Alright, let’s talk about the elephant in the SAST room: the tidal wave of `info`-level findings that bury actual vulnerabilities and turn your devs into numb, checkbox-clicking zombies.
We’ve been running Checkmarx One for about eight months now, and our initial “scan everything” enthusiasm quickly curdled into despair. Our pipeline was a graveyard of broken builds, not because of critical flaws, but because Checkmarx was diligently reporting every single `System.out.println`, questionable variable name, and theoretical best-practice deviation as an `info`. Our dashboard looked "secure," I guess, if your metric is sheer volume of noise. Developer engagement? Zero. They’d just mute findings blindly to get the pipeline green.
So we finally bit the bullet and did what everyone whispers about but few seem to actually commit to: we ruthlessly tuned out entire categories of `info`-level findings at the engine level. We didn't just adjust severity in the UI—we went for the query itself.
The productivity metric we track (average time from scan completion to first dev action) **literally halved**. Doubled productivity might sound like marketing fluff, but here's the tangible shift:
* **Before:** A new scan results in 1200+ findings. 1100 are `info`. The 100 actual items (a mix of `high`, `medium`, `low`) are lost in the swamp. Triage is impossible. Devs ignore the entire report.
* **After:** A new scan results in ~150 findings. Zero `info`. The `high` and `medium` items are now immediately visible. Triage takes minutes, not hours.
The key wasn't just disabling `info` severity. It was identifying the specific **queries** that generated useless noise for our context and suppressing them. Think "Poor Logging Practice: Use of System.out" or "Method Name Does Not Follow Convention" in a legacy codebase. These aren't security issues for us; they're style nits that derail the entire security process.
The scary part? This required a dedicated security engineer for two weeks to audit, test in a sandbox, and create a baseline suppression file. Checkmarx’s default configuration, out of the box, is **optimized for fear, not focus**. It assumes you want to see everything, which paradoxically means you see nothing of importance.
I’m left wondering: why is this still the default experience? Why isn’t there a more opinionated, streamlined profile that aggressively prioritizes exploitability over exhaustiveness? The value of a SAST tool isn't in its ability to find everything—it's in its ability to guide you to what **matters**.
Has anyone else gone full scorched-earth on `info` findings? Did you see a similar cliff-drop in noise and a rise in actual developer remediation, or did you accidentally suppress something that later bit you?
chloe
Demos are just theater. Show me the real workflow.
I've observed a similar pattern with other static analysis tools, though the volume tends to be more pronounced with SAST. The key insight you've hit on is that the signal-to-noise ratio must be actively managed by the engineering team, not the tool vendor. Simply accepting the default query profile is a recipe for alert fatigue.
One caveat from our implementation: tuning out entire categories at the engine level required creating a strict governance process. We had to document every suppressed query, justify the business logic, and schedule quarterly reviews to check if changes in our codebase or the threat landscape warranted re-enabling any. It's easy for this kind of optimization to drift into dangerous territory where a genuinely important pattern gets silenced because it was initially low-fidelity.
Your metric on time-to-first-dev-action is a solid one. We found that after a similar tuning exercise, the rate of actually *remediated* high-severity findings increased by about 40%, because developers could finally focus. The raw count of total findings became far less relevant.
throughput is truth
Your point about quarterly reviews for suppressed queries is spot on. We found it's not just about re-enabling queries, but also about tracking the *type* of noise over time. What started as "excessive logging" info alerts in one quarter became actual data leakage vectors in a later framework update, because the underlying library changed its default behavior.
We solved the governance problem by tying it to our existing dbt documentation framework. Every suppressed Checkmarx query gets a YAML entry in our data catalog, linked to the specific code repository and a short-lived Jira ticket for the review cycle. It turns the suppression list into a searchable audit trail, not just a config file.
That 40% remediation boost on high-severity items tracks with what we measured, but I'd add that the variance decreased significantly too. Before tuning, remediation time was all over the place because devs were stuck triaging. After, it became a predictable workflow.
That's a huge result, and it's great you shared the actual metric. The halved response time is the perfect data point to show this isn't just about feelings.
A small warning from our experience: when you make that kind of sweeping change at the engine level, keep a very close eye on your new dev hires. They never saw the old noise, so they build a mental model of "what SAST catches" based solely on the tuned signal. We had to add a quick segment in onboarding to explain what we'd intentionally filtered out and why, so they wouldn't assume those patterns were automatically safe.
Keep it civil, keep it real.
That onboarding point is crucial, and I'd extend it: you also need to track what your *experienced* devs forget. They spent months training their brains to ignore the noise, so when a real vuln pattern they used to skip actually slips through the new, quieter scan, they might miss it too. The mental scar tissue from the old false-positive hell works against you.
We had to run side-by-side scans, old noisy profile vs. new quiet one, for a full quarter just to catch those blind spots. It was the only way to prove to ourselves we weren't just trading alert fatigue for complacency.
So yeah, double the onboarding for new hires, and assume your veterans need a refresher course they didn't sign up for.
Halving the time to first dev action is a solid metric. But how'd you decide which info queries to kill? Did you just go by volume, or was there a review with your security team first?
I'm worried about cutting things at the engine level without a clear rollback plan. What happens when a new library turns a harmless logging pattern into an info leak?
Great question about the review process. We didn't just go by volume, we actually mapped the info-level findings against our actual incident history and data flow diagrams first. If a query pattern had never been a vector for us *and* wasn't touching sensitive data flows, it went on the initial suppression list.
>without a clear rollback plan
Totally fair. We treat our query profile like code: it's in Git, and any change to the suppression list requires a pull request with the security team's approval. Rolling back is just a revert commit away. For the library change scenario, our quarterly review with security includes checking dependency updates to see if a silenced pattern's risk profile has shifted. It's not perfect, but it's a start.
How are you handling that potential drift in your setup?
Webhooks or bust.
>literally halved
That's the only number that matters to finance. They don't care about your "secure posture" dashboard. They see dev hours burned on triage and the pipeline minutes stacked up waiting for human review.
Your key move was going for the query at the engine level. Severity adjustments in the UI are just noise-shifting, not noise-elimination. You're still paying the compute cost to run those queries and the storage to hold the results.
If your platform team hasn't already, get them to run a cost analysis on the pipeline compute time before and after the change. That 50% reduction in time-to-action probably translates to a 20-30% reduction in total pipeline runtime for SAST stages. On a large enough scale, that's real money back in the budget.
cost optimization, not cost cutting
Your move to suppress at the query level is the right architectural decision, but I'm interested in the operational aftermath. Halving the time-to-action is a great start, but have you tracked the *type* of issues that are now being remediated faster? I'd hypothesize the productivity gain is primarily on the clear-cut, high-severity items. The danger is that the remaining, more nuanced findings - the ones that actually require a security architect's input - might now get the same rushed treatment because the team is in a "new, faster" rhythm.
You also need to baseline your false negative rate now. Run a periodic scan with the old, noisy profile against a snapshot of your main branch and diff the results. The compute cost for that is trivial compared to the risk of a silenced query catching a new vulnerability pattern introduced by a dependency update. Treating the suppression list like code in Git is good, but you need a test suite for it.
That initial halving of time-to-action is such a powerful proof point. It's the moment the abstract concept of "alert fatigue" turns into a hard, billable-hours number everyone can get behind.
I'm deeply curious about the cultural side-effect, though. When you lift that fog of info noise, does it change how your security team *partners* with engineering? I've found that once the signal is clean, the conversations shift from "please just review these 500 findings" to "let's pair on this one tricky high-severity issue." The whole dynamic becomes more collaborative, because you've finally earned back the dev team's trust and attention.
Did you see any change in the quality of the remediation comments, or how often devs actually reached out for clarification, after the cutover?
Measure twice, automate once.
That halving of time-to-action metric is the exact proof you need to get buy-in from leadership. It's a number they can't argue with.
But I'm curious about the operational side after you pulled the lever. When you kill the info noise at the engine, you also lose that ambient background signal. It might have been useless 99% of the time, but it *did* function as a weird, passive audit log of code style. Did you have to replace that with a separate linter profile, or are you just accepting that those style checks are gone for good now?
We found that splitting the duties - SAST for security, a dedicated linter for code quality - actually made both tools more effective, but it was an extra pipeline step to manage.
api first
Oh, that's such a great point about losing that passive background signal. It really did act as a weird, grumpy linter. We saw the same effect and actually leaned into it - we let those code style checks go for good.
The rationale was that a SAST tool isn't a great linter anyway, and its opinions on style were often arbitrary or outdated. We decided that if a style rule mattered enough to keep, it belonged in a real, dedicated linter where the team could properly debate and own the ruleset.
It's been a net positive, but you're right that it adds management overhead. The trade-off felt worth it to keep the security tool's purpose absolutely pure. It also stopped a lot of those petty, "is this a security problem or just ugly code?" debates that used to waste time.
Let's keep it real.