AI-assisted development tools are genuinely reshaping engineering productivity, which we’ve covered elsewhere. This is specifically about a narrower, more consequential question: what does AI-assisted code generation mean for a website’s actual security and performance once it ships to production, since those are outcomes that matter regardless of how the code was written.
Where AI-generated code needs extra security scrutiny, specifically
Generated code tends to reproduce common, well-represented patterns from its training data - which is genuinely useful for standard functionality, but means it can also reproduce common vulnerable patterns just as fluently as secure ones, particularly for security-sensitive operations (input validation, authentication logic, database queries) where the “obviously working” version and the “actually secure” version can look deceptively similar to someone not specifically reviewing for security. A generated SQL query that works correctly for the happy path can still be vulnerable to injection if it wasn’t specifically written with parameterized queries in mind - the code runs, appears correct, and the vulnerability isn’t visible without deliberate security review.
What this actually means for how we review AI-assisted code
- Security-sensitive code gets the same or stricter review regardless of whether it was AI-generated or hand-written - authentication, authorization, data validation, and anything touching sensitive data goes through explicit security review, not just functional testing, because functional correctness and security correctness are genuinely different properties that generated code doesn’t automatically satisfy together.
- Automated security scanning as a standard part of the pipeline, catching common vulnerability patterns regardless of code origin - this is good practice generally, and becomes more important, not less, as more code is generated quickly enough that manual review alone can’t keep pace with volume.
- Dependency and library choices reviewed independently - AI tooling can suggest outdated or less-secure libraries if its training data includes older patterns, so dependency choices get the same currency and security-track-record check they’d get from a human-proposed dependency.
Where performance needs similar deliberate attention
Generated code optimized purely for “does this produce the correct output” doesn’t automatically account for performance considerations - an AI-generated database query might be functionally correct while missing an index that a performance-conscious engineer would have added, or a generated component might re-render inefficiently in ways that only show up under real usage load, not in a quick functional test. We run the same performance review discipline - profiling, bundle analysis, database query review - on AI-assisted code as we do on any code, rather than assuming speed of generation implies quality of output.
What actually works well about combining AI assistance with this discipline
Faster initial implementation combined with unchanged review rigor is a genuine net win - more code reviewed per unit of engineering time, not less scrutiny per line of code. The productivity gain from AI assistance should go toward faster delivery of properly-reviewed code, not toward skipping review because the code was generated quickly and reads fluently.
What we’d actually recommend
Treat AI-generated code as a fast first draft that earns the same security and performance review as any other code - never as a shortcut past that review, regardless of how much faster the generation itself is.
We build with this discipline as standard practice across our development work. Get in touch if you want to know how we specifically review AI-assisted code before it ships.