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The AI App Audit Checklist

Built something with Lovable, Cursor, Bolt, or another AI tool? Run it through this before you put real users on it. Thirty-plus checks across the six areas AI-generated apps most often miss.

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Security

AI tools rarely think about security unless pushed, and even then they miss things. These are the gaps that turn into public incidents.

No secrets, API keys, or tokens are committed to the repo or shipped in the client bundle.
Every server endpoint validates and authorizes the request — the interface is not the security layer.
Rate limiting and abuse protection are in place on public and authentication endpoints.
Dependencies are pinned and free of known critical vulnerabilities (npm audit or equivalent).
User input is validated and sanitized on the server before it reaches the database.
Stack traces and internal error details are never shown to end users in production.

Authentication & access

Generated login screens often look right but enforce nothing underneath. Anyone who knows the pattern can reach data they should not.

Authentication is enforced on the server for every protected route and action.
Row-level security (or equivalent) restricts each user to their own data — the single most common Lovable and Supabase gap.
Roles and permissions are checked server-side, not hidden in the UI.
Sessions expire, and password reset and email verification flows actually work.
Admin and internal routes cannot be reached by guessing the URL.

Data & privacy

If you handle personal data in Europe, this is work, and it is not optional.

The database has a real schema with constraints, not free-form fields that accept anything.
Backups run automatically and a restore has actually been tested at least once.
Personal data has a lawful basis and a privacy policy that matches what the app really does (GDPR).
You can export and delete a user's data on request.
Personal data is not logged in plaintext or sent to third-party tools without consent.

Tests & quality

Without tests, every AI edit is a gamble — you fix one screen and quietly break another, and you find out from a customer.

The flows that make money or hold sensitive data (sign up, payment, core action) have automated tests.
There is error handling wherever the app talks to a database, an API, or a payment provider.
The app degrades gracefully on bad input, a lost connection, and a double-clicked button.
A CI check runs the tests on every change before it ships.
Linting and type checks pass; there are no silent runtime type errors.

Deployment & monitoring

You cannot fix what you cannot see. This is the early-risk floor.

Production, staging, and local environments are separated, each with its own config and secrets.
Uptime monitoring and error logging alert you before your customers do.
Deploys are repeatable — one command or one click — not manual and undocumented.
There is a rollback path for when a deploy goes wrong.
Performance holds up under realistic load, not just for the first five users.

Handover & documentation

A prototype lives in one person's head. A product needs to be understood by the next person who touches it, including you in six months.

A README explains how to run, test, and deploy the app locally.
The main parts of the system and the data model are documented.
You own all the source code, accounts, and infrastructure — nothing is locked to a vendor or a single contractor.
Environment variables and third-party accounts are inventoried, and access is transferable.
Another developer could pick this up in days, not weeks.

Failing more of these than you would like?

A KloudGentic audit fixes them, in a fixed scope, with a Blueprint from €219 delivered in 3 business days.

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