AI coding tools write working code quickly. Working and safe are different properties.
What goes wrong
AI-generated code tends to fail in the same places: an authorization check missing on one endpoint out of forty, secrets pasted into config files, user input passed straight into queries or prompts, dependencies nobody chose on purpose. Each is easy to fix once found. The problem is that nobody is looking, because the code works.
Why scanners are not enough
Scanners find patterns. They do not know that this endpoint returns another customer’s invoice, or that this export was meant to be internal. We review by hand where it matters: authentication, authorization, data flows, payments, anything that touches personal data. Findings are ranked by how exploitable they are, not by how many a tool produced.
Working and safe are different properties.
What you get, and how fast
A scope and threat model in the first call, a deep audit with questions asked asynchronously, a ranked report with reproduction steps and a fix backlog your team can work through, and a re-check of the critical fixes. Days, not months. Plus a review workflow and guardrails so the same issues don’t return with the next sprint.