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Give an AI query feature access through a dedicated database identity that can read only the data it needs. Grant the minimum required SELECT permissions at the narrowest practical scope; do not rely on a prompt saying “read-only” or connect the feature as an administrator, database owner, or broadly privileged application account. The exact grants vary by database, and a read-only role limits database permissions—not every risk posed by generated queries or the data they can retrieve.
What does read-only access actually mean for an AI query feature?
Read-only access is an authorization boundary enforced by the database. The AI feature connects using a database identity with permission to read selected data, but without permission to change data or schema. A prompt that asks the AI not to write SQL is not a substitute: the database must enforce the restriction even if the model generates an unintended statement.
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Keep that identity separate from administrator, database-owner, and ordinary application credentials. Microsoft’s SQL Server least-privilege guidance advises against using administrator or database-owner accounts for tasks that need fewer permissions. A separate identity also makes it possible to grant, test, and revoke the AI feature’s access without changing unrelated application access.
“Read-only” does not necessarily mean “can see only harmless information.” A principal allowed to select sensitive records may still read them. Nor does the permission model alone establish protections against expensive queries, side-effecting functions, prompt injection, or exposure through the AI system’s data flow. Treat database grants as one necessary control, not a complete AI safety design.
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The practical objective is narrower than “make SQL safe.” It is to ensure that the database itself rejects data-changing and schema-changing operations for this connection. That boundary should remain true if a model is confused, an instruction is malicious, or an application bug sends an unexpected statement. Application prompts, SQL-generation templates, and output filters can be useful additional measures, but none should be the only enforcement point.
How should I choose the right access scope?
Start with the data the feature must retrieve, then grant access at the narrowest level your database supports and your use case can maintain. Each broader scope can make administration easier, but it also increases the amount of data the AI identity can read.
| Scope | What it controls | When it may fit | Main trade-off |
|---|---|---|---|
| Object, such as a table | Access to a specifically named database object | The feature needs a known, limited set of tables or other supported objects | More precise grants require more maintenance as the set of objects changes |
| Schema | Access to objects grouped within a schema, depending on the database’s permission model | The objects in that schema genuinely belong in the AI feature’s scope | Can include more data than intended if unrelated objects share the schema |
| Database-wide reader role | Read access across the database; SQL Server’s db_datareader, for example, can read all tables |
Broad access is explicitly acceptable for the feature | Convenient, but exposes every table within that role’s scope rather than only task-specific data |
| Row-level policy | Which rows a querying user is allowed to access, where the database supports row-level security | Different users or requests must see different subsets of rows | Requires correctly configured policies and an architecture that supplies the intended identity to the database |
Think of scope as multidimensional. The database determines which database the principal may enter, which schemas it may use, which tables or views it may select, and, on systems that support it, which rows policies allow. A platform may also require access to a compute resource or warehouse before any table query can run. Granting table SELECT while omitting a required container permission can make a connection fail; granting broad container access does not automatically justify broad table access.
Object-level access is usually the clearest starting point when the feature has a stable query model. A schema grant can be reasonable when every object in that schema is intentionally part of the feature’s vocabulary and new objects are reviewed as part of schema ownership. A database-wide reader is an administrative convenience, not a default recommendation: SQL Server’s db_datareader can read all tables, which may include unrelated operational or confidential data.
Do not assume that permissions have identical meanings across engines. Microsoft’s SQL Server permissions documentation describes object-level SELECT as its most granular example and explains that higher-level grants can flow to child objects. Azure SQL also documents row-level security. Those are platform-specific behaviors, not portable rules for every database.
How do I set up the AI database identity?
Use this sequence as a design and verification process. The actual commands and labels depend on your database engine, deployment, and version; the platform examples below illustrate permission requirements rather than provide a universal SQL recipe.
- Inventory the data needed. List the database, schemas, tables, views, and—if applicable—rows the feature must query. Identify sensitive fields and objects that do not support the task, and leave them out of scope.
- Create a dedicated principal. Set up a user, role, or other database identity specifically for the AI query path. Do not reuse a superuser, administrator, database owner, or credential whose existing permissions are broader than necessary.
- Grant only the needed reads. Prefer grants on individual objects where practical. Use a schema-level grant only when all relevant objects in that schema belong in scope. Before using a database-wide reader role, confirm that access to every table it covers is acceptable.
- Add required container or compute permissions. Some platforms require more than table-level
SELECTbefore a principal can run queries. In Snowflake’s documented example, the custom read-only role needsUSAGEon the database, schema, and warehouse, plusSELECTon the selected tables. - Decide how future objects should be handled. Choose whether new tables should become readable automatically or require a deliberate grant. Existing-object access and future-object access can be separate permissions; configure the behavior that matches your change-management and exposure requirements.
- Apply row restrictions if the task needs them. Where supported, row-level security can limit which records a user sees. Confirm that the database receives the intended user identity and that the policies match the application architecture before relying on this control.
- Test using the AI identity itself. Verify that it can read the intended objects, cannot read excluded data, and cannot perform representative write or schema-changing operations. Testing as an administrator does not show what the AI principal can actually do. Re-test after changes to grants, policies, schemas, or the feature’s connection identity.
The final test is an operational check of the effective permissions; it is not a substitute for the platform’s own authorization documentation. Keep the identity’s credentials and grant-management process specific to this access path so a later change does not silently turn it into a broader account.
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Make the inventory concrete. Record the exact database and schema names, the tables or views that answer supported questions, and any rows that must be separated by tenant or user. If a table contains both useful and sensitive attributes, do not assume that a table grant will hide selected columns; verify whether the engine offers a suitable view or column-specific control. The platform examples here establish object and, in some cases, row controls, but they do not create one universal column-permission rule.
Use views or other deliberately designed objects when the feature should query a stable shape rather than the underlying tables, provided the database’s permission model supports that design. Keep excluded objects outside the role’s grants instead of relying on the model to avoid naming them. Review inherited permissions and role membership as well as direct grants: a narrowly written grant can still be defeated by an inherited database-wide role.
How do read-only permissions differ across database platforms?
The same goal—letting a feature read data without changing it—has different permission components on different systems. Use the current documentation for the exact engine and version you deploy; do not copy one platform’s grant syntax into another.
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| Platform | Documented read-access approach | Important scope or setup detail |
|---|---|---|
| SQL Server | db_datareader reads all tables in a database and cannot make changes. Custom user-defined roles can provide more granular permissions. |
Object-level SELECT is described as the most granular example in Microsoft’s Database Engine permissions guidance. Higher-level permissions can flow to child objects. |
| Azure SQL Database | db_datareader reads every table; Microsoft describes custom roles as a way to scope access more carefully. |
Row-level security can restrict which rows a user may access. Validate identity propagation and policies for the application design. |
| Snowflake | A custom read-only role can receive SELECT on selected tables. |
The documented example also requires USAGE on the database, schema, and warehouse. Current-table grants and future-table grants are separate. |
| PostgreSQL guidance from AWS | The role setup separates database CONNECT, schema USAGE, and table SELECT. |
Grants on existing tables do not cover future tables; default privileges are relevant. Confirm syntax and owner/default-privilege behavior against the deployed PostgreSQL version. |
| Supabase SQL snippets | Supabase documents a Read-Only role for SQL snippets, limited to SELECT; snippets run as supabase_read_only_user with predefined pg_read_all_data. |
The documentation notes that plan availability matters. Check the project’s current configuration before depending on this option. |
These examples are not interchangeable. A role that can read all tables may be too broad for production, while a narrowly scoped role may require additional grants for schemas, warehouses, or other platform containers. Confirm both the identity’s effective permissions and the rules for objects created later.
SQL Server illustrates the convenience-versus-scope decision especially clearly. Microsoft describes db_datareader as able to read every table while being unable to make changes. That can be acceptable for an isolated reporting database, but it is more access than a task-specific production feature usually needs. A custom user-defined role lets an administrator grant only selected objects. Microsoft’s permissions hierarchy also matters: a grant at a higher level can flow to child objects, so review inherited access rather than checking only the direct SELECT statements.
Azure SQL follows the same broad reader pattern but adds documented row-level security capabilities. A policy can restrict rows according to the user running a query, which is useful when one connection must serve different users or tenants. The policy is effective only if the application passes the intended identity to the database and the policy is written for that identity model; a connection that always appears as one shared principal cannot automatically express each end user’s permissions.
Snowflake demonstrates why table grants are not the whole setup. The documented custom role needs USAGE on the database and schema and on the warehouse used to execute the query, in addition to SELECT on selected tables. Snowflake also separates grants for existing tables from grants on future tables. Decide deliberately whether a newly created table should appear in the AI feature’s data domain or wait for review.
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For PostgreSQL, AWS guidance separates database CONNECT, schema USAGE, and table SELECT. A role can therefore have a table grant but still fail to use the database or schema. Grants on all existing tables do not automatically cover tables created later; default privileges govern future objects and depend on the object owner and deployment details. Check the exact syntax and ownership behavior for the PostgreSQL version you operate.
Supabase documents a Read-Only role for SQL snippets that is limited to SELECT. Those snippets run as supabase_read_only_user with predefined pg_read_all_data. The documentation notes that plan availability matters, so verify that the feature is enabled for the current project rather than assuming the role exists everywhere.
How should I handle existing and future tables?
Many permission mistakes occur when an administrator grants today’s objects and forgets that tomorrow’s objects follow a different rule. Ask two separate questions: which objects are readable now, and which objects will become readable when they are created later?
For a narrow AI feature, requiring an explicit grant for each new table is often easier to review. It creates a deliberate checkpoint for sensitive data and prevents an unrelated table added to a shared schema from silently entering the feature’s scope. Automatic future-object grants reduce maintenance, but they are appropriate only when the schema boundary is trusted and every future object is expected to be suitable.
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What should I verify before connecting the AI feature?
Review the permissions from the database identity’s perspective, not from the administrator’s. A short checklist helps catch overbroad access and missing platform requirements before the feature is put into use.
- The AI connection uses its own database principal, not an owner, administrator, or general-purpose application account.
- Each granted database, schema, table, or row scope has a clear reason to be accessible.
- A database-wide reader role is not being used merely for convenience when narrower grants would meet the need.
- Required platform permissions—such as database, schema, or warehouse usage—are present without granting unrelated access.
- The behavior for future tables is intentional: newly created objects either remain unavailable until reviewed or are included only when that is appropriate.
- Where row-level security is used, the intended identity reaches the database and the applicable policies have been checked.
- Tests performed as the AI identity confirm the intended reads and reject representative writes and schema changes.
Expand the test beyond one successful query. Try a permitted read from every intended object and a read from an excluded object. Attempt representative data changes such as an insert, update, or delete, and representative schema changes such as creating or altering an object; the database should reject them for this identity. Also test access through inherited roles, because a hidden membership can make the effective permission set broader than the grant list suggests.
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Repeat these checks after a schema migration, role change, connection-pool change, or alteration to row-level policies. A feature may accidentally switch credentials while its application code remains unchanged. Keep a record of which identity was tested, which objects were in scope, and whether future-object behavior was enabled, so a later review can compare the effective state with the intended design.
The final test is an operational check of the effective permissions; it is not a substitute for the platform’s own authorization documentation. Keep the identity’s credentials and grant-management process specific to this access path so a later change does not silently turn it into a broader account.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do read-only grants not guarantee?
They restrict the database actions permitted to the identity, but they do not establish that every permitted query is safe, inexpensive, or appropriate to expose to an AI system. The platform guidance described here does not settle hard execution-cost limits, risks from functions with side effects, or protections against prompt injection and AI data exposure.
Do not infer that “cannot write” means “cannot cause a problem.” A query can still retrieve data beyond what a user should see if the grants are too broad; row-level rules, where supported, address a different part of that problem. Likewise, whether a particular function or generated statement can have indirect effects requires platform-specific analysis. Do not treat read-only grants as proof that such cases are harmless.
Keep the claim precise: the database identity has only the permissions granted to it, as enforced by that platform. For additional controls over query execution or AI data handling, consult guidance specific to the database and application architecture rather than assuming the read-only role supplies them.
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Frequently asked questions
Frequently Asked Questions
Can a read-only database account still access confidential information?
Yes. Read-only describes what the account may do, not whether the records it can retrieve are sensitive. Confidentiality depends on limiting the granted data scope and, where appropriate, using row-level policies supported by the database.
Does granting SELECT mean the AI can read only certain columns?
Not necessarily. The platform examples here establish object-level and, in some cases, row-level controls; they do not establish a portable column-level rule. Check the exact engine’s permissions documentation before assuming a table grant hides particular fields.
Can read-only permissions stop a generated query from using too much compute?
They are not, by themselves, evidence of a hard execution-cost limit. Whether and how to limit query cost depends on the database platform and requires controls beyond the read grants described here.
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No. The permissions prevent actions outside the identity’s grants, but they do not establish that all permitted queries are harmless or that functions cannot have side effects. Assess those risks for the specific database and application rather than treating the role as a complete safety boundary.
Should I use the database’s built-in reader role for simplicity?
Only when its full scope is acceptable. SQL Server’s db_datareader, for example, can read every table. A custom role with object-level grants is narrower and is preferable when the AI feature needs only a defined subset.
Do grants on current tables automatically cover tables created later?
Not necessarily. Snowflake documents separate future-table grants, and AWS PostgreSQL guidance distinguishes existing-table grants from default privileges for later tables. Decide explicitly whether new objects require review or should be included automatically.
Why might SELECT still fail on Snowflake or PostgreSQL?
A table grant may not supply other required permissions. Snowflake’s documented setup also needs USAGE on the database, schema, and warehouse. PostgreSQL guidance separates database CONNECT, schema USAGE, and table SELECT.
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Can row-level security replace a dedicated AI identity?
No. Row-level security can restrict which rows a user sees where supported, but it depends on the identity supplied to the database and correctly configured policies. Use a dedicated principal and verify that the application architecture propagates the identity the policy expects.
Is Supabase’s SQL-snippet Read-Only role available on every project?
Supabase documents the role as limited to SELECT, running as supabase_read_only_user with predefined pg_read_all_data, but its documentation notes that plan availability matters. Check the current project configuration.
What should I do if the AI needs data from several schemas?
Grant access to the specific objects across those schemas, or use schema-level grants only when every object in each schema is intentionally in scope. Also grant any required database, schema, or compute usage permissions and test the combined effective access.
The Bottom Line
Create a separate database principal for the AI query path, grant only the required reads at object, schema, and row scope, and add platform-specific usage permissions. Decide explicitly how future tables are handled, test effective permissions with the AI identity, and remember that read-only authorization does not by itself solve query-cost, side-effect, prompt-injection, or data-exposure risks.
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