Security Checklist Before Launching An AI Generated App

Most founders don’t skip security on purpose. They just never get to it, because the app looks like it’s working, and “working” gets mistaken for “safe.” An AI generated app can pass every manual click-through test you throw at it and still have a wide open door somewhere in the code that nobody, including the AI that wrote it, ever checked.

This is the checklist we go through with founders before a launch or an App Store submission, and it’s the same lens we use when we audit and stabilize vibe coded apps before they go live. Think of it as a first pass, not a replacement for a proper AI app security audit done by someone who can actually read the code. What it will do is tell you fast whether you’re close to ready or whether you need to stop and get one.

Credentials And Secrets

This is where almost every audit starts, because it’s where almost every vibe coded app fails first.

• Open your source files and search for anything that looks like an API key, a database password, or an access token. If you find one sitting in plain text, that’s your first fix.
• The app should refuse to start if a required environment variable is missing, rather than quietly falling back to a default value nobody remembers setting.
• Check your .gitignore. Backup folders, log folders, and local data exports need to be covered, not just the obvious file types.
• Git history holds onto things longer than people expect. If something sensitive was ever committed, even briefly, it’s worth confirming it was actually scrubbed, not just deleted from the current version.
• A full database dump or a “temporary” export file has no business sitting inside your repository, ever.

If you’re unsure about the last two, that’s normal. Most people building fast with AI tools never look at what’s actually tracked in their repository. It’s worth a second look before launch, because rotating a password after the fact doesn’t undo an exposure that already happened.

Authentication And Authorization

Being logged in and being allowed to do something are two different checks. AI-generated backends are usually good at the first one and inconsistent at the second.

• Pick a random user record and ask: does the route that reads or edits this confirm the caller actually owns it, or does it just confirm they’re logged in as someone?
• Admin actions and bulk operations need a real permission check behind them. Hiding a button from the interface isn’t the same thing.
• Test endpoints, internal tools, and “temporary” AI routes have a habit of staying reachable long after they were supposed to disappear. Worth confirming none of them are still live.
• Look at what an API response actually returns. If it hands back an entire database record when the client only needed a name and an email, that’s more exposure than necessary.

Database And Data Protection

If your backend platform supports database-level security and you’re not using it, every check above becomes the only thing standing between a user and someone else’s data.

• Turn on row-level security, or your platform’s equivalent, for any table holding user-specific or child-specific data.
• Names, dates of birth, and conversation history sitting in plain, unprotected columns deserve a second look, especially for anything collected from minors.
• Account deletion should remove a user’s data across every related table in a single transaction. A process that walks tables one by one, with no rollback if it fails partway, tends to leave orphaned data behind.

AI Feature Safety

If your app has any kind of AI-powered feature, chat, generation, recommendations, this section applies to you specifically.

• User input headed into a prompt should be treated as untrusted data. Pasting it directly into a string the model reads as instructions is how prompt injection happens.
• Output deserves the same scrutiny as input. A safety check that only looks at what goes in and never at what comes out is only doing half the job.
• Every path to generation should run through the same safety layer. A shortcut that calls a lower-level function directly defeats the point of having guardrails at all.
• Spending caps per user matter more than most founders realize until the bill arrives. Set one, and set an alert for anything that spikes above normal.

An app collecting data from families or minors gets reviewed against a higher bar here, by both Apple and Google. An AI feature with no guardrails and no spending limit is the kind of thing that gets flagged, not overlooked.

Payments And Account Security

This is the section that decides whether you pass App Store and Google Play review or get sent back to fix it.

• Subscription access needs to check the actual expiry date. A status flag that only updates later, once a webhook catches up, leaves a gap where lapsed users keep access.
• iOS subscriptions belong in StoreKit. Android subscriptions belong in Google Play Billing. A generic web payment processor handling either one inside the app is a policy violation, not a technical detail.
• Account deletion has to be available from inside the app itself. Both platforms require this, and it’s one of the most common reasons apps get rejected.

We go deeper into everything Apple and Google actually check at submission time in our App Store Approval For AI Generated Apps breakdown.

Running Your Own AI App Security Audit Before You Hit Submit

• Run your test suite and check what it actually covers, not just whether it passes. Tests on login, ownership, deletion, and billing matter more than a high count of tests on low-risk surface area.
• Have someone other than the person who built a feature try to access another user’s data on purpose. If they succeed, that’s a finding, not a fluke.
• Line up your privacy policy against what the app actually collects and sends, including anything shared with a third-party AI provider. App store privacy declarations need to match too.

What To Do If You Checked “No” On Anything

If you went through this list and found more than one or two gaps, you’re not behind, you’re normal. Most vibe coded apps we look at have some version of this list unchecked, because building fast and building securely aren’t the same skill, and AI coding tools are optimized for the first one.

Knowing which gaps are a quick fix and which need real rework is hard to tell from the outside. That’s what a technical audit is for. We walk through what that process actually looks like in Technical Audit For AI Generated Apps. If you want to understand the specific vulnerabilities we find most often and why they happen, that’s covered in Common Security Issues In AI Generated Apps.

Or skip the self-diagnosis. We can look at the code directly and tell you exactly where you stand, which is usually faster.

Getting An AI App Security Audit Done Before You Launch

A checklist gets you most of the way there. It won’t catch everything, because some of these issues only surface once someone reads the code line by line and tests how it behaves under pressure. But if you’ve gone through this list honestly and you’re still not sure, that uncertainty is the signal itself. It means it’s time for a proper AI app security audit before you submit, not after a rejection, and not after a user is affected.

Our team specializes in exactly this kind of pre-launch review. We can go through your app, tell you honestly where the real risks are, and help you get it production ready before you submit