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.
It covers secrets, authentication, access control, data protection, input validation, dependencies, and monitoring.
Any “no” is a gap to close before production.
AI build tools optimize for speed and frequently leave gaps like exposed secrets, missing access controls, and unsafe data handling.
If the app will handle real money, personal data, or regulated information, this checklist is the floor, not the ceiling.
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.
- Go through each itemAnswer every item honestly.
- Mark every “no”Any “no” is a gap to close before production.
- Close the gapsThe production work: hardening, tests, infrastructure, and monitoring.
- Know when to go furtherReal money, personal data, or regulated information calls for a professional review before launch.
Figure 1. The 25 points by area
Counted from the six sections of this checklist: 3, 5, 4, 4, 4 and 5 items, 25 in all. Bar widths are proportional to the counts.
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.)
Figure 2. From checklist to launch
Built from this page: close every “no” with the production work it names (hardening, tests, infrastructure, monitoring), and when the app handles real money, personal data, or regulated information, a professional production-readiness review goes deeper before launch.
Guides on this topic
Related in this topic: secure AI for government agencies.
Frequently asked questions
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.
Is this checklist an application security assessment?
It 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. A professional production-readiness review goes deeper and is worth it before you launch.
Want a professional pass over your AI-built app? Request a production-readiness audit and we will tell you exactly what to fix before launch.
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- Best AI Tools to Build an MVP (and Their Limits)
- What Is Custom Software Development?
- How Much Does Custom Software Development Cost in 2026?
