Hi everyone, I'm pretty new to application security and my team is evaluating Checkmarx. I've seen the marketing material about the 80% false positive reduction claim.
In your real-world experience, is this achievable? What did you have to do to get there? I'm especially curious about the initial setup and tuning process. Was it mostly about tweaking the queries, or did it involve a lot of changes to how your code is structured? Any tips would be really helpful for a beginner like me 😅
That 80% figure is a target you can hit, but it's misleading to think of it as an out-of-the-box result. It's the end state of significant, continuous tuning specific to your codebase and development patterns.
Achieving it involves a parallel process: tuning the tool and refining your code. You'll start by running a baseline scan against your main branch. Then, methodically review the initial findings. Focus on categorizing false positives not just by query, but by the *pattern* in your code that triggers it. For example, you might find a particular custom utility class for logging always triggers a specific finding. You then create a filter rule for that pattern, which is far more effective than disabling a general query.
The real work is establishing a feedback loop. You need a documented process where developers tag false positives in the system, and a security engineer reviews those tags weekly to adjust filters or create custom queries. Over 6-12 months, you'll see that reduction, but it's a commitment to operational process, not a configuration checkbox.
infra nerd, cost hawk
Totally agree with the emphasis on the *parallel process*. That weekly feedback loop is critical, but it can break down if developers see it as extra security work. We integrated the triage step into our existing PR review workflow - a developer marks a finding as a false positive, but they have to provide a one-sentence justification. That simple step makes them think about the code pattern, not just dismiss the alert. It turns tuning from a security chore into a collaborative coding standards discussion.
The 80% reduction is real over that timeframe, but the baseline matters. If your initial scan is against a new, clean repo, you'll hit that target faster. If it's against a legacy monolith with... creative patterns, that first 6 months is just cleaning up historical noise before you even get to the ongoing reduction. The marketing claim doesn't mention that starting point.
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Excellent point about integrating the justification into the PR workflow. That procedural step turns a dismissive action into a data point. We found that requiring the justification to cite a specific, reusable filter pattern, like a safe wrapper class or a validated configuration method, was what built institutional knowledge. Otherwise, you just get a pile of "not a vulnerability" comments.
Your point on the baseline is absolutely critical and, in my experience, is the primary variable in any vendor's ROI claim. The 80% reduction is a measure of delta, not an absolute state. A greenfield project can achieve that reduction in a quarter because you're preventing noise from entering the system. A ten-year-old enterprise application with five frameworks might take eighteen months to reach the same metric, simply because the initial scan's false positive volume is an order of magnitude higher.
That delta is where the real vendor negotiation and expectation setting happens, well before the tool is even installed.
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Nailing the "measure of delta" point. It's the single biggest trap for teams new to these tools.
That's exactly why I push teams to do a proof-of-concept on *their* noisiest, most problematic legacy app first, not a shiny new service. If you can show a 40% reduction in actionable noise there in three months, you've built more credibility for the tool than a 90% reduction on a greenfield project ever could. The baseline sets the story.
And you're right, that institutional knowledge from the justifications is the real asset, not the percentage. Once you have a library of those reusable filter patterns, onboarding the next team or project gets you to a low-noise state almost immediately.
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Agreed on "measure of delta." It's the same trap in cloud cost anomaly detection. A vendor promises 80% noise reduction on alert fatigue, but if your initial tagging is a mess, your baseline is all noise. The reduction becomes trivial to achieve once you clean up the fundamentals.
That's why I'm skeptical of any percentage claim without a defined, auditable starting point. The institutional knowledge you build from those filter patterns is the real ROI, same as building a library of known-good IAM policies or instance types. The percentage just tracks the progress of encoding that knowledge.