We Now Build Code Faster Than We Can Understand It: The New Risk of AI Speed

There’s an old story about an apprentice who enchants a broom to fetch water, then can’t stop it. He panics and hacks it in half, only to end up with two brooms working just as fast, then more, then a flood. Software in 2026 has the same problem. It doesn’t need more automation. It needs something that can actually keep up with what’s already been set loose.

Securing enterprise codebases was already challenging because most contain code written in multiple languages. AI assistants are now adding to that challenge by generating code in whichever language they determine is best for the task, whether or not your scanner can read it.

Those AI assistants write faster than people do. Checkmarx’s 2026 Future of Application Security survey names what that speed has done to the job: developers have moved from authoring code to editing it, supervising output instead of producing it.

That combination – more code, written faster, across more languages – creates a security challenge most teams don’t understand and were not built to handle.

The more production code AI generates, the more risk can slip through at scale. According to the same report, organizations where 81-100% of production code is AI-generated ship known vulnerabilities at 3.4x the rate of organizations where AI writes just 1-20%.

And, in addition, attackers are not waiting for security to catch up. Anthropic’s research on N-day exploits found that WannaCry took 59 days to weaponize after its 2017 patch. In the same research, Anthropic’s own model, Claude Mythos Preview, had a working Firefox exploit ready within an hour of Mozilla shipping the patch, 18 days before the patched browser itself went out.

The imbalance is clear: attackers can move in hours, while defenders are still trying to understand code generated at AI speed, across more languages than ever. Closing that gap needs to start before the code ships.

One Scan, Two Lenses

Scanning code has always meant choosing a lens. A deterministic scanner looks through the lens of known rules, signatures, and patterns. An AI-based scanner uses a different lens. It reasons through code more like a human reviewer, which helps it catch risks that do not match a known rule or signature.

Neither one was ever going to be enough by itself. That is the compromise Checkmarx’s hybrid architecture refuses to make: depending on the language, either a deterministic, rules-based engine or an AI-based reasoning engine runs, and a Findings Analysis Engine (FAE) reconciles the output into one validated list. That pairing is Next-Gen SAST. On its own, it reaches an F1 score of 0.64.

One Step Further

The harder challenge is finding risks with no CVE, signature, or established pattern to match. That is where Checkmarx Fusion adds another layer. Checkmarx Fusion runs several frontier AI models, hunting for zero-day and unknown-pattern findings.

Run together, Checkmarx SAST with Checkmarx Fusion achieves an F1 score of 0.74. The point is to give developers scan results that they can trust: precise enough to avoid false-positive noise, and broad enough to catch critical vulnerabilities that simpler scanners might miss.

The apprentice’s mistake wasn’t casting the spell. It was assuming he could still control however many brooms showed up. Checkmarx Fusion is built for the version of that story where the brooms keep coming.