Why AI-written code needs a security review - every time

The speed of AI-assisted development is real. So is the security debt it quietly accumulates. When a model writes most of your code and no human reads it line-by-line, nobody is asking the questions a security reviewer would.

Models optimise for 'works', not 'safe'

An LLM's objective is to satisfy your prompt. 'Build a user search endpoint' produces an endpoint that searches users - it does not produce one that resists injection, unless you explicitly asked. Security is an absence you have to notice, and absences are exactly what a fast-moving team stops noticing.

The bugs are systemic, not random

Because models share training data and patterns, they make the same mistakes across projects: missing authorization, string-built queries, hardcoded secrets, outdated dependencies. If you've shipped AI code, you almost certainly have some of these - the only question is how many.

Traditional tooling isn't enough

Linters and simple SAST match patterns; they can't reason about whether a specific route checks ownership, or whether tainted input reaches a dangerous sink three files away. The subtle, high-impact logic bugs - the ones that actually get you breached - need something that reads the code in context.

Make the review a habit

The fix isn't to stop using AI - it's to add a security pass to the loop, the same way you'd add tests. Scan after meaningful changes, fix what's critical first, and re-scan to confirm. RedFlag makes that a two-minute step: paste the repo, get a ranked report, copy the fix prompts back into your AI tool. Your first scan is free.