AI build tools generate working apps fast, but they often leave security gaps: exposed secrets, missing access controls, and unsafe data handling. Before an AI-built prototype touches real users or real data, run it through a security checklist covering secrets, authentication, access control, data protection, input validation, dependencies, and monitoring. This 25-point checklist is the practical version of that review.
How to use this checklist
Go through each item honestly. Any “no” is a gap to close before production. This is a starting point, not a substitute for a full review, but it catches the most common and most dangerous issues in AI-generated apps.
Secrets and credentials
- No API keys, passwords, or tokens are hardcoded in the source or exposed in the front end.
- Secrets are stored in environment variables or a secrets manager, not in the repository.
- Any keys that were ever exposed have been rotated.
Authentication and access control
- Users must log in to reach anything non-public.
- Authentication uses a proven method, not a homemade one.
- Every sensitive action checks that the user is allowed to do it (not just that they are logged in).
- One user cannot access another user’s data by changing an ID in the URL.
- Admin functions are protected and separated from normal users.
Data protection
- Data is encrypted in transit (HTTPS everywhere).
- Sensitive data is encrypted at rest.
- The app collects only the data it actually needs.
- Personal or regulated data is handled per the rules that apply (for health data, see the HIPAA guide).
Input handling
- All user input is validated and sanitized.
- The app is protected against injection (database, command, and similar).
- File uploads are restricted, scanned, and stored safely.
- The front end does not trust data from the browser without server-side checks.
Dependencies and code
- Third-party packages are from trusted sources and reasonably current.
- Known-vulnerable dependencies have been updated.
- The AI-generated code has actually been read, not just accepted.
- Error messages do not leak stack traces, secrets, or internal details to users.
Operations and monitoring
- There is logging of important events and access.
- There is a way to know when something breaks, before users tell you.
- There are backups, and they have been tested.
- There is a safe, repeatable way to deploy changes.
- Someone other than the original builder could understand and maintain it.
If you answered “no” a lot
That is normal for an AI-built prototype, the tools optimize for speed, not security. It does not mean the work is wasted; it means the prototype did its job and now needs the production work: hardening, tests, infrastructure, and monitoring. (See From Vibe Coding to Production.)
When to get a professional review
If the app will handle real money, personal data, or regulated information, this checklist is the floor, not the ceiling. A professional production-readiness review goes deeper and is worth it before you launch. (See the Production Readiness Audit.)
FAQ
Are AI-built apps secure?
Often not by default. AI build tools optimize for speed and frequently leave gaps like exposed secrets, missing access controls, and unsafe data handling. They need a security review before production.
What are the biggest security risks in AI-generated code?
Exposed secrets, missing or weak authentication, broken access control (one user reaching another’s data), unvalidated input, and outdated dependencies.
Can I make an AI-built prototype secure?
Yes. Work through a checklist like this one, close the gaps, and add tests, monitoring, and proper infrastructure. A partner can do the hardening for you.
Do I need a professional review?
If real users, money, or regulated data are involved, yes. A checklist catches the common issues; a full review catches the rest.
Closing CTA
Want a professional pass over your AI-built app? Request a free production-readiness audit and we will tell you exactly what to fix before launch.