Secure Natural Language Search for SQL Databases using LLM

This article explores a secure and privacy-preserving approach to searching data within an advanced SQL database using natural language. It outlines a multi-step process that leverages natural language processing (NLP) and vectorization techniques to enable accurate retrieval while safeguarding sensitive information.

step 1 Preparing Data schema with synonymous of table name and column name.

? ?? Data Schema with Synonyms: Instead of storing the exact table and column names, create a separate table containing synonyms and mappings. This obfuscates the actual schema structure.

? ??Dummy Columns and Tables: Introduce dummy entries in the synonym table to further anonymize the representation.

step 2: Data Preprocessing to Vectorize Metadata or DB Schema details

???? ?? Select only relevant columns containing synonyms for table and column names.

??? ?? Discard unnecessary information like data types and reference details.

Step 3: Vectorize Metadata or DB Schema details

???? ?? Utilize a Word2Vec model to convert the preprocessed schema data (synonyms) into numerical vectors. These vectors capture semantic relationships between terms.

? The model learns from the provided examples, allowing it to represent similar terms with close vectors.

Step 4 : Create and Store Vectors in Vector Database

These pre-computed vectors are stored in a dedicated vector database for efficient retrieval. Example ChromaDB, pineconemilvus

Step 5: Retrieve related tables & columns ?(RAG) Against User query

??? Get related tables will reduce LLM payload

So that LLMs work more accurate result also you can save lots of cost

Step 6: Replace the Sensitive word (NLP spacy )

This step utilizes spaCy, an NLP library, to identify potentially sensitive

?

by doing this we can ensure our data is not leaked anywhere

Step 7: Construct Payload to LLMs (ChatGPT, Gamini, Text2SQL.AI) to Get SQL query ?? Data Minimization: Only send the most relevant tables and columns (identified in Step 5) along with the user's natural language query to the LLM. This minimizes data exposure and reduces processing overhead.

?? Data Anonymization (Optional): Consider anonymizing sensitive information within the provided data using techniques like tokenization and replacement with generic terms. This adds an extra layer of security, especially if the LLM provider doesn't offer robust privacy guarantees.

?? Query Construction: Formulate a clear and concise question for the LLM that leverages the identified tables and columns. This question should guide the LLM towards generating a safe and accurate SQL query.

Step 8: Sanitizing and Verifying the LLM-generated SQL Query

1. Syntactic Validation: Ensure the returned SQL query is syntactically correct and adheres to the database dialect you're using.
2. Permission Check: Verify that the current user has the necessary permissions to access the tables and columns included in the generated query.
3. Security Checks: Implement mechanisms to detect and prevent potential security vulnerabilities, such as SQL injection attacks. Here are some approaches: Parameterized Queries: Use parameterized queries to separate data from the actual SQL statement, preventing malicious code injection. Whitelisting: Allow only pre-defined and authorized operations (e.g., SELECT statements) within the generated query.
4. Explainability (Optional): If the LLM provides explanations for its reasoning, consider incorporating them into your security analysis for better understanding of the generated query.

Part 2: Securely Manipulating the Result Dataset

This section will be covered in a separate response to maintain focus and avoid exceeding character limitations. It will delve into techniques for filtering, aggregating, and transforming the retrieved data while upholding security principles.