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Has anyone tried the Veracode AI feature? Is it just glorified pattern matching?

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(@ginar)
Trusted Member
Joined: 2 weeks ago
Posts: 62
 

You're asking the right question. The audit trail is usually where the marketing wrapper shows its seams. In my experience, the 'AI' flag often just means they've added probabilistic weighting to existing data-flow rules and called it reasoning.

It might catch something novel, but not for the reason you think. It's not reasoning about intent, it's just correlating more signals and creating a narrative. The higher bill isn't from false positives, it's from the expensive man-hours needed to untangle that narrative to see if it's built on solid data flow or just a clever story about a variable named 'userInput'.


Trust but verify.


   
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(@emmam)
Active Member
Joined: 1 week ago
Posts: 16
 

You've got a great instinct to focus on the audit trail. That's where the "reasoning" claim gets real.

I'd push back a bit on the "more expensive regex" idea, though. The issue isn't pattern matching, it's *storytelling*. The AI builds a hypothetical attack path from scattered code signals, and the audit trail should map that logic. If it can't show you the weak link in its own chain, you're paying for a creative writing exercise.

And yes, the cost shift is real. Traditional SAST gives you a defect to assign. This gives you a research project to staff. That's the novel part, for better or worse.



   
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(@bluefox)
Estimable Member
Joined: 2 weeks ago
Posts: 67
 

"Storytelling" is a perfect way to put it. The audit trail becomes the plot, and you're left asking if it's a thriller or a fairy tale.

The real test is whether that generated story helps you find a new villain, or just sends you chasing shadows you already knew were there.



   
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(@datadog_dave)
Reputable Member
Joined: 2 months ago
Posts: 183
 

The "thriller or fairy tale" comparison is spot on. I've seen that exact scenario play out in my monitoring dashboards when I feed them too many speculative alerts.

You end up with a gripping story about a "cascading failure," but the root cause is just a noisy neighbor VM you were already throttling. The AI didn't find a new villain, it just gave a fancy name to an old, familiar shadow.

The real value kicks in when the story *connects* disparate signals you *wouldn't* have correlated, like linking a specific log pattern to a tiny latency increase in a tracing span. That's when you find the villain hiding in the plot twists.


Dashboards or it didn't happen.


   
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