How I Use AI for Code Reviews and Security Audits
When you're a small team shipping real software, you don't have a dedicated security engineer. What you have is a release deadline and a surface area that grows with every feature you add. At TRON Technologies LLC, that surface area includes cloud authentication, a strict Content Security Policy, third-party API integrations, and an architecture where every line of JavaScript that touches user interaction has to survive a hostile environment. I use AI to review that code before anything ships, and it's changed what I can catch on my own.
The workflow is direct. Before any commit that touches authentication, external API calls, data handling, or user-facing input, I run a structured review session with Claude. I paste the diff, or the full file if the change is significant, and I ask it to treat the review as an adversarial analysis, not a style check. What's the attack surface here? What does an attacker who knows this codebase do with this function? Which OWASP Top 10 categories does this touch? That framing matters. A generic "review my code" prompt gets you a style guide. An adversarial framing gets you something closer to a real security review.
The vulnerability classes where AI earns its keep are the ones where pattern recognition matters most: XSS vectors hiding in DOM manipulation, Content Security Policy misconfigurations that create unsafe-inline exposure, insecure storage of tokens in localStorage versus sessionStorage, missing input sanitization at trust boundaries, and API key exposure in client-side code. These aren't exotic attacks. They're the mistakes that show up in every post-mortem from a real breach, and they're easy to miss when you wrote the code yourself and you're reading it with the mental model of what it's supposed to do rather than what it actually does. AI doesn't have that mental model. It reads the code cold.
There are real limits to what this catches. AI is excellent at recognizing known vulnerability patterns against known code. It is not good at understanding your specific business logic and what an attacker who knows your product would actually try. It doesn't know that your payment flow has a timing window between a state transition and a validation check, or that a particular API endpoint is reachable from a context you didn't intend, unless you describe those constraints explicitly. The review is only as good as the context you give it. I've learned to include a threat model in the prompt (who's likely to attack this, what do they gain, what's the trust boundary between the client and the server) because without it the review stays generic.
The security review skill I use is specifically designed for this. Before merging anything on the main branch that touches sensitive functionality, I run a full security review pass and look at the findings before committing. What I'm watching for isn't just "did it find something." I'm watching for what it flagged and why, because that tells me whether the reviewer understood the context of what I built. A finding that says "this could expose user data" without being able to explain the actual data flow is a false positive. A finding that traces a specific XSS vector through a specific DOM method to a specific user input field is real signal.