Using a Supabase Backend Behind Your Store App

Many AI app builders and coding agents choose Supabase as the default backend: a Postgres database, authentication, file storage and generated APIs in one service. It is a sensible choice for mobile apps, provided you configure it deliberately. The most serious risks come from defaults that were fine for a demo and are not fine for thousands of users. This guide covers the main points. Check Supabase's current documentation for exact features.

Understand which key is which

Supabase projects have a public client key and a privileged service key. The client key is designed to be included in apps, which means anyone can extract it from your binary. Its safety depends entirely on your database rules. The service key bypasses those rules and must never ship in a mobile app or any front-end code. If an AI agent ever placed the service key in client code, treat it as compromised and rotate it.

Row level security is not optional

Row level security (RLS) policies decide which rows each user may read or write. Without them, a table exposed through the API can be readable, or even writable, by anyone holding the public key. Review every table:

• Is RLS enabled?
• Does each policy reference the authenticated user, for example by user ID, rather than being permissive for everyone?
• Are there separate rules for select, insert, update and delete?
• Are views and functions respecting the same rules?
• Have you tested with two different user accounts to confirm that user A cannot see user B's data?

Generated policies can look right and still leave gaps, so a human review here is high-value work. See also our security review checklist.

Authentication inside a native app

Email magic links, OAuth providers and password resets often involve redirects. In a mobile app you must handle them with deep links or universal links, otherwise users end up in a browser and never return. Plan these flows early and test them on real devices; our guide to deep links for converted apps helps. If you offer social sign-in, check Apple's current rules about equivalent sign-in options. And provide an in-app account deletion path that removes or anonymizes the user's data, as both stores expect.

Storage, realtime and functions

1. Storage buckets: default to private, and serve files through signed URLs unless they are truly public.
2. Realtime: subscribe only to what the user may see, and remember it consumes connections.
3. Edge functions or server code: use them for secrets, payments and third-party APIs; the app calls the function, not the provider directly.
4. Rate limits and abuse: protect sign-up and expensive endpoints.

Environments, backups and region

Use separate projects for development, staging and production, and keep schema changes in versioned migrations rather than clicking in a dashboard. Check the backup options of your plan and test a restore. Choose a region that fits your compliance needs; EU regions are often preferred for GDPR-relevant data. Our article on deploying an AI-generated app covers these practices in more depth.

A short test plan makes these checks repeatable. Create two users, add data as each, and try to read, update and delete the other's records through the same API calls your app uses. Try the calls without any login. Try them with an expired session. Then repeat after every schema change. If your team uses coding agents, add this plan to the repository instructions so the agent runs it, and a human reads the results, before any migration is merged.

How apprat.io approaches Supabase backends

Coding agents are good at drafting schemas, policies and client code quickly. At apprat.io, an AI Code Engineer then reviews each policy and migration, tests with multiple accounts, verifies that no privileged key reaches the client and takes accountability for the configuration, under ISO 27001 certified information security processes. We also support apps built on Lovable or Base44 that use such backends. For an independent review of your setup, book a call for free.

Scaling thoughts

As usage grows, watch query performance, indexes, connection limits and costs. Add monitoring before you need it, and revisit policies whenever the data model changes. A prototype's schema is rarely the schema you want at ten thousand users; see scaling an AI-built MVP for a staged approach.