WHERE tenant_id = $tenant is useful query logic, but it is not an independent authorization boundary when the application controls the query. If that condition is omitted or changed, the database needs its own row-access rules to stop an ordinary application query from reaching another tenant’s rows. PostgreSQL row-level security (RLS) can provide that layer for normal table access, provided roles, policies, ownership and bypass privileges are configured deliberately.
Why a tenant filter in application SQL is not enough
Suppose a RAG retrieval query selects chunks from a shared table and adds WHERE tenant_id = $tenant. That condition limits rows when the query is constructed and executed as intended. But if application-controlled SQL omits the condition, or a code path builds a different query, the predicate cannot independently enforce authorization.
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PostgreSQL RLS adds a database-side row policy. The PostgreSQL Global Development Group’s PostgreSQL 18 documentation says that, once row security is enabled, normal access to select or modify table rows must be allowed by a policy. If no applicable policy allows access, PostgreSQL uses default deny. RLS is therefore an additional control for ordinary table access, not a substitute for correct application queries or database permissions. PostgreSQL row-security documentation
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RLS works alongside SQL privileges
A policy does not grant access to a table. The database role still needs the relevant table or column privileges, and an applicable RLS policy must permit the requested row operation. In practice, both layers matter: grants determine which operations a role may attempt, while policies constrain which rows those operations may access.
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That distinction also defines the boundary of the protection. RLS can constrain normal table access even when a query lacks the expected tenant predicate, but it does not make every database actor or configuration safe. In particular, privileged roles and table ownership affect whether policies are applied.
Write policies need their own checks
RLS policy design must cover reads and writes. PostgreSQL uses USING to determine which existing rows are visible or eligible for an operation. WITH CHECK validates rows being inserted or updated. A read condition alone does not establish that a write cannot create or move a row into the wrong tenant.
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This illustrative policy uses the same tenant expression to constrain eligible rows and validate written rows:
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CREATE POLICY tenant_chunks ON rag_chunks
FOR ALL
USING (tenant_id = current_setting('app.tenant_id')::uuid)
WITH CHECK (tenant_id = current_setting('app.tenant_id')::uuid);The example shows policy shape, not a complete deployment recipe. Its safety depends on how the application establishes the tenant identity and how the database connection is managed. A session value is not trustworthy merely because a policy reads it. Ensure the database receives an authenticated tenant identity through a controlled path, and test transaction and connection reuse in the actual application stack. PostgreSQL documents that, where applicable, a policy without an explicit WITH CHECK can use its USING expression as the check expression; spelling out both makes the read and write intent easier to inspect. Verify policy behavior for the PostgreSQL version and commands you use. PostgreSQL row-security documentation
Know which roles can bypass row policies
“We use RLS” is not a complete security statement unless the roles and table ownership are reviewed. PostgreSQL documents three important exceptions:
- Superusers bypass row security.
- Roles with the
BYPASSRLSattribute bypass row security. - Table owners normally bypass policies on their tables.
ALTER TABLE ... FORCE ROW LEVEL SECURITYsubjects the owner to row security.
Keep application connections away from superuser, BYPASSRLS, and table-owner roles unless there is a carefully justified reason. Review the roles actually used by application queries and background jobs, not only the role used for migrations. PostgreSQL row-security documentation
RLS and pgvector solve different parts of isolation
Database authorization and vector retrieval quality are related design concerns, but they are not the same requirement. RLS controls normal access to rows. The physical layout and search method determine how approximate vector search behaves across tenants.
According to the pgvector project README, exact nearest-neighbor search is the default and provides perfect recall. HNSW and IVFFlat indexes are approximate methods that trade some recall for speed. With approximate indexes, filtering is applied after the index scan. If a tenant filter matches only a small fraction of indexed rows, the search can return fewer matching rows than the requested limit. pgvector README
The pgvector project also warns that vectors in a shared approximate index can affect recall and speed for other tenants. Its guidance for multi-tenant isolation is to use list partitioning or separate tables. That is a recommendation about approximate-index isolation; it does not mean RLS alone resolves index behavior, or that one storage layout is best for every workload. pgvector README
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare retrieval layouts by what they actually change
| Search or storage choice | Retrieval behavior | Tenant-layout implication |
|---|---|---|
| Exact nearest-neighbor search | Default pgvector search; perfect recall. | The cited project guidance about shared approximate indexes does not describe exact search as having that approximate-index effect. |
| Shared approximate index with filtering | Trades some recall for speed; filtering occurs after the index scan, so fewer than the requested number of matches may be returned. | Vectors in the shared approximate index can affect recall and speed for other tenants, according to pgvector. |
| List partitioning | pgvector documents partitioning as an approach when filtering by many distinct values. | Recommended by pgvector as an option for multi-tenant approximate-index isolation. |
| Separate tables | pgvector lists separate tables as an approach to consider for filtering and tenant isolation. | Recommended by pgvector as an option for multi-tenant approximate-index isolation. |
This comparison does not establish which option will be fastest, cheapest, or simplest for a particular deployment. Those outcomes depend on tenant count, query patterns, recall requirements, index maintenance, and the operational cost of partitioning or separate tables. Test the choices against the workload rather than treating a layout recommendation as a benchmark result.
The pgvector README is a rolling master branch document, not a pinned release reference. Check the documentation and available settings for the extension version actually deployed. pgvector README
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Use a deployment-specific review and test plan. The following checks identify the controls and outcomes to verify; they are not a substitute for testing the real application and database configuration.
Quick Recap
- Inspect grants and role attributes for every application-facing database role, including whether any has
BYPASSRLS. - Confirm RLS is enabled on every table containing protected tenant data, and review policies for each relevant command.
- Check table ownership and whether owner behavior is appropriate; use
FORCE ROW LEVEL SECURITYwhere the owner must also be subject to policies. - Test reads and writes with identities for multiple tenants, including queries that omit the application’s tenant predicate. Confirm that reads cannot expose another tenant’s rows and writes cannot assign rows to the wrong tenant.
- Test retrieval result counts and recall with realistic tenant filters and data distribution. Include cases where approximate search returns fewer matches than the requested limit.
- Evaluate exact search, shared approximate indexes, partitioning, or separate tables against the deployed pgvector version and workload.
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