Natural language SQL interface with read-only permissions

Yes. If your key requirement is natural-language querying over an SQL database while preserving read-only permissions, there are several credible options.

1. Wren AI — strongest match

Wren explicitly recommends connecting with a dedicated read-only database user. For example, its MySQL connector requires only SELECT permissions and says it will never issue INSERT, UPDATE, DELETE, or DDL. The same approach is documented for SQL Server, Redshift, and Oracle.docs.getwren.ai

More importantly, Wren has a Context Layer that limits what the LLM can query, and its current security architecture supports row-level and column-level security at query time.docs.getwren.ai

That makes it particularly attractive if by "adheres to read-only permissions" you mean:

The AI should be unable to bypass the permissions already established for the database user.

2. AgentSQL — very straightforward

AgentSQL is almost exactly the use case you described: ask questions in plain English, have it generate SQL, execute it read-only, and display the SQL alongside the result. It supports PostgreSQL, MySQL, Snowflake and BigQuery.agentsql.com+1

A particularly good security characteristic is that AgentSQL says the read-only boundary is enforced by the database/cloud rather than by an application setting. You give it a read-only role/user/service account.agentsql.com+1

3. Vanna AI — good for building your own interface

Vanna provides a natural-language SQL agent and has access-control features. Its SQL execution tool can be restricted to particular access groups.vanna.ai

For security, I'd still configure the underlying database connection with a strict read-only database account rather than relying solely on Vanna's application-level controls. Its architecture keeps database credentials in your environment rather than sending them to Vanna's servers.vanna.ai

My recommendation

If you're evaluating this for an organization, I'd shortlist:

Wren AI → AgentSQL → Vanna AI

And I'd make this a hard requirement:

The LLM should never have credentials capable of writing to the database.

In other words, don't rely on a prompt saying "only generate SELECT statements." Give the AI a database identity that literally has only SELECT access. Then even if the model generates malicious or erroneous SQL, the database itself rejects writes.

Wren AI is the most compelling choice if you also need row/column-level permissions and governance. AgentSQL looks attractive if you primarily want a simple, read-only natural-language interface.

If you tell me which SQL database you use (Postgres, SQL Server, MySQL, Oracle, Snowflake, etc.) and whether this is internal/enterprise, I can narrow this down to the 2–3 best fits and compare security architecture, deployment, pricing, and permissions.

When implementing a natural language-to-SQL interface, relying purely on the AI model's prompt to "behave" and only write SELECT statements is a security risk. The industry consensus and security best practice is that read-only permissions must be enforced at the database layer (by connecting the tool using a database user profile granted strictly SELECT or read-only privileges, ideally on a read-replica).aifordatabase.com+1

Virtually all major commercial and open-source natural language SQL tools support or require this setup. They achieve adherence to read-only parameters by allowing you to supply limited database credentials.

Dedicated Commercial & Managed AI BI Tools

• Querio AI : Explicitly enforces read-only database connections at the infrastructure level to eliminate any risk of data modification or corruption, integrating natively with database access controls.querio.ai
• Defog AI / SQLCoder : Offers secure deployment models (including on-premise or VPC) where you connect your database using a restricted read-only service account.skopx.com
• Snowflake Cortex Analyst : For data residing in Snowflake, Cortex Analyst relies on Snowflake's native role-based access control (RBAC), meaning the underlying database role dictates exact read-only limitations and object visibility.

Open-Source & Developer Frameworks

• Vanna AI : A Python-based RAG framework for Text-to-SQL. Security is handled by passing a database connection string; you simply pass credentials for a user that only has SELECT permissions on your Postgres, MySQL, Snowflake, etc.medium.com+1
• CrewAI (NL2SQL Tool) : Out of the box, CrewAI's database agent tool defaults strictly to read-only operations (SELECT, SHOW, DESCRIBE, EXPLAIN only) unless write/DML operations are explicitly toggled on by an admin environment variable.docs.crewai.com
• LangChain SQL Agents : When building custom agents via LangChain, developers configure the SQLAlchemy connection string using a restricted database user role.

Best Practice Checklist for Setup

1. Database User Scoping: Create a dedicated database user (e.g., ai_readonly_user ) and grant it CONNECT and SELECT only on the specific schemas/tables you want exposed. Revoke all INSERT, UPDATE, DELETE, DROP , and ALTER capabilities.
2. Read-Replicas: Point the natural language tool to a read-only database replica rather than your primary transactional production database to protect application performance and isolation.
3. Execution Guards: Add statement timeouts and strict row-count limits (e.g., LIMIT 500 ) at the proxy or connection pool layer to prevent resource exhaustion from broad natural language questions.aifordatabase.com+1

If you can share which database engine you use (e.g., PostgreSQL, Snowflake, MySQL) and whether you prefer an open-source library or a managed SaaS tool , I can provide a more tailored recommendation.

When implementing a natural language-to-SQL (NL2SQL) tool that strictly enforces read-only access, relying solely on prompt instructions ("please only write SELECT statements") is a massive security risk . LLMs can hallucinate or be jailbroken via prompt injection.

To achieve strict, ironclad read-only enforcement , you need a multi-layered defense:

1. Database-level permissions (connect via a read-only DB user role that lacks INSERT, UPDATE, DELETE , or DROP privileges).
2. AST (Abstract Syntax Tree) parsing/validation before query execution.
3. The right tool architecture built with security in mind.reddit.com+1

Here are the best architectural approaches and tools to accomplish this safely:

1. The Gold Standard: Database Role + AST Validation

No matter what application layer you pick, configure your database connection string using a dedicated DB user.

• Grant SELECT only on required schemas/tables.
• Revoke all DDL and DML capabilities entirely at the database engine level. If an errant query slips through, the database itself rejects it.
• Query Parsing: Use tools or middleware that parse the generated SQL (e.g., using libraries like Python's sqlglot or .NET's parsing logic) to guarantee that only SELECT or WITH (CTE) statements are present before hitting the wire.reddit.com+1

2. Enterprise & Cloud-Native Managed Tools

If you are operating inside a major data warehouse or enterprise stack, use native semantic-layer tools that inherit role-based access control (RBAC):

• Snowflake Cortex Analyst: Converts natural language to SQL grounded in a curated semantic model and executes natively under your warehouse's strict RBAC constraints.zenlytic.com
• Databricks Genie: Operates within the Databricks AI/BI suite, reading from curated Genie spaces and Unity Catalog metadata to run read-only queries.zenlytic.com

3. Open-Source & Self-Hosted Engines

If you want a customizable, self-hosted application layer that emphasizes validation and safety checks:

• NLQueries: An open-source, self-hosted NL2SQL engine that parses generated SQL column-by-column against your live schema using sqlglot and executes strictly read-only. It functions as a CLI, Python library, or MCP server.
• NLP-to-SQL (FastAPI/React): A popular open-source template featuring input sanitization and code enforcement explicitly scoped to read-only SELECT queries for PostgreSQL and MySQL.github.com+1

To help narrow down the best implementation path, could you tell me:

• What database engine are you using (PostgreSQL, Snowflake, MySQL, BigQuery, etc.)?
• Do you prefer a self-hosted open-source stack or a managed cloud service?

If you need a natural-language interface to your SQL database where users can ask questions in plain English but cannot modify, delete, or insert data under any circumstances, I recommend an architecture that enforces read-only access at the database level, rather than trusting the AI to follow instructions.

I found several tools worth evaluating, depending on whether you want a ready-to-use product or a self-hosted solution.

Best options to evaluate

• 

1. NLQueries

(Best self-hosted starting point)

An open-source, self-hosted NL-to-SQL engine that validates generated SQL against the actual schema before execution. Supports PostgreSQL, MySQL, Snowflake, SQL Server, BigQuery, and more. It also provides a CLI, Python library, and MCP server.nlqueries.com+2

Best for: Teams that want to host the tool themselves and inspect or customize the security pipeline.

Website and documentation

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2. AgentSQL

A ready-made interface where users ask questions in plain English and receive results, charts, and the exact SQL used. It advertises read-only connections for PostgreSQL, MySQL, Snowflake, and BigQuery.agentsql.com+1

Best for: Getting a user-facing analytics tool running quickly without building the entire application.

Website

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3. Chion

Advertises two layers of SQL validation, read-only database roles, row-level security inherited from the database role, and query auditing. Its security documentation also explains what query results and schema information are sent to the language model.chion.ai+2

Best for: Evaluating a more security-focused managed solution.

Security documentation

These are candidates to evaluate, not independent security certifications. Verify their implementation and deployment model against your requirements before granting production access.

How to enforce read-only access strictly

Regardless of which tool you choose, I would require these controls:

1. Database-enforced permissions

Create a dedicated database account with SELECT permissions only on approved tables or views. Do not give the AI service an administrator or application-owner account.

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2. SQL validation before execution

Parse generated SQL and reject writes, DDL, multiple statements, unsafe functions, and locking queries. Allow only approved read queries. SQL validation supplements—not replaces—database permissions.

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3. Enforce each user's data access

Apply database row-level security or an equivalent authorization layer so users cannot retrieve records they are not allowed to see. A shared read-only account alone does not guarantee per-user access control.

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4. Limit and audit queries

Set execution timeouts, result-size limits, resource quotas, and audit logging. A read-only query can still expose sensitive data or consume excessive database resources.

These safeguards align with the security guidance for SQL agents and the validation approach documented by NLQueries and Chion.reference.langchain.com+2

My recommendation

• For a self-hosted solution: Start by evaluating NLQueries.
• For the quickest managed experience: Evaluate AgentSQL.
• For stronger emphasis on security controls: Include Chion in your comparison.

One question will help me narrow this down to the best fit:

Which SQL database are you using?

1. Database engine

PostgreSQL

Microsoft SQL Server / Azure SQL

MySQL / MariaDB

Snowflake

BigQuery

Other

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2. Deployment preference

Self-hosted / inside our infrastructure

Managed SaaS

Either is fine

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3. User access requirements

Everyone can read the same approved data

Different users can access different tables or rows

[Find the best fit]