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Give an AI query feature read-only access by connecting it through a dedicated database identity that has only the read permissions it needs. Do not rely on a prompt such as “never change data”: database permissions, not the prompt, are what restrict the account. The right setup depends on your database engine and on whether the feature needs access to whole tables, particular schemas, or only selected rows.
What does read-only access actually protect?
Read-only access is an authorization boundary enforced by the database. The AI feature sends queries using a database identity; the database evaluates that identity’s permissions before allowing the requested operation. If the identity has only the intended read permissions, it should not be able to alter data or schema through ordinary operations that require broader privileges.
This is different from instructing an AI model to generate only SELECT statements. A model can misunderstand a request or produce unexpected SQL, so the account used to run that SQL should not have write privileges in the first place. Microsoft’s least-privilege guidance advises against using administrator or database-owner accounts for tasks that need fewer permissions.
Read-only grants do not, by themselves, establish that every query is harmless. The cited platform guidance covers permissions and access scope, but does not settle execution-cost limits, functions with side effects, or how to protect data sent to an AI system. Treat those as separate design questions, and verify the controls available for your database and application before relying on them.
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How should I decide what the AI identity can read?
Start with the task, then grant access to the smallest set of database objects and rows that can support it. A database-wide reader role is convenient, but may expose unrelated tables; object-level permissions can narrow that exposure.
Inventory the query’s data needs
Write down the database, schemas, tables or views, and—if relevant—rows the feature needs. Review sensitive fields as part of this inventory. If a table contains columns or records the task does not need, consider whether a narrower object or row-level control is available in your platform rather than granting broad access by default.
Also identify the identity that the database will actually see. An AI service may connect directly, through an API, or through an application that opens the database session. Row-level policies and audit records are meaningful only if that path propagates the intended principal or session context. Do not assume that a user selected in the application is automatically the database user evaluated by an authorization policy.
Choose the narrowest practical scope
- Object-level: Grant reads on selected tables, views, or other supported objects. This is more precise, but may require more administration as the feature’s needs change. Microsoft’s SQL Server permissions documentation describes
GRANT SELECT ON OBJECT::Region TO Jae;as its most granular example in the Database Engine permission hierarchy. - Schema-level: Use this when the objects in a schema genuinely belong in the AI feature’s scope. It can simplify grants, but also includes objects beyond the current list if the scope is broader than intended. A later table added to that schema may therefore become visible, depending on the engine’s inheritance and default-grant behavior.
- Database-wide: Use a built-in reader role only when access to every table in that database is acceptable. SQL Server’s
db_datareader, for example, can read all tables in a database. Microsoft describes custom roles with more granular permissions as an option for tighter scope. - Row-level: Consider row-level security when the feature should see only some rows of an otherwise permitted object. Azure SQL documentation describes row-level security as a way to restrict rows based on the user running a query. Confirm that your application passes the intended identity to the database and that the policies match that architecture.
Scope has more than one dimension. A role can be narrow by database but broad by schema, narrow by table but broad by row, or correctly scoped for data while missing the container or compute permission needed to issue a query. Check database, schema, object, row, and—where applicable—warehouse or other compute access separately.
How do I set up a dedicated read-only identity?
Create an identity for the AI query path, separate from administrator, owner, and ordinary application accounts. Then grant the specific read access and any platform-required access needed to reach the objects. The exact statements and role behavior vary by engine, so use the current documentation for your database version rather than copying a grant from another platform.
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- Define the scope. Record the database, schemas, tables or views, and rows the feature requires. Remove objects and fields that do not serve the task. If a report needs a small set of columns, assess whether a view or another narrower object can provide that shape without exposing the base table more broadly.
- Create a dedicated principal. Use a separate database user or role path for the AI feature. Do not connect it with an administrator, database owner, superuser, or application credential that has broader rights. A separate principal also makes permission testing and audit review specific to the AI workload.
- Grant reads at the chosen scope. Prefer selected-object grants where feasible. Use a schema or database-wide reader only when the broader set of objects is appropriate. Avoid solving a missing-table error by attaching an unrelated high-privilege role.
- Add required access to containers or compute. Some platforms require permissions beyond reading tables. Snowflake’s documented custom read-only role, for example, requires USAGE on the database, schema, and warehouse, as well as SELECT on the tables in scope. A table grant alone is not sufficient if the role cannot use the surrounding objects or compute resource.
- Decide how new objects should be handled. Determine whether later-created tables should become readable automatically or require a deliberate grant. Existing-object grants and future-object grants can be separate. A default that automatically exposes every future table is convenient but can expand the data boundary without a review.
- Check ownership and grant inheritance. Find out which role or owner creates objects and which role is allowed to grant privileges. PostgreSQL default privileges, for example, are associated with the object-creating owner; configuring a default for one owner does not necessarily cover objects created by another owner.
- Test using the AI identity. Connect as that principal and check that an intended read succeeds, an out-of-scope read is denied, and write and schema-change attempts are denied. This is an operational verification step; vendor documentation explains permission models but cannot verify your particular connection.
- Review the grants when requirements change. If the feature needs a new object, update its scope deliberately instead of solving access errors by giving it a broader role than it needs. Remove grants when a table or workflow is retired.
How do the permissions differ across common platforms?
The same goal—letting an AI query data without granting broad write access—has different permission requirements on different platforms. These examples describe documented behavior, not interchangeable SQL recipes.
| Platform | Read-access approach described | Scope or additional requirement | Future objects | Administrative trade-off |
|---|---|---|---|---|
| SQL Server | db_datareader can read all tables in a database; custom user-defined roles allow more granular permissions. |
Object-level SELECT is Microsoft’s most granular example in the cited Database Engine permissions guidance; higher-level grants can flow to child objects. | Not stated in the cited SQL Server guidance. | The built-in role is simple but broad. A custom role requires maintaining grants as the feature’s data contract changes. |
| Azure SQL Database | db_datareader reads every table; Microsoft notes that this is more access than strictly necessary for most production databases and describes custom roles for narrower scope. |
Row-level security can restrict which rows a user can access. Identity propagation and policy behavior must match the application architecture. | Not stated in the cited Azure SQL guidance. | Custom roles and row policies provide tighter boundaries, but require policy testing and ongoing review. |
| Snowflake | A custom read-only role uses SELECT on the tables in scope. | It also requires USAGE on the database, schema, and warehouse. | Grants for existing tables do not automatically cover future tables; a distinct future-tables grant is available. | Separate usage, table, and future-table grants are explicit, but there are more permission components to document. |
| PostgreSQL, as covered in AWS’s role guide | The guide demonstrates database CONNECT, schema USAGE, and table SELECT as separate requirements. | Confirm exact syntax and ownership behavior against the deployed PostgreSQL version. | SELECT on existing tables does not include future tables; default privileges are relevant for later tables. | The model is precise, but the owner that creates future objects and the default-privilege configuration must be managed carefully. |
| Supabase SQL snippets | Supabase documents a Read-Only role limited to SELECT; snippets run as supabase_read_only_user with predefined pg_read_all_data. |
The documentation’s footnote indicates plan availability matters; check the current project configuration. | Not stated in the cited Supabase documentation. | The managed role can reduce setup work, but its availability and predefined scope may not fit every project. |
Sources for the platform-specific details are Microsoft’s SQL Server least-privilege and Database Engine permission guidance, Microsoft’s Azure SQL roles and row-level security guidance, Snowflake’s read-only-role documentation, AWS’s PostgreSQL role guide, and Supabase’s SQL snippets documentation. Specific permissions and behavior can vary by product, configuration, and version.
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What should I check about tables created later?
A grant that covers current tables may not cover tables added later. Decide whether new objects should be readable by default or reviewed and granted one at a time; the safer administrative choice depends on how predictable and controlled the schema is.
Snowflake documents future-table grants separately from grants on existing tables. AWS’s PostgreSQL guidance likewise distinguishes SELECT grants on existing tables from default privileges relevant to future tables. With either approach, check which role or owner creates the future objects and how the deployed database handles those grants; do not assume a current-table permission automatically follows newly created objects.
There are two common policies. Under an explicit-review policy, a new table remains unavailable until someone grants access. This limits accidental exposure but can cause a feature to fail when its schema is intentionally expanded. Under an automatic-inclusion policy, future tables in a defined scope become readable automatically. This reduces maintenance but means a sensitive table added to that scope can be exposed without a separate AI-access review. Document which policy you chose and test it by creating a controlled test table.
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How can I verify that the AI account is truly restricted?
Test the effective permissions while authenticated as the dedicated AI identity—not as an administrator who can see or change more. A grant list can help explain the configuration, but the behavior that matters is what the actual connection can do.
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- Try to read an object deliberately left out of scope and confirm access is denied.
- Attempt representative INSERT, UPDATE, DELETE, and other data changes, and confirm they are denied.
- Attempt representative schema changes such as creating, altering, or dropping an object, and confirm they are denied.
- If row-level security is part of the design, check that the identity sees only the intended rows through the same application path the AI feature will use.
- Check a newly created table if your policy covers future objects, or confirm that access is withheld until an administrator grants it if new objects require review.
- Verify that the connection string, secret, role assumption, and connection pool all resolve to the dedicated principal. A correct role definition is ineffective if the application silently uses another credential.
Record the identity and scope used for each check so you can distinguish a failure in the permissions design from a connection accidentally using another account. If a test succeeds unexpectedly, stop using that connection for the AI feature until you have found and removed the broader permission path. Repeat these checks after role changes, schema migrations, ownership changes, and platform upgrades.
Common implementation pitfalls
- Relying on the model’s instruction: “Only write SELECT” is a generation preference, not an authorization control.
- Reusing an application credential: The application may need writes or access to unrelated tables. Sharing it defeats independent scoping and makes audit attribution harder.
- Choosing a convenient database-wide role: A role such as SQL Server’s
db_datareadercan expose every table, including tables added for unrelated business functions. - Forgetting non-table permissions: Snowflake requires database, schema, and warehouse USAGE in addition to table SELECT; PostgreSQL requires CONNECT and schema USAGE in addition to table SELECT in the AWS example.
- Assuming current grants cover future tables: Snowflake and PostgreSQL document separate mechanisms for later-created objects.
- Ignoring rows: Table-level SELECT does not automatically express a tenant, region, or customer boundary. Use row-level security where the engine and architecture support it.
- Testing with an overprivileged account: An administrator can make a broken design look successful. Test the exact principal and connection path used in production.
- Treating a managed role as universal: Supabase’s documented SQL-snippet Read-Only role depends on project-plan availability and has predefined behavior; confirm that it is enabled and appropriate for your project.
What does read-only access not guarantee?
Read-only permissions restrict database operations according to the engine’s authorization model, but they are not a complete safety or privacy plan for an AI query feature.
- Query cost and resource use: The platform guidance cited here does not establish hard limits for expensive or long-running generated SELECT queries. Find and configure the relevant controls for your engine before assuming a read-only account cannot consume excessive resources.
- Side effects: The cited guidance does not settle whether functions or other query-accessible features can have side effects under a particular configuration. Review the functions and execution behavior available to the account in your own platform.
- Data sent to the AI system: A database grant controls what the identity can read; it does not, by itself, establish how query results are handled by the AI application or service. Assess that data flow separately.
- Prompt injection or misleading requests: A read-only account can still return data it is permitted to see. Database authorization does not replace controls over what data the feature may expose in its responses.
- Complete operational isolation: Read-only status does not automatically isolate workloads, guarantee availability, or prevent a permitted query from affecting shared resources. Those concerns require controls specific to the database platform and deployment.
A practical decision checklist
Before enabling the feature, answer these questions in writing:
- Which database, schemas, tables, views, columns, and rows does the feature need?
- Which sensitive objects are intentionally excluded?
- What dedicated principal will the feature use, and where is that credential stored?
- Is the chosen scope object-level, schema-level, database-wide, or row-level, and why is any broader scope acceptable?
- Does the engine require database, schema, warehouse, or other compute USAGE in addition to SELECT?
- Are future tables reviewed individually or included automatically, and which owner/default-privilege mechanism controls that behavior?
- Have successful reads, denied out-of-scope reads, denied writes, denied DDL, and row policies been tested with the real connection path?
- Which separate controls address query cost, side-effecting functions, result handling, and prompt-driven data disclosure?
This checklist turns “read-only” from a prompt convention into a documented authorization design that can be reviewed and maintained.
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Frequently Asked Questions
Can a read-only database user still run a query that returns sensitive information?
Yes, if the user has SELECT permission on the object containing that information. Database authorization controls what the identity can read; it does not decide whether a particular result is appropriate to disclose in an AI response.
Does telling the AI to use SELECT make its database connection read-only?
No. A prompt can influence generated SQL, but it does not change the database account’s permissions. The database identity still needs restricted grants.
Should I use a database-wide reader role for convenience?
Only when access to every table in the database is acceptable. SQL Server’s db_datareader is convenient but broad; Microsoft describes custom roles and object-level SELECT for tighter scope.
Can I use the same database account for the AI feature and my application?
A shared account makes it harder to give the AI feature a narrower permission set and to test which access path produced an action. A separate identity lets you scope and verify the AI query path independently.
Why does a table grant still produce a permission error in Snowflake?
Snowflake’s documented custom read-only setup also requires USAGE on the database, schema, and warehouse. SELECT on the table alone does not provide those container and compute permissions.
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Will a grant on all current tables cover tables created next month?
Not necessarily. Snowflake documents a separate future-tables grant, and AWS’s PostgreSQL guidance distinguishes existing-table SELECT from default privileges for later tables. Check the engine’s owner and default-grant behavior.
How do I restrict an AI query to a tenant’s rows?
Use row-level security where your platform supports it, such as Azure SQL’s documented row-level security feature. Confirm that the application passes the intended identity or session context and test the policy through the actual AI connection path.
Is Supabase’s SQL-snippet Read-Only role available on every plan?
Supabase documentation describes a Read-Only role limited to SELECT and running as supabase_read_only_user with predefined pg_read_all_data, but its footnote indicates plan availability matters. Check the current project configuration.
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The cited vendor guidance does not compare replicas with permission-based controls or establish when a replica is appropriate. A replica would not, by itself, define which tables or rows the AI identity is authorized to read.
Does read-only access stop expensive or side-effecting queries?
Not automatically. The cited permission guidance does not establish hard execution-cost limits or settle the behavior of functions with side effects. Review platform-specific controls and execution behavior separately.
The Bottom Line
Give the AI feature its own database principal, grant only the required SELECT access, and add the database, schema, warehouse, or other usage permissions its engine requires. Choose object, schema, database, and row scope deliberately; handle future tables explicitly; then test reads, denied reads, writes, DDL, and row policies with the real connection. Read-only authorization is essential, but query cost, side effects, and AI data handling need separate controls.
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