When should I use Row Level Security (RLS) instead of checking permissions only in my backend?

[ammiyo]

I'm building a Supabase application where all requests currently go through my backend before reaching the database.

If my backend already validates authentication and authorization, is it still considered best practice to enable Row Level Security (RLS) on every table?

What are the advantages of keeping RLS enabled in this architecture, and are there any performance or maintenance trade-offs?

[isthatpratham]

Even if all requests go through your backend, enabling RLS is generally considered a defense-in-depth best practice.

Here's why:

Database-level security: RLS ensures that data access rules are enforced directly by PostgreSQL, regardless of which application or service is making the request.
Reduced risk of mistakes: If a new API endpoint, Edge Function, or future service accidentally bypasses an authorization check, RLS still prevents unauthorized access.
Safer client-side access: If you later decide to let your frontend query Supabase directly using the anonymous or authenticated keys, your existing RLS policies continue to protect your data without requiring major changes.
Centralized authorization: Instead of duplicating permission logic across multiple services, the database becomes the single source of truth for access control.

As for performance, simple RLS policies usually have minimal overhead because PostgreSQL evaluates them as part of query execution. The main cost is maintaining well-designed policies, especially for complex authorization rules involving multiple tables or subqueries.

A common production pattern is:

Enable RLS on all user-facing tables.
Use authenticated policies for normal users.
Use the service_role key only in trusted backend environments where elevated access is intentionally required, since it bypasses RLS.

This approach provides strong security while keeping your architecture flexible as your application grows.

[Krishnakr21]

keep RLS enabled even if your backend handles authentication and authorization.

Security: Provides defense in depth if a backend authorization check is accidentally bypassed.
Future-proofing: Protects data if you later allow direct Supabase access, add Edge Functions, or introduce another service.
Database-level protection: Authorization rules remain enforced at the data layer.
Performance: Usually minimal overhead, provided RLS policies are simple and properly indexed.
Trade-off: Some additional policy maintenance and potentially duplicated authorization logic.

For a backend-only architecture, I'd use backend authorization as the primary layer + RLS as a safety net.

[cekuu35]

Use RLS when:

• Multi-tenant data (tenant_id, user_id, org_id columns)
• Direct client-to-DB access (Supabase client, PostgREST, realtime)
• Defense-in-depth: even if backend is compromised, data stays isolated
• Compliance requires row-level audit trails

Backend-only checks are fine when:

• Single-tenant / admin-only internal tools
• All DB access goes through a vetted API layer you control
• Complex cross-table authorization logic (RLS = single-table)

Hybrid (recommended for most):

• RLS as safety net (owner = auth.uid() on all tenant tables)
• Backend for business logic (quotas, workflows, complex rules)
• Frontend reads via Supabase client (RLS enforced), writes via API (double-checked)

Cost of RLS: ~1-5ms per query. Cost of leak: GDPR fines, trust loss, rewrites.

Free decision checklist: https://github.com/cekuu35/supabase-rls-leak-demo

[https-shubhamsahu]

One thing the answers above don't cover, and it decides whether RLS does anything at all in your setup: which role your backend queries as.

If your backend talks to Supabase with the service_role key, or connects to Postgres directly as postgres, RLS is bypassed completely. In that setup "RLS as a safety net" is switched on but never evaluated, so a missed check in the backend is still a leak. For RLS to back up your backend, the backend has to query as the user:

• With supabase-js on the server: create a client per request with the anon key and the user's access token as the Authorization: Bearer <jwt> header (global.headers in createClient). Queries then run as authenticated, and auth.uid() is that user.
• With a direct Postgres connection: do it per transaction, e.g. begin; set local role authenticated; select set_config('request.jwt.claims', '<claims json>', true); ...; commit;. Use set local/is_local = true so the role and claims can't stick to a pooled connection and apply to the next request.

Keep service_role for the few jobs that really need to cross users (webhooks, cron cleanup, admin tools), and keep those code paths small, because they're the part RLS can't catch.

Two smaller points:

• Even if every request goes through your backend, the Data API is still reachable at your project URL, and the anon key isn't a secret. A table in an exposed schema with RLS off is readable by anyone who has that key. Either enable RLS (no policies = deny for anon/authenticated) or remove the schema from the exposed schemas in the API settings.
• RLS isn't limited to one table. Policies can use subqueries or call a security definer helper (e.g. is_member(org_id)) for cross-table rules. For performance, write (select auth.uid()) instead of auth.uid() so it's evaluated once per statement rather than per row, and index the columns your policies filter on.

I ran into the first point building a file-sharing app on Supabase. It's easy to assume the policies are protecting you when the connection never goes through them.