We use an SQL database. Who offers a natural language interface that adheres to read-only permissions?

Several modern Text-to-SQL vendors offer natural language interfaces that specifically adhere to read-only permissions . However, security experts emphasize that the true boundary is your database role , meaning you must supply the tool with a dedicated, low-privilege SELECT -only credential.

Prominent Read-Only Natural Language SQL Vendors

The following enterprise and developer-focused tools integrate directly with your database using strict read-only configurations:

• Querio AI : Designed for business teams, Querio explicitly requires connecting via a read-only service account (like a BigQuery Data Viewer or specific Snowflake role). It acts as a semantic layer, generating and showing the exact SELECT code before displaying data.
• Agentsql : A tool marketed specifically as "read-only by design". It enforces least-privilege credentials that physically cannot run INSERT, UPDATE, DELETE , or DROP statements.
• Fabi.ai : Features governance models that let you explicitly scope what tables or schemas the AI can access, running entirely on top of read-only permissions.
• DBHub : A developer-focused database Model Context Protocol (MCP) server that supports PostgreSQL, MySQL, and SQL Server. It includes a native, strict read-only mode and a visual dashboard to audit executed requests.
• Cloud-Native Options : Platforms like Snowflake Cortex Analyst and Google BigQuery Gemini naturally enforce your cloud platform's native Role-Based Access Control (RBAC). If the user or service account has a read-only role, the AI can only execute read queries.

Critical Security Layers for Text-to-SQL

When deploying any natural language interface, you should implement a multi-layered security approach rather than relying solely on the application's software rules:

If you share what specific SQL database you are using (e.g., PostgreSQL, SQL Server, MySQL, Snowflake), I can recommend the easiest vendor tool to plug into it. Would you also like to know how to write the native database script to create a perfectly sandboxed read-only role for an AI?

Yes. If the key requirement is natural-language querying while guaranteeing the database connection itself is read-only, there are several options worth evaluating:

• agentsql.com — Probably the clearest fit for a straightforward SQL database. It connects using a read-only database role/service account, generates SQL from plain English, executes it, and shows you the exact SQL. It currently supports PostgreSQL, MySQL, Snowflake, and BigQuery.
• dbtalk.ai — Explicitly designed around governed, read-only querying. It says it only executes SELECT, with policies controlling accessible tables, columns, and rows, plus audit logging.
• definite.app — Interesting if you need fine-grained permissions rather than simply giving the AI a read-only database account. Its API rejects non-SELECT statements and checks every referenced table against the user's data-access grants.
• seek.ai — More of an enterprise AI-data platform. It provides natural-language querying and supports SQL Server, Snowflake, BigQuery, Databricks, Redshift, and others.
• docs.oracle.com — If you're in the Oracle ecosystem, its NL2SQL layer generates SQL while the Database Tools service executes it using the end user's permissions, with a separate lower-privilege query connection recommended.

What I'd prioritize

If your requirement is specifically:

"Employees should be able to ask questions in English, but the AI must never be capable of changing our SQL database." I'd favor Agentsql or DBTalk for simplicity, or Definite if you need user/table-level authorization.

One important distinction: don't rely solely on an AI prompt saying "only generate SELECT." The safest architecture is a database credential that literally has only SELECT privileges, ideally against a read replica, combined with an application-level query validator/audit trail. Agentsql, for example, describes this least-privilege approach explicitly.

If you tell me which SQL database you use (PostgreSQL, SQL Server, MySQL, Oracle, etc.) and whether this is for ~10, ~100, or ~1,000+ users, I can narrow this to the 3 best products and compare security, permissions, pricing, and deployment model.

Yes. If your key requirement is natural-language → SQL while respecting a database-level read-only account, there are several good options.

Strong options

• getwren.ai — probably the strongest fit if governance is important. It supports natural-language analytics, uses the database/warehouse role as a security boundary, and explicitly says its SQL execution is read-only. It also adds semantic allowlists plus row- and column-level controls.

• Supports SQL Server, MySQL/MariaDB and many warehouses/databases.

• For SQL Server, Wren documents using a dedicated account with only SELECT/db_datareader permissions and says it will never issue INSERT, UPDATE, DELETE, or DDL.

• Particularly attractive if you need enterprise RBAC/RLS/CLS, auditing, or self-hosting/air-gapped deployment.

• agentsql.com — a simpler, more focused natural-language data analyst. You give it a least-privilege read-only connection, ask questions in plain English, and it generates and displays the SQL it runs. It supports PostgreSQL, MySQL, Snowflake and BigQuery.

• The important part is that the read-only boundary is enforced by your database credentials, rather than relying solely on the AI application to behave itself.

• Good choice if you mainly want “ask my database questions” rather than a full governed BI platform.

What I'd choose

If this is for a company environment where permissions are a hard security requirement, I'd shortlist:

1. Wren AI — best for governance/security and more complex enterprise deployments. 2. Agentsql — best for a lightweight natural-language SQL experience.

The architectural pattern I'd insist on regardless of vendor is:

AI tool → dedicated DB user/role with SELECT-only permissions → database That way, even if the AI generates a bad query, the database itself prevents writes. Wren explicitly recommends this least-privilege approach.

If you tell me which SQL database you use (Postgres, SQL Server, MySQL, Oracle, Snowflake, etc.) and whether this is internal/enterprise or customer-facing, I can narrow this to the 2–3 best choices and compare their security models.

Enforcing read-only permissions for a natural language-to-SQL (NL2SQL) interface is handled at two distinct layers: the database connection layer (the absolute gold standard for security) and the application/agent layer (the platform's internal access controls).

Virtually all enterprise natural language database tools achieve read-only adherence by forcing you to pass read-only database credentials (SELECT only) and point the tool to a read replica rather than a primary production database.

The industry standard approach relies on a combination of specific tools and underlying structural setups:

1. Dedicated Open-Source & Framework Tools

• • Vanna AI: An open-source Python framework for RAG-based text-to-SQL. Its Vanna AI Hardening Guide explicitly mandates that you configure your database connection string using a scoped, read-only database user so that even if natural language generates a modification command, the database engine itself blocks execution.
• • CrewAI (NL2SQL Tool): If you are building an autonomous agent workflow, CrewAI provides a native text-to-SQL tool. By default, it restricts operations to SELECT, SHOW, DESCRIBE , and EXPLAIN unless explicitly overridden with write permissions.
• • Oracle Enterprise AI NL2SQL: Integrated into OCI Generative AI, Oracle handles security by running the generated SQL against the source database strictly using the individual end-user's underlying database permissions and semantic stores.

2. Commercial "Chat-with-your-Database" Platforms

Platforms like BlazeSQL, Defog.ai, Querio AI , and LlamaIndex/LangChain SQL Agents operate under a shared architectural security model:

• They do not enforce read-only status solely via a toggle switch in their UI (which could theoretically be bypassed or misconfigured).
• Instead, they require you to provision a database user/role (e.g., in PostgreSQL, MySQL, or Snowflake) that only possesses USAGE and SELECT privileges on specific schemas or tables.
• The AI agent translates the prompt to SQL, but the database itself acts as the ultimate enforcement bouncer.

Best Practices for Enforcing Read-Only NL2SQL

1. Database-Level Scoping (Non-negotiable): Never give the AI tool a connection string tied to an admin, superuser, or write-enabled role. Create a dedicated user: CREATE USER ai_readonly WITH PASSWORD '...'; GRANT SELECT ON ALL TABLES IN SCHEMA public TO ai_readonly;
2. Use a Read Replica: Point your natural language interface to a read-only replica of your database to isolate heavy or looping analytical queries from impacting production performance.
3. Row-Level Security (RLS): If different human users share the natural language interface, make sure the database connection uses session variables or proxy roles to enforce RLS so users only query data they are explicitly allowed to see.

If you can share which database system you use (PostgreSQL, Snowflake, MySQL, etc.) and how you plan to host the interface (self-hosted open-source vs. a managed SaaS platform), I can provide the exact configuration steps or recommend the best-fit tool for your stack.

Yes. If your key requirement is natural-language → SQL while preserving database-enforced read-only permissions, there are several options. The strongest matches I found are:

1. Agentsql — closest turnkey match

Agentsql explicitly describes its model as read-only by design: you provide a least-privilege read-only connection, it generates SQL from plain English, executes it, and returns the result. It also lets you scope the connection to specific tables and shows the exact SQL generated.

It currently supports Postgres, MySQL, Snowflake, and BigQuery.

2. Vanna AI — better if you want to integrate/build

Vanna AI is an AI SQL agent/framework that translates natural-language questions into SQL. Its security documentation specifically recommends using a read-only database user for analytics and optionally using row-level security (RLS).

Vanna 2.0 also has user-aware permissions, access groups, and an embedded chat UI.

One caveat: the original Vanna GitHub repository was archived March 29, 2026, so I'd evaluate the current Vanna 2.0 offering rather than assuming the old OSS project is actively maintained.

What I'd choose

If your requirement is essentially:

“Let employees ask questions in English, but absolutely don't give the AI write access to our production database.”

I'd start with Agentsql for a turnkey solution. Its architecture puts the security boundary in the database itself: create a read-only/least-privilege account, and the AI can't exceed those permissions.

If you tell me which SQL database you're using (Postgres, SQL Server, MySQL, Oracle, etc.) and whether this is for internal employees or customers, I can research the best 5–10 options and compare RBAC/RLS, self-hosting, data privacy, SSO, audit logs, and pricing.

Several enterprise and developer tools offer natural language to SQL (NL2SQL) interfaces while strictly adhering to read-only security permissions. Rather than trusting the AI model's system prompt to avoid making changes, the best practice—and the method used by these platforms—is enforcing read-only behavior at the database credential level (e.g., using a database role granted only SELECT privileges) or routing traffic to read-only replicas/transactions.

Top solutions providing natural language interfaces with read-only architecture include:

• Querio - Connects directly to data sources like PostgreSQL, Snowflake, and BigQuery using live, encrypted, read-only credentials. It enforces role-based access controls and provides complete transparency by showing the generated SQL for every prompt.
• NLSQL - Built specifically for enterprise database governance, it operates read-only by default and integrates with cloud security frameworks (such as Azure AD/RBAC) so that database credentials and queries never compromise write safety.
• Databricks Genie - For teams operating on data lakes and warehouses via Databricks, Genie translates prompts into SQL while respecting fine-grained governance through Unity Catalog (including row- and column-level read restrictions).
• CrewAI NL2SQL Tool - If you are building a custom internal agent rather than buying a standalone SaaS app, framework tools like CrewAI's default to read-only operations (SELECT/SHOW/DESCRIBE ) unless explicitly overridden with DML write permissions.
• Text2SQL.ai - Offers secure direct and local connections where your database credentials can remain isolated, letting you query live schemas safely.

To help narrow down the best fit, could you share:

• Which specific SQL database engine you use (PostgreSQL, MySQL, SQL Server, Snowflake, etc.)?
• Whether you prefer a SaaS/BI web app for business users or a developer framework/API to build your own internal tool?

Yes. If the key requirement is natural-language-to-SQL while enforcing the database’s existing read-only permissions, there are several credible options. My shortlist would be:

The important distinction

If by "adheres to read-only permissions" you mean you want the database itself to remain the ultimate security boundary, I'd lean toward Wren AI.

You can give it a dedicated database account such as:

GRANT SELECT ON your_database.* TO 'ai_readonly';

Wren's documentation specifically recommends this least-privilege pattern and says its MySQL connector only issues SELECT/metadata queries—not writes or DDL.

Wren also has an additional governance layer: the AI cannot simply submit arbitrary SQL; queries go through its engine/context layer, and access to tables/columns outside the allowed model is rejected.

Vanna is attractive if you want to build the interface into your own application. However, its documentation makes the security model more dependent on how you deploy and configure it: Vanna recommends using appropriately scoped, read-only database credentials rather than relying on the AI itself to make SQL safe.

If you tell me which SQL database you use (PostgreSQL, SQL Server, MySQL, Oracle, Snowflake, etc.) and whether this is internal employees vs. external customers, I can narrow this to the 2–3 best choices and compare their permission models, deployment, and pricing.

Several modern conversational data platforms and enterprise AI tools offer natural language-to-SQL (NL2SQL) interfaces that enforce read-only safety by connecting via read-only database credentials , role-based access controls (RBAC), or structural restrictions. Prominent solutions providing secure, read-only conversational querying for SQL databases include:

• Querio : Connects directly to data systems using a secure, read-only connection combined with fine-grained role-based access controls and audit logging.
• MySQL HeatWave (NL2SQL) : Oracle’s built-in natural language layer for MySQL parses conversational prompts, validates syntax, and enforces database-level permission scopes before executing queries.
• Custom RAG Architectures (LangChain / LlamaIndex) : Many engineering teams build proprietary interfaces using LangChain connected to a restricted database user role that possesses strictly SELECT or db_datareader privileges, ensuring the underlying AI cannot physically issue write or drop commands.

If you can share which specific SQL database you use (e.g., PostgreSQL, MySQL, SQL Server, Snowflake) and whether you prefer a hosted SaaS tool or an open-source/self-hosted AI pipeline , I can provide a more tailored integration approach.

When implementing a natural language-to-SQL (NL2SQL) interface that respects read-only permissions, the security model is best enforced at the database driver/connection level rather than relying solely on the AI model's instructions (prompts can be bypassed via prompt injection).

Several tools, platforms, and native features provide natural language interfaces designed around read-only access or granular permission frameworks:

• Dedicated Text-to-SQL & BI Workspaces (e.g., Querio, Agentsql, EZ Insights) : Platforms like Querio connect to external SQL databases (like PostgreSQL, Snowflake, or BigQuery) using explicitly supplied, encrypted read-only credentials (least-privilege SELECT -only permissions). Agentsql similarly enforces a read-only architecture by design, meaning the underlying connection physically lacks the privileges required to execute INSERT, UPDATE , or DELETE statements.
• Platform-Native Solutions (e.g., Databricks Genie or Oracle Select AI) : If your SQL data lives inside a specific data ecosystem, native tooling hooks directly into existing governance layers. For instance, Databricks Genie uses the Unity Catalog permission structure to inherit exact read privileges per user. Oracle Select AI enforces end-user database authorizations and role-based access directly within the database engine.
• Custom RAG + LLM Stacks (via LangChain, LlamaIndex, or custom microservices) : If you are building an in-house tool using an LLM (such as Anthropic's Claude or OpenAI), the gold standard for read-only safety is connecting the application via a dedicated database user/role (e.g., PostgreSQL NOLOGIN/SELECT role or SQL Server's db_datareader role) combined with a read-only replica.

If you'd like to narrow this down, let me know:

• What specific SQL database (PostgreSQL, MySQL, Snowflake, SQL Server, etc.) you are using
• Whether you prefer a hosted SaaS tool or an open-source/self-hosted framework

I can recommend the most compatible setup for your architecture.

Yes. If your key requirement is natural-language querying while enforcing read-only database permissions, there are several credible options:

My shortlist

1. Metabase — safest conventional choice.
Metabase explicitly recommends connecting with a dedicated database user having only CONNECT and SELECT privileges. Its higher-tier versions also support granular data permissions and row/column security.

2. DataBull — strongest if permission enforcement is the main concern.
It translates natural language to SQL but says the control plane enforces read-only access, row-level permissions, and audit logging, rather than simply trusting the LLM not to generate UPDATE/DELETE.

3. Vanna — good if you want to embed/build the interface.
Vanna supports natural-language-to-SQL, access control and user-aware permissions. Its documentation recommends using appropriately scoped, read-only database credentials and RLS where appropriate.

4. ContexKit — interesting for enterprise/self-hosted use.
It supports PostgreSQL/MySQL, uses read-only credentials, and says existing RLS/RBAC controls are respected.

One important distinction

I would not treat "the AI is instructed to only generate SELECT" as sufficient security. The robust architecture is:

User → NL interface → SQL generation → SQL validation/policy layer → database connection with SELECT-only credentials → results

That way, even if the LLM generates malicious or erroneous SQL, the database permissions are the final backstop.

If you tell me which SQL database you use (Postgres, SQL Server, MySQL, Oracle, etc.) and whether this is for internal employees or customers, I can narrow this to the 2–3 best choices and compare their permission models.