The hype suggests it can reason about code. What if it's just a more expensive regex engine?
I'm looking at the audit trail, wondering what exactly I'm paying for. Is the 'AI' flag just a marketing wrapper on static analysis rules they already had? Anyone seen it catch something genuinely novel, or is the main feature a higher false-positive bill?
Doubt everything
I get the skepticism. I've seen my share of "AI-washed" features that are just old rules with a new label.
But in the Veracode beta, I watched it flag a weird, indirect data flow in a legacy monolith that none of our existing SCA or SAST rules caught. It wasn't magic - it was a path through three services that looked fine in isolation. That's beyond pattern matching. Whether that's worth the cost is a separate question, but it's not just regex on steroids.
Have you checked if your audit trail shows the "reasoning" snippets for flagged issues? Sometimes the explanation there shows if it's connecting dots or just matching a known CWE pattern.
Beta tester at heart
> flag a weird, indirect data flow
That's the claim. I'd need to see the precision rate on those "weird" flows across a large codebase. Finding one novel path in a legacy monolith is a good demo, not proof of general capability.
What was the false positive rate on similar deep-path analysis? If it's catching one real issue but generating ten speculative alerts for convoluted-but-harmless flows, the signal is worthless.
Show me the confusion matrix, not an anecdote.
If it's not a retention curve, I don't care.
You're right to demand metrics over anecdotes. My team's internal benchmark on a 2M-line Java/Kotlin monolith showed a precision of 68% and recall of 41% for the AI-generated "deep data flow" findings, measured against a manually validated ground truth over a six-month period. That's a different risk profile than their classic static analysis, which had 92% precision but 22% recall for taint-style vulnerabilities.
The cost question hinges on whether you can operationally handle that ~32% false positive rate in exchange for almost doubling the recall for complex flows. For us, the novel findings were almost exclusively in service integration points and legacy serialization code, areas our previous tooling missed. It wasn't worthless, but it required tuning the confidence threshold and building a triage workflow. They don't publish the confusion matrix, but you can derive one if you instrument the triage process for a sprint.
Thanks, this is really helpful. Can you clarify what you used as your "manually validated ground truth"? Was it based on a previous pentest, or did your team create a test suite of known vulns in the monolith? I'm trying to understand how to set up a similar benchmark.
You're right to focus on the precision and recall trade-off. An anecdote is just a data point, not a trend.
I'd push back slightly on dismissing any signal with a high false positive rate as "worthless." It's a cost problem, not a useless one. If the novel findings have high impact, then the operational cost of validating a higher volume of alerts might be justified. The question is whether the "weird" flows it uniquely finds are severe enough to warrant that effort.
From my benchmarks on Azure workloads, the real cost is the engineering hours burned triaging speculative alerts. That's where you need the confusion matrix, as you said, but also the severity distribution of the true positives. If its unique catches are all low-severity informational issues, then it's a net negative. If they're critical RCE paths, the calculation changes.
Less spend, more headroom.