No longer just an autocomplete tool, AI has been incorporated into agents that can partly or even fully automate the coding process, dramatically increasing development speed. But with great power comes great responsibility.
The shift to AI coding agents also means less human attention goes into quality assurance on each individual line of code. But what if security reviews can also be automated?
That’s exactly the role of SAST (Static Application Security Testing), as it tackles the review of both human and AI-written code, catching flaws before they ever reach production.
More generated code can mean more security flaws
AI doesn’t invent new categories of vulnerabilities. It makes the same mistakes experienced coders do, introducing anything from injection flaws to exposed secrets. It’s just faster and can handle more work at once.
A recent study found that AI-generated code reported an average pass rate of 56%, meaning that across tests, 44% of scanned code bases had at least one security flaw. The danger grows when an agent repeats the same insecure pattern across multiple files or components. A single bad habit can turn into dozens of vulnerabilities.
And unlike a bug that a user might complain about, a security flaw like this doesn’t announce itself. It can sit in production for months before anyone notices.
SAST provides an automated security checkpoint for agent-generated code
SAST analyzes source code directly. As soon as new code is written (or generated), SAST can scan it before it ever runs. While some vulnerabilities appear only when the application is running, most exist in the code itself.
As AI agents generate code, you want a checkpoint that runs just as quickly. Some practitioners now report producing 12,000 lines of code per day with AI agents, a pace no human reviewer can keep up with line by line.
And the manual review that does happen isn’t particularly effective, either. One report found that a 90% increase in AI adoption correlates with a 9% climb in bug rates and a 91% increase in code review time. Human-driven review simply cannot scale at the speed AI agents write code, and that’s the gap SAST is built to close.
Bring SAST into the coding agent’s development loop
For SAST to keep up with AI-generated code, it needs to run everywhere that code gets written and shipped. The alternative is to check code only at the end. That’s ineffective for two reasons.
First, it takes longer. A SAST scan analyzes however much code you give it, so scanning a small change as it’s written takes seconds, while scanning a large batch of accumulated changes at the end takes far longer.
Second, by the time a flaw surfaces, often enough, additional code has already been built on top of it. Fixing it would then likely entail reworking everything that depends on it.
Ideally, SAST should live inside the IED, scanning code as it’s written, and way before it reaches a repository, pull request, or a CI/CD pipeline. By the time code reaches a pull request, it’s already had multiple chances to slip past a check that should have caught it in the IDE.
SAST can focus human review on security-sensitive code paths
It’s important to note that while SAST finds issues automatically, a person still needs to interpret each finding, rule out false positives, and decide how to fix it. With way less human attention going to every line of AI-generated code, that remaining review needs to count for more.
SAST leads developers straight to the security-sensitive areas. They don’t just point at a line number, either. Many provide contextual explanations of why a flagged issue is a risk, along with remediation guidance on how to fix it.
That saves developers from having to research every flag from scratch. They can act on it directly, without extensive security expertise or hours spent figuring out what the issue is.
Security rules should apply equally to human and AI code
As AI agents take over much of the most repetitive coding work, it’s important for organizations to establish consistent security policies that apply the same scanning requirements to every change, whether a human or an agent wrote it.
This kind of policy should live in the pipeline, not in someone’s judgement. If scanning depends on a person remembering to run it, it will eventually get skipped. It’s best to build the requirement into CI/CD workflows, to ensure that it happens automatically.
The stakes only go up as agents gain more autonomy. Once an agent can open a pull request or merge a branch on its own, it needs to clear the same bar a human developer would. There’s no reason to lower cybersecurity vigilance standards just because a person isn’t the one overseeing the merge.
Conclusion
AI agents are slowly taking over the coding space, with less and less human oversight. SAST is what keeps that trend from turning into a security problem. If teams can automate code generation, they should also automate how the code gets checked for vulnerabilities.
SAST does the work that human reviewers can no longer keep up with. It scans every line, flags what’s risky, and leaves developers to do what only they can do, which is decide how to fix it.
