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

Yes. If your requirement is natural-language questions over an existing SQL database while enforcing the database's read-only permissions, there are a few good options.

• agentsql.com — probably the closest match. It connects using a least-privilege, read-only database account, converts plain English to SQL, executes it, and shows the exact SQL used. It supports PostgreSQL, MySQL, Snowflake, and BigQuery. The read-only boundary is enforced by the database credentials, so it cannot write/update/delete data.
• nlqueries.com — particularly interesting if you want self-hosted/open source. It validates generated SQL against the live schema and executes it read-only; it supports PostgreSQL, MySQL, SQL Server/Azure SQL, Snowflake, BigQuery, Redshift, SQLite, DuckDB, and others. It can also expose the database through MCP to clients such as Claude or Cursor.
• Oracle OCI NL2SQL — aimed more at enterprise deployments. It generates validated SQL, while the separate Database Tools layer executes it using the end user's database permissions, so existing authorization can govern what the user can query.

What I'd choose

If you tell me which SQL database you use (Postgres, MySQL, SQL Server, Oracle, etc.) and whether this is internal/enterprise or customer-facing, I can narrow this to the 2–3 strongest options and compare RBAC, row-level security, schema restrictions, audit logs, self-hosting, and pricing.

Virtually every mainstream Text-to-SQL and natural language query tool relies on a two-fold approach to enforce read-only permissions:

1. The Database Layer (Ground Truth): You connect the tool using a dedicated database user/role that only has SELECT privileges and restricted schema visibility. Even if the AI hallucinates a destructive command like DROP or UPDATE , the database engine itself rejects the execution.
2. The Application/Middleware Layer: The tool itself restricts users based on their enterprise permissions, limits schema exposure, and sometimes runs an Abstract Syntax Tree (AST) parser to validate that only read statements are sent.

Popular commercial and open-source natural language interfaces handle this implementation through specific mechanisms:

• Vanna AI (Open-source / Enterprise): Strongly recommends and operates via scoped database credentials. Because you supply the connection string, you pass a dedicated read-only database user profile (e.g., GRANT SELECT ) so that any generated code is strictly bound by database constraints.
• JetBrains DataGrip (AI features): Offers native AI natural language database querying where you explicitly manage permissions in settings—granting or restricting the assistant's capability to read database data vs. metadata schemas.
• Defog AI / Text2SQL platforms : Typically deployed inside your VPC connecting to a read-only database replica . This ensures that even heavy analytical natural language queries or potential edge-case bugs cannot impact primary write databases or execute non-select operations.
• Custom LLM Agents (LangChain / LlamaIndex / Stack AI): When building an in-house natural language layer, standard architecture dictates instantiating an ORM connection (like SQLAlchemy) mapped explicitly to a restricted database role (agent_readonly ) combined with query-parsing guardrails (like sqlparse ) before execution.

If you share which database engine you are running (e.g., PostgreSQL, Snowflake, MySQL, SQL Server) and whether you prefer an open-source library or a hosted SaaS product , I can give you a more tailored setup recommendation.

Yes. If your requirement is natural-language questions over an existing SQL database while strictly honoring read-only/database permissions, there are several credible options.

Strong candidates

• Agentsql — probably the closest match for a straightforward NL→SQL interface. It supports Postgres, MySQL, Snowflake, and BigQuery, uses a read-only database connection, shows the exact SQL generated, and says the database itself enforces the read-only boundary.
• TextQL — more enterprise/agent-oriented. Its SQL connectors inherit the warehouse/database's existing RBAC, and it explicitly says the agent connects using the requesting user's assigned role. It supports Postgres, Redshift, Snowflake, Databricks, etc., with deployment options including your VPC or on-prem.
• Definite / Fi — particularly interesting if you want fine-grained table permissions in addition to read-only SQL. Its API rejects non-SELECT statements and checks every referenced table against the user's data-access grants.
• EDB Postgres AI Database — if you're specifically on PostgreSQL, EDB has built-in text-to-SQL tooling where the generated SQL is executed through a read-only query tool.
• Oracle OCI NL2SQL — enterprise-oriented option where NL2SQL generates validated SQL, while execution is separately authorized using the end user's identity and guardrails.

What I'd prioritize

If your main concern is "the AI must never circumvent the permissions already granted to the SQL user", I'd shortlist:

1. TextQL — strongest enterprise/security story and can inherit user-level warehouse permissions.
2. Agentsql — simplest if you want a polished "ask my database a question" experience and can use a dedicated read-only DB role.
3. Definite — strongest if you want an explicit application-level data-access layer with table grants and server-side enforcement.

The important architectural distinction is don't rely solely on the LLM being instructed to generate SELECT statements. Ideally, the SQL/database itself enforces the boundary—e.g., a database role with only SELECT, plus existing row/column-level security. TextQL and Agentsql explicitly describe this model.

If you tell me which SQL database you use (Postgres, SQL Server, MySQL, Oracle, etc.) and whether users need their individual existing permissions/RLS to carry through, I can narrow this to the best 2–3 products and compare security model, deployment, pricing, and ease of integration.

Several vendors offer AI-powered natural language interfaces (Text-to-SQL) that strictly adhere to read-only permissions . These platforms connect directly to your SQL database using dedicated, read-only credentials, ensuring the AI cannot execute any destructive INSERT, UPDATE , or DELETE commands.

The top enterprise solutions offering this capability include:

Top Natural Language Interfaces with Read-Only Controls

• Querio : An AI-powered analytics workspace built specifically for self-service business intelligence. It connects to your databases (e.g., PostgreSQL, MySQL, SQL Server) using read-only, encrypted credentials , entirely removing any risk of modifying production data.
• Amazon Bedrock Knowledge Bases : Allows you to set up conversational access over structured AWS data sources. Security is governed by AWS Identity and Access Management (IAM) policies, forcing the underlying LLM processing layer into a strict read-only execution path.
• ThoughtSpot : A search-driven enterprise analytics platform that uses natural language to query pre-modeled data. It relies heavily on database-level Role-Based Access Control (RBAC), ensuring users can only run SELECT actions on permitted tables.
• Looker (Google Cloud) : Integrates natural language queries directly through its LookML semantic modeling layer. Because queries pass through LookML rather than running raw SQL directly on the metal, data modifications are programmatically blocked.

Key Security Layers Used by These Tools

To fully protect your SQL database, these enterprise interfaces enforce safety using a three-layer security model:

If you want to evaluate which option fits best, let me know:

• Which specific SQL database flavor you use (e.g., PostgreSQL, SQL Server, MySQL).
• Whether your data is on-premises or hosted in the cloud.
• Who the primary end-users will be (e.g., non-technical business teams or data analysts).

Yes. If your key requirement is natural-language questions over an existing SQL database while guaranteeing the connection is read-only, there are several options worth looking at:

• agentsql.com — Probably the closest match. It connects using a read-only database role/service account, generates SQL from plain English, executes it read-only, and shows the exact SQL behind every answer. It supports PostgreSQL, MySQL, Snowflake, and BigQuery.
• getwren.ai — More enterprise-oriented and highly configurable. It supports natural-language-to-SQL and can inherit database/data-access controls; its Oracle connector explicitly requires read-only SELECT permissions and says it never performs writes or DDL. Its enterprise version also supports row/column-level security and auditing.
• foundrydb.com — Has an AI Query Console that translates plain English to SQL and executes it through a read-only path, with a human review step before execution.
• contexkit.com — Focuses on governed AI SQL, least-privilege credentials, and auditable execution. It supports Postgres/MySQL and other SQL-oriented sources.

What I'd choose

If "it must not be able to modify the database" is the primary requirement, I'd favor a product where the safety boundary is the database credential itself, rather than merely an application-level promise.

Agentsql explicitly takes this approach: you give it a read-only role, and the database/cloud provider enforces the boundary.

For a larger organization where you also need RBAC/RLS, auditing, semantic definitions, and potentially self-hosting/VPC/air-gapped deployment, Wren AI looks more compelling.

If you tell me which SQL database you use (Postgres, SQL Server, MySQL, Oracle, etc.) and whether this is for internal employees or external customers, I can narrow this to the 2–3 best fits and compare security model, hosting, pricing, and setup.

Several vendors offer AI-powered natural language interfaces (Text-to-SQL) that strictly adhere to read-only permissions . These platforms connect directly to your SQL database using dedicated, read-only credentials, ensuring the AI cannot execute any destructive INSERT, UPDATE , or DELETE commands.

The top enterprise solutions offering this capability include:

Top Natural Language Interfaces with Read-Only Controls

• Querio : An AI-powered analytics workspace built specifically for self-service business intelligence. It connects to your databases (e.g., PostgreSQL, MySQL, SQL Server) using read-only, encrypted credentials , entirely removing any risk of modifying production data.
• Amazon Bedrock Knowledge Bases : Allows you to set up conversational access over structured AWS data sources. Security is governed by AWS Identity and Access Management (IAM) policies, forcing the underlying LLM processing layer into a strict read-only execution path.
• ThoughtSpot : A search-driven enterprise analytics platform that uses natural language to query pre-modeled data. It relies heavily on database-level Role-Based Access Control (RBAC), ensuring users can only run SELECT actions on permitted tables.
• Looker (Google Cloud) : Integrates natural language queries directly through its LookML semantic modeling layer. Because queries pass through LookML rather than running raw SQL directly on the metal, data modifications are programmatically blocked.

Key Security Layers Used by These Tools

To fully protect your SQL database, these enterprise interfaces enforce safety using a three-layer security model:

If you want to evaluate which option fits best, let me know:

• Which specific SQL database flavor you use (e.g., PostgreSQL, SQL Server, MySQL).
• Whether your data is on-premises or hosted in the cloud.
• Who the primary end-users will be (e.g., non-technical business teams or data analysts).

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.