AI code review: what it catches and what it misses
AI code review is the use of AI tools that read a pull request or a whole repository and flag bugs, known vulnerability patterns, leaked secrets and style problems before the code merges. In 2026 it carries a twist the name hides: much of the code being reviewed by AI was also written by AI — one model checking another's homework.
The people closest to that loop are the most skeptical of it. Stack Overflow's 2025 developer survey shows trust in unreviewed AI output falling precisely among the developers who use the tools daily. Doomity, a software development firm serving clients in the US, UK, Spain and Portugal, audits AI-generated codebases for production and for investor due diligence — this guide covers what AI review genuinely does well, and where it stops.
Updated: August 2026
What does an AI code review actually check?
Pattern-shaped problems, fast and cheaply. Current tools are genuinely good at spotting known vulnerability classes — injection, path traversal, unsafe deserialization — plus hardcoded secrets, missing input validation, obvious null-handling bugs and style drift. Run on every pull request, that is real value: a tireless first reader that never skips a diff because it is Friday.
That strength has a boundary worth naming: the tool reviews the code it can see, against patterns it has seen before. For a codebase that was itself AI-generated — the scenario covered in fixing AI-generated code vs rebuilding — the review inherits the blind spots of the generation.
What does AI review miss that a senior engineer catches?
Everything that is not in the diff. An AI reviewer sees the change; it does not sit in the meeting where the change was the wrong idea. The recurring misses Doomity finds when auditing behind AI-reviewed codebases: architecture that cannot scale past the demo, permission models that are coherent but wrong for the business, logic that is plausible and incorrect — code that reads well and does the wrong thing — and the decisions that are absent entirely: no rollback story, no data retention policy, no threat model.
The evidence that generators cannot audit themselves is quantitative: roughly 45% of AI-generated code introduces known vulnerabilities (Veracode 2025), produced by models that would happily review that same code and pass it. A different model reviewing is better than none. It is still pattern-matching, not judgment.
AI review or human audit — which one do you need when?
Both have a place, and the mistake is using the cheap one where the expensive one is due. Doomity's position, stated plainly: AI review as a gate on every pull request, yes — as the only audit before production or before a due diligence, no:
| What is being judged | AI code review | Senior human audit |
|---|---|---|
| Known vulnerability patterns | Strong — its home ground, on every PR | Covered, but slower and costlier per finding |
| Secrets and configuration | Good at spotting them in code | Also checks where secrets live outside the repo |
| Architecture and permission model | Sees files, not the system or the business | The core of the job — including what should exist and does not |
| Business logic correctness | Plausible-but-wrong code passes | Caught by reading the code against the business, not against patterns |
| Verdict you can act on | A list of findings, unranked by consequence | A prioritized plan — what blocks launch, what waits |
What should a code review of an AI-built app cover?
If the codebase was generated by AI, the review checklist is not generic — it targets the places generation reliably fakes. Six items, in the order Doomity audits them:
- Authentication that enforces. Not the login screen — the checks behind every route and API call.
- Per-user data rules. Can one authenticated user read another's records? The classic AI-generated leak.
- Secrets off the client. API keys and tokens out of the frontend bundle and out of the repository history.
- Deploys with a way back. A rollback path that has been exercised, not assumed.
- Tests that protect the critical paths. Payment, signup, data export — the flows where a regression costs real money.
- Dependency and license traceability. What the AI pulled in, under which licenses — the item investors' advisors check first.
When does code review become a due diligence problem?
The moment a funding round or an acquisition enters the roadmap. The investor's technical advisor will read the repository, and an AI-generated codebase with only AI review behind it is exactly the profile that stalls negotiations — the checklist of what they look for is in 5 red flags that kill funding rounds. Arriving with your own senior audit beats explaining someone else's findings.
Doomity works both sides of that moment: production hardening for teams about to launch, and its technical due diligence service for teams preparing to be examined. Its engineers come from teams that built for Holcim, Canon España and Indra, and its delivery process is AI-integrated — up to 40% faster, with every finding verified by a senior engineer. That last clause is the whole point of this page.
FAQ
As a per-PR gate, it can carry most of the routine load: patterns, secrets, style, obvious bugs. As the final authority before production or an investment decision, no — it cannot judge architecture, business logic or what is missing entirely. The working setup is AI review on every pull request, senior human audit at the moments with consequences.
It helps, with a caveat: review inherits the blind spots of generation. Veracode's 2025 report found roughly 45% of AI-generated code introduces known vulnerabilities — written by models that would also review that code without complaint. A second model catches some of it. A senior engineer reading the auth, data rules and permissions catches the part that ends up in incident reports.
Pick by workflow, not by ranking: a tool that comments on pull requests if your team lives in GitHub, IDE-integrated review if changes should be caught before the PR, or both. The differentiator is adoption — the tool your team actually leaves enabled beats the theoretically better one. No tool choice changes what AI review structurally misses.
Run the two free scorecards Doomity publishes before paying anyone: the production scorecard at /tools/production-scorecard for launch readiness, and the investor scorecard at /tools/investor-scorecard for due diligence readiness. Ten questions each, three minutes, and a scored verdict on whether the honest next step is fixing or examining.
If your codebase was written — or reviewed — mostly by AI and real users or real investors are next, the useful step is an audit where a senior engineer reads what the patterns cannot judge.