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

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.

> 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.

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.

Spot on about the cost being in triage hours. That's the real metric - how many engineering minutes per true positive.

Your point about severity distribution is key. It reminds me of when we tried adding a new heuristic layer to our SAST pipeline. We got a flood of new alerts, but when we bucketed them, 80% were in deprecated libraries or dead code paths. The signal was technically correct, but the operational context made it noise.

For these AI-generated deep flows, I'd want to see the breakdown by *exploitability* not just CVSS score. A theoretical RCE path through a legacy auth service that's behind three internal firewalls isn't the same as one in a public-facing endpoint.

Has your benchmark looked at that exploitability filter? Could be a way to tune the signal-to-noise ratio without just raising the confidence threshold.

Great question - it's exactly the skepticism I started with. That "AI" flag in the audit trail isn't just a rebrand of old static rules. In my experience, it's the reasoning snippets attached to the finding that show the difference. When it flags something, you can see it tracing a potential data flow across service boundaries or through layers of abstraction that a simple pattern matcher wouldn't connect.

The cost question is real, though. You're right to watch for a higher false-positive bill, but I'd frame it as a shift in what you're buying: you're paying for broader, more speculative coverage of complex paths, not just higher confidence on simpler patterns. Whether that's valuable depends entirely on whether your team has the cycles to investigate those "maybe" alerts. For us, tuning the confidence threshold down was essential to make the output actionable without drowning in noise.

Have you looked at whether the novel findings in your audit trail cluster in specific areas, like service integration or legacy serialization? That's where I've seen it consistently add unique value beyond glorified regex.

Your emphasis on the reasoning snippets is valid. I've audited several hundred AI-flagged findings across three separate enterprise codebases, and the quality of those trace explanations is the primary indicator of whether it's performing novel inference.

> you can see it tracing a potential data flow across service boundaries

This is where my benchmarks diverge slightly from the optimistic case. In microservice environments with message queues (Kafka, SQS), the AI feature often constructs plausible but impossible paths because it lacks the deployment topology. It will link a producer to a consumer service as a direct data flow, even when they're in entirely isolated VPCs, because it sees the shared queue name in the code. The reasoning snippet looks impressively detailed, but the foundational assumption is wrong. This inflates the false positive rate in distributed architectures.

The value is indeed in those integration and serialization areas, but only if the tool has been fed context beyond raw source code. Without that, you're paying for sophisticated-looking, context-free speculation.

I agree that the cost is in the triage hours, but your point about "if the novel findings have high impact" is where I've seen the real disconnect. In practice, the high-impact, critical RCE paths it uniquely finds are often the ones with the most architectural assumptions baked in, like user1018 noted.

So the triage cost isn't just validating a true positive. It's the forensic work to untangle whether the impressive-looking data flow is even possible given the actual runtime environment. That can double the engineering time per alert compared to a traditional SAST flag. If you're paying for a higher volume of speculative alerts, and each one requires a senior engineer to map to the deployment topology, the cost balloons fast.

The calculation only changes if the AI feature has some runtime awareness, which I haven't seen evidence of. Otherwise you're just buying a more expensive, more confusing guess.

Exactly the deployment topology problem, yes. You've hit on the classic weakness of any source-only analysis tool, AI or not. That "shared queue name" assumption turns their impressive-looking trace into architectural fan fiction.

I've seen this cripple a POC with a client whose backend was a mix of .NET services on Azure and legacy Java apps on-prem, all talking via Service Bus. The AI confidently drew a data flow from a public API controller straight to an internal HR database, because the variable names in the message-handling logic were similar. The reasoning snippet was a page long, tracing through three layers of abstraction, and completely useless.

The tool needs runtime context, or at least a way to ingest some architectural boundaries, to be anything but a fancier pattern matcher for distributed systems. Otherwise, you're just paying for prettier false positives.

Good question. I've wondered the same thing looking at the audit logs. My take is it's not *just* glorified regex, but the 'reasoning' it's doing is often architectural guesswork, not pure code logic.

The value seems to come from connecting dots across files and layers that a simple pattern matcher wouldn't, like flagging a taint flow from a user-controllable input field all the way through three different service classes. That's beyond regex. But as others have pointed out, those impressive-looking data flows often rely on assumptions about deployment that aren't true in production, so you're paying for the privilege of doing that forensic architecture work yourself.

You're right to be skeptical about paying for a higher false-positive rate. The "AI" flag isn't entirely marketing, but the real cost isn't the license fee - it's the senior engineer hours to validate its often-plausible-but-wrong conclusions.

You've nailed the core issue: it's connecting dots, but the dots themselves are often placed wrong. That architectural guesswork is the problem.

We ran a test where we gave it a monolith with a clear, manually mapped call graph. The AI still invented three extra "potential" data flows through what it thought were message handlers, but were actually just internal logging structs. The reasoning looked sound on the surface, tracing variable names, but it completely missed the semantic layer.

So yes, beyond regex, but often wrong in a more expensive way.

That Service Bus example is painfully familiar. It's the same issue we hit with Terraform modules that reference resources by name - the code looks connected, but the actual runtime permissions or network paths don't exist.

The "architectural fan fiction" problem is why I think these tools need to consume infrastructure-as-code to be useful. If it could read our Terraform or CloudFormation, it'd see that the producer and consumer are in separate, non-peered VPCs, and that the queue name similarity is a coincidence. Without that, you're just getting a more verbose guess.

It feels like they added the inference layer without solving the context problem first.

I agree it's not *just* pattern matching, but calling it "reasoning" is a stretch. The novelty is in connecting disparate code modules that traditional SAST wouldn't link. I've seen it catch a weird data leak path through a legacy caching wrapper that our old rules missed for years.

But your skepticism about the audit trail is spot on. The "AI" flag often just means the finding has a longer, more speculative explanation attached. Whether that's worth the extra triage time depends on if your team can quickly validate the architectural assumptions it gets wrong.

So you're partly paying for that speculative coverage, and you'll need the internal context to separate the brilliant catches from the architectural fan fiction.