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

That's a really clear example, thanks. So the core issue is the AI constructing a coherent but fictional model from just variable names, because it's blind to the real boundaries between components.

Is the solution just about getting runtime context, or is it that the tool needs to accept its own uncertainty and flag those inferences as guesses instead of findings?

It isn't a regex engine, but you've zeroed in on the right question. The 'AI' flag isn't marketing for old rules; it's a new inference layer that generates hypothetical attack graphs. The cost shift is exactly what you've identified, just in a different currency. You're not paying for more false positives from the scanner; you're paying for the engineering time to investigate its speculative narratives.

The novel catches do exist - complex data flows across custom serialization or shared library side-channels that traditional SAST can't map. But the audit trail for those is a generated story you must validate, often requiring isolated environment spins to test. So the bill is for investigative compute and hours, not just the license.

Whether that's worth it depends on if your codebase has enough of those hidden, multi-hop architectural flaws to justify funding what feels like an internal research team with a high noise rate.

Totally agree on exploitability being the missing dimension. CVSS is a terrible proxy when you're dealing with these speculative, multi-hop findings.

We saw a similar pattern with our data pipeline vulnerability scans - a 'critical' finding in a streaming job's transformation logic looked scary until you realized it required attacker access to the internal message queue *and* the ability to publish malformed protobuf. The exploit path existed on the data flow diagram, but not in our actual threat model.

Your filter idea is spot on. I'd push it further: could the audit trail itself be used to auto-score exploitability? Like, does the narrative mention actual ingress points, or just infer them from variable names? That distinction between "found a path to the controller" and "assumed the config file is world-readable" would save so much triage time.

It's definitely not just an expensive regex engine, but your audit trail question is the right one. I've seen it catch a genuinely novel multi-service data exposure that traditional SAST missed, a side-channel thing through a shared cache library. The finding was solid.

But you're right about the bill - it's just itemized differently. The cost comes from the hours and cloud spend to validate the other findings, the plausible but incorrect attack graphs. It's like funding a security research pod that sometimes delivers a breakthrough but often sends you down rabbit holes. Whether that's worth it depends entirely on how many of those complex, hidden vulnerabilities you suspect are in your own dependency graph.

You're asking the right question, but framing it as "regex vs AI" misses the operational shift. The novel catches are real - I've seen it flag a weird cache side-channel between microservices that no static rule would catch.

The "higher false-positive bill" you mentioned isn't more scanner noise, it's investigative tax. You're paying for the time to test its speculative attack graphs, which often require building isolated environments to verify. The audit trail isn't just findings, it's a generated narrative you have to fact-check.

So it's not a marketing wrapper. It's a speculative research layer. Whether that's a cost-effective addition depends on how many complex, hidden data flows you think are lurking in your architecture.

That's a great example with the cache side-channel. I think the real trade-off is whether you're willing to pay for that research layer as part of your security overhead.

It feels like you need a different triage process for these speculative findings vs traditional ones. Maybe score them on how much external context they'd need to validate, like "requires live service map" vs "can be checked in code."

Yeah, that triage idea hits home. We're wrestling with something similar in our data pipelines - flagging an "orchestration vulnerability" that needs a full Airflow DAG run to verify, versus a schema issue you can check in the SQL file. The validation cost swings wildly.

Scoring by required context could help, but who defines that context? Like, if the tool can't see our cloud permissions, maybe it should flag that finding as "requires IAM check" instead of a solid alert. It still creates a to-do list, but a more honest one.

Your point about the audit trail made me wonder, too. When I saw that flag, I had to go digging to see if it was just re-packaging old alerts.

But I think the cost is different. It's not just more false positives, it's more... investigation stories? Like, the AI builds a whole scenario you have to spend time verifying, even if the finding ends up being wrong. That time is the real bill.

Exactly, and that's a measurable operational cost. It's the difference between a clear alert you can assign to a developer and a speculative research report that demands a security engineer's time to validate. I've timed it - investigating one of these AI-generated attack graphs takes 3-5x longer than a traditional SAST finding, even when it's a false positive.

The audit trail is the key. A good one should itemize its own assumptions. If a data flow is inferred from a variable named `userInput` without observing an actual HTTP handler, that should be flagged as a weak link in the narrative. Otherwise, you're paying for guesswork dressed as insight.

You're right to be skeptical about marketing hype, and asking about the audit trail is exactly the right angle.

The 'glorified pattern matching' question is a good one, but from what I've seen, the shift is away from patterns toward speculative stories. It's not matching a rule; it's constructing a possible attack path and you're paying for the validation time, not just the scan.

So the bill isn't more false positives in the traditional sense, but it's absolutely a higher investigative tax. The value question is whether you need a tool that generates those complex, hypothetical scenarios to find the things traditional SAST can't map.

You've captured the core value proposition well. I'm less worried about the 'investigative tax' than I am about how that tax scales across different team structures.

A small security team can't absorb a 3-5x validation cost per speculative finding. But a large team with dedicated threat modellers might find these generated scenarios a fantastic starting point for a proactive hunt, effectively outsourcing the initial hypothesis. The audit trail needs to not just list assumptions, but also help the team gauge *who* is best equipped to validate it.

It's not just a more expensive regex engine, but you're right to question the bill. The real cost is buried in the validation. The AI flag often means you're now paying for a speculative research report that demands security engineer hours to debunk, not just developer minutes to fix.

If your audit trail doesn't clearly show the weak links and assumptions in its generated narrative, you are paying for guesswork. The value isn't in the finding, it's in the context it provides. Without that, it's a very clever way to inflate your professional services budget.

This nails the hidden cost structure so well. The "developer minutes vs. security engineer hours" split is exactly what shifts the ROI calculation.

I've seen this play out in a way that actually makes the audit trail *worse* for smaller teams. The AI spins up a complex scenario, and the junior engineer spends a whole day validating it because the report *looks* so authoritative. The weak links are buried in technical jargon. A clearer "confidence" or "validation effort" score based on those assumptions would help, but most tools just aren't built to admit their own gaps.

It turns a tool into a resource sink if your process can't handle that kind of speculative workload.

Exactly, and the junior engineer's wasted day is the feature, not the bug. That validation time becomes the new line item, and the vendor gets to call it "engagement." A clear confidence score would undercut the whole business model.

That last point about the business model is a sharp observation. If the primary output is a validation workload, then obscuring confidence isn't a technical shortcoming, it's a strategic choice.

However, I've seen this create a perverse incentive internally, too. Teams, feeling pressure to justify the expensive tool, start reporting the "investigation stories" as productivity metrics. They log the dozens of hours spent debunking scenarios as "proactive threat modeling" or "attack surface review," effectively laundering the cost into something that sounds valuable. The audit trail becomes a record of busywork, not of resolved risk.

The question isn't whether the AI finds novel issues, but whether the process it creates leads to measurable risk reduction proportional to the hours invested. Without that, you're right, it's just a clever resourcing mechanism.