Row-Level Security in REST APIs: Protecting Data Row by Row
A request hits your REST API. It’s clean. It’s valid. But it’s not safe—unless every row returned is meant for the eyes that asked for it.
Row-level security (RLS) is the line between controlled access and accidental data leaks. For REST APIs, it means enforcing permissions at the granularity of individual rows inside your data store. Without it, a single careless query can expose sensitive information across tenants, teams, or users.
RLS starts at the database. Define policies that match your authorization logic—user IDs, tenant IDs, or any attribute tied to the request context. PostgreSQL, for example, lets you set CREATE POLICY rules that filter rows per role. But database policy alone isn’t enough. In distributed systems, REST APIs often cache, aggregate, or transform data, which can bypass RLS if not enforced everywhere data is touched.
A secure REST API with row-level security must integrate three layers:
- Authentication – Verify who the caller is. Without strong auth, RLS is meaningless.
- Authorization – Map the caller to roles and claims that determine visibility.
- Query Enforcement – Apply filters at query time, either in the ORM, query builder, or directly in SQL. Never rely on client-provided filters for security.
Performance matters. Filtering at the DB layer using indexed attributes is the most reliable and efficient method. Avoid fetching broad datasets into memory to apply RLS in application code—this increases attack surface and latency.
Testing RLS is critical. Create scenarios where unauthorized users attempt to fetch restricted rows. Verify that policies hold under edge cases like joins, views, and bulk exports. Continuous monitoring should catch attempts to request forbidden data patterns.
When REST API design aligns with row-level security, you get consistent, enforceable rules across all endpoints. The API becomes a trusted boundary. Without RLS, every response is a gamble.
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