distinct layers: natural language input processing, SQL query generation, access level verification, and result delivery. This segmentation allows independent optimization of user interface simplicity and security enforcement mechanisms.
2Ease of operation
If natural language queries are processed to generate SQL code, then ease of operation is improved, but system complexity deteriorates
Solution Approach 1:
The system employs multiple intermediary components including a natural language processing module, a query generation module, and an access control module. These intermediaries handle the complexity of translation and security enforcement, keeping the user interface simple while managing system complexity internally.
Solution Approach 2:
The system performs self-service by automatically translating natural language queries into SQL commands and enforcing access control policies without requiring user intervention or manual SQL writing. This automation handles the complexity internally while presenting simplicity to users.
3Reliability
If access levels are enforced on query results, then security is improved, but information completeness deteriorates
Solution Approach 1:
The system applies local quality by customizing the information content of query results based on each user's access level. Different users receive different subsets of data with appropriate sensitivity obfuscation, ensuring each user gets the maximum information permitted by their security clearance without unnecessary restrictions.
Solution Approach 2:
The system dynamically changes the parameters of data presentation based on user access levels. This includes modifying data granularity, applying obfuscation to sensitive fields, and filtering results to match authorized access levels, thereby balancing security enforcement with information completeness for each user context.
Data Source
AI summary
Systems and methods are provided for processing a natural language question using structured query language (SQL). A computing system receives natural language questions, obtains access levels and database schema, generates prompts, identifies relevant tables, and generates SQL code using a SQL large language model (LLM). It executes the code on the database and generates a result message with obfuscated data values based on access level. The result message is provided in response to the natural language question.