The Gap Nobody Warns You About

You built an app with an AI builder in a weekend. It works. Your beta users love it. You're ready to charge money. Then you realize nobody told you about the gap between "it works in preview" and "it works safely for strangers on the internet."

This gap is not small. AI-generated code is optimized for speed and demo-ability. It is not optimized for security, resilience, or scale. That responsibility falls entirely on you.

The good news? Most of the fixes are mechanical. They don't require rewriting your app. They require layering in the things a production environment actually demands.

Why AI Builders Create This Problem

Tools like Lovable, Bolt, and v0 are genuinely useful. They compress weeks of scaffolding into hours. However, they make a quiet assumption: you know what they left out.

Most indie developers don't know, because the preview environment hides the risk. There's no attacker in preview. There's no traffic spike. There's no second user account trying to read someone else's data. The problems only appear when real users show up.

Additionally, the defaults these tools ship with are convenience defaults. Convenience for building fast. Not convenience for sleeping soundly after launch. Understanding that distinction is the first step toward fixing it.

What Gets Left Out by Default

Secret key exposure in client-side JavaScript bundles
Database tables with no row-level access rules
Authentication that doesn't verify email ownership
No rate limiting on expensive or sensitive endpoints
Zero error logging, so failures are invisible to you
No automated deployment pipeline, meaning every deploy is manual and risky

This list is not exhaustive. But fixing these six categories covers the majority of production incidents that hit early-stage apps.

Start With What Can Drain Your Bank Account

Open your browser dev tools right now. Go to the Sources tab and search for words like "secret", "key", or "token". If you find your OpenAI API key, your Stripe secret, or your database service key sitting in the JavaScript bundle, you have an urgent problem.

Anyone who visits your site can extract those keys. A malicious script can use your OpenAI key to rack up thousands of dollars in API calls overnight. A leaked database key gives a stranger full read and write access to every row you've ever stored. This is the highest-priority fix on the list.

Essential Production Checklist

✓

Secret Key Exposure
Avoid keys in client-side JavaScript.

✓

Row-Level Access Rules
Implement access rules for database tables.

✓

Email Verification
Ensure authentication verifies email ownership.

✓

Rate Limiting
Limit requests on sensitive endpoints.

✓

Error Logging
Enable logging to track failures.

✓

Automated Deployment
Set up CI/CD for safer deployments.

The solution is straightforward. Move every secret to a server-side environment variable. Route any request that needs that secret through a backend function. Public-facing keys, like an anonymous read key, are fine in the client. Anything labeled "secret" or "service" is not. Here's a basic pattern for handling this in a Node.js serverless function:

// BAD: secret key in client code
const response = await fetch('https://api.openai.com/v1/chat/completions', {
  headers: { 'Authorization': `Bearer ${process.env.OPENAI_SECRET_KEY}` }
});

// GOOD: call your own backend endpoint instead
const response = await fetch('/api/chat', {
  method: 'POST',
  body: JSON.stringify({ message: userInput })
});

// In /api/chat (server-side only):
// const openai = new OpenAI({ apiKey: process.env.OPENAI_SECRET_KEY });

If you're building APIs and want to document them properly, Swagger Docs is a solid reference for structuring your backend endpoints cleanly.

Database Security Is Not Optional

Most AI-built apps that use Supabase ship with Row Level Security (RLS) turned off. RLS is the database feature that controls which rows a user can read or write. Without it, any logged-in user can technically query any row in any table.

Picture a multi-user invoicing app. Without RLS, user A can read user B's invoices by changing a single number in an API request. That's a data breach. It doesn't require hacking skills. It requires curiosity and browser dev tools.

Turning on RLS means writing a policy per table. The policy says, in plain terms: a user can only touch rows where the user ID column matches their own verified ID. Write the policy, then test it. Create two separate test accounts and try from account two to access account one's data. If it works, your policy is wrong. Keep testing until it fails.

Auth That Actually Protects Users

Preview-mode authentication often skips email verification entirely. For production, your auth layer needs three things working correctly. First, verified signups, where users confirm they own their email before getting access. Second, sessions that expire, with short-lived access tokens that stop working after an hour or so, limiting the damage from a stolen token. Third, server-side session validation on every sensitive request.

The critical mistake to avoid is trusting a user ID that comes from the browser. Read the authenticated user from the verified server-side session instead. If you're using Clerk or Supabase Auth, most of this is configuration. Use Postman API Platform to test your auth endpoints and confirm they reject invalid or expired tokens.

For broader API architecture patterns, Microservices.io covers security patterns that apply even to solo-built apps at smaller scale.

Stability and Observability for One-Person Teams

Here's a practical question: if your app goes down at 2am, how long before you know? If the answer is "when a user emails me", you have an observability gap. Observability means you can answer three questions at any moment: is it up, is it slow, and what just broke?

You don't need a complex platform to start. A basic uptime monitor like Better Uptime or UptimeRobot sends you a text when your site goes down. A free tier of Sentry catches and logs unhandled errors with stack traces. These two tools together cover the first 90 percent of what you need to debug production issues without guessing.

Rate limiting is the other stability fix most AI apps skip. Without it, one automated script can call your signup endpoint thousands of times per minute. This runs up your email provider bill, triggers OpenAI rate limits on your account, and potentially locks out real users. Add rate limiting to any endpoint that sends email or calls a paid API. A simple in-memory counter works fine to start.

Deployment Should Be Boring

Manual deploys are risky. You make a change at midnight, upload files, and something breaks in a way that didn't break locally. CI/CD (continuous integration and continuous deployment) makes deployments repeatable and testable. GitHub Actions with a simple workflow file is enough for most indie apps. Every push to main runs your tests and deploys if they pass. No more finger-crossing.

Database Performance Before You Need It

Scaling problems are almost always database problems. They're invisible when you have 100 rows. They're catastrophic when you have 100,000. Two issues show up first.

Missing indexes mean queries that filter or sort by a column scan every single row as your table grows. Add indexes on the columns you use in WHERE clauses and ORDER BY statements. The second issue is unbounded queries, which load every row at once. Paginate any query that returns a list. Test with roughly ten times your current data volume before you hit that volume in production.

The Security Gap in AI Apps

Identify Risks

Recognize the gaps left by AI builders.

Implement Security Measures

Layer in necessary security features.

Test Environment

Simulate real user interactions.

Monitor Performance

Keep an eye on database performance.

Prepare for Launch

Ensure all security checks are complete.

Want to analyze your query performance data visually? A free online chart maker can help you plot query times against data volume to spot degradation early.

When assessing how your app performs relative to similar tools or benchmarks, a percentile calculator helps you understand where your response times actually land in a distribution.

Your Security Posture Goes Beyond Code

Code security is one layer. Your personal digital exposure is another. If you're the sole operator of a product, attackers sometimes target the founder directly to get access to the app. Running a digital footprint audit tool on your own email and username shows you what's publicly accessible about you across platforms.

Furthermore, if you work from coffee shops or shared networks while building and deploying, read this comprehensive VPN guide before your next deploy from a public network.

And if you're building video or audio features into your app and need to test media processing workflows, a free video to MP3 converter can help you quickly extract audio samples for testing without additional software.

What to Prioritize This Week

You don't need to fix everything today. In contrast, trying to fix everything at once usually means fixing nothing properly. Here's a prioritized order:

Audit your live bundle for exposed secret keys. Fix any you find immediately.
Enable Row Level Security on every table that holds user data. Test it with two accounts.
Add uptime monitoring and error tracking before your next marketing push.
Set up a basic CI/CD pipeline so your next deploy isn't manual.
Add indexes on your most-used query columns and paginate list endpoints.

AI builders are tools, not engineers. They generate a starting point. The production hardening is your job. The five steps above are where to start. None of them require a complete rewrite. All of them meaningfully reduce the risk of something going badly wrong after launch.

Check back on Tools On Fire Insider for more practical guides on shipping solo products safely. And if you're evaluating AI tooling for your stack, browse our full tool directory for vetted options that fit indie workflows.