The productivity case for AI-assisted development is settled. Engineers using AI coding tools are moving faster, and that advantage is compounding. But there's a question becoming increasingly urgent for engineering leaders: when your team is generating code at two or three times the rate they were six months ago, how confident are you in what's shipping?
Not the surface-level confidence that comes from "it passed the tests and looked fine in review." The deeper confidence that comes from knowing what went into production is secure, handles edge cases properly and won't surface vulnerabilities until someone finds them the hard way.
Why AI-Generated Code Is a Different Problem
Reviewing code has always been difficult. AI-generated code makes it harder in specific ways:
- Volume. There's more of it. If your team generates twice as much code, your review process needs to handle twice the throughput without doubling the time spent reviewing.
- Plausibility. AI-generated code looks right. It follows conventions and uses familiar patterns. This is a feature when the code is good. It's a problem when there's a subtle flaw, because the surface presentation gives reviewers fewer signals to slow down on.
- Security blind spots. Language models generate patterns they've seen in training data, including insecure ones. Without specific attention, AI-generated code can introduce SQL injection vulnerabilities, insecure authentication flows and a range of issues not visible from a functional review.
What a Good Review Process Looks Like Now
The answer isn't to slow everything down. It's to get smarter about where human attention is most valuable and use automation to cover the rest.
- Automated security scanning is non-negotiable. Static analysis, dependency vulnerability scanners and SAST tooling should run on every commit. The argument for implementing them has never been stronger.
- Bring security thinking earlier. Engineers should be including security requirements in prompts and reviewing AI output with a specific security lens before anything moves downstream.
- Establish clear zones of higher scrutiny. Authentication flows, payment handling, data access layers and anything touching PII should have additional review requirements regardless of how the code was written.
- Review for architecture, not just correctness. AI can solve the immediate problem in a way that creates larger structural issues — coupling that makes the system harder to change, patterns that fail at the next order of magnitude. This requires senior engineering judgment.
The Code That's Already in Production
Many organisations have moved fast over the past 12–18 months, building internal tools, prototypes that became production systems and integrations — using AI tooling throughout, under time pressure, without full security review. That code is running. It's touching real data.
A targeted review looks at what was actually generated and deployed, identifies security gaps and data risks, and produces an actionable remediation plan.
The goal isn't to slow down. It's to move at the pace AI enables while maintaining the security posture the business requires. Speed without exposure is what production-grade AI delivery actually looks like.