Hey folks, I've been wrestling with getting a clean baseline for our new SAST and SCA setup. The initial scans were drowning us in thousands of findings, most of which were inherited or in code we can't touch right now. It made the whole thing feel useless.
I found that the key is to establish a "known-good" baseline from day one, before you even think about fixing anything. Here's the practical approach that worked for our team:
**First, run your initial scan to capture EVERYTHING.**
* Don't apply any filters yet. Let the tool (we're using Checkmarx and Snyk, but this applies to most) find every vulnerability and license issue.
* Export this full report. This is your "time zero" snapshot.
**Second, categorize the findings into actionable buckets.**
We sorted everything into three groups:
1. **Legacy / Technical Debt:** Vulnerabilities in third-party libraries or old modules that are approved but not scheduled for update.
2. **Accepted Risks:** Findings in internal code that are false positives or intentional design choices (e.g., a hardcoded password in a test fixture).
3. **Net-New & Critical:** Everything else—especially new vulnerabilities introduced *after* the baseline date.
**Third, create your baseline exclusions.**
Most tools let you suppress findings by a fingerprint or ID. We took groups 1 & 2 and created a baseline suppression file (in Snyk it's a `.snyk` policy file, in Checkmarx you can use query IDs). This is NOT about ignoring issues forever, but about silencing the historical noise so new PRs only highlight *new* problems.
Now, our CI/CD pipeline only flags findings **not in the baseline**. It cut our initial PR scan noise by about 70%, making the tool actually usable for developers. How do other tools like SonarQube or Semgrep handle this baseline concept? I'm curious if their approach is more or less granular.
Cheers,
Carla
Benchmarking my way to better decisions
"Known-good baseline" sounds nice in theory. But what happens when that giant "legacy debt" bucket gets ignored for six quarters and your tool starts flagging the same old vulnerabilities in every new scan report? The noise never actually goes away, you just stop looking at it.
Trust but verify.
You've correctly identified the primary risk with the "legacy debt" bucket: out-of-sight becomes out-of-mind. To prevent this, your baseline process must be integrated into your CI/CD governance.
The critical step missing from the OP's categorization is a formal review and expiration mechanism. You need a documented policy stating that items in the "accepted" or "legacy" buckets require re-approval, say, every quarter. This forces periodic business justification.
Technically, you should also tag these suppressed findings in the tool itself (e.g., using Checkmarx's "Not Exploitable" with a custom comment like `BASELINE-2024-Q2-APPROVER-jdoe`). This creates an audit trail and allows you to run reports showing only suppressed issues that are older than your policy's expiry period.
infrastructure is code
Integrating the baseline with CI/CD governance is the only way this doesn't become technical debt. The quarterly re-approval cadence is smart, but you must tie it to a measurable performance penalty or it gets deprioritized.
We enforce this by having our pipeline fail if a baseline finding's expiry date passes without a new Jira ticket linked to its suppression comment. The ticket must include the business justification and, critically, the calculated risk-acceptance latency. If the review isn't done, the scan treats it as a new, active finding and breaks the build.
Your tagging format is good for audit, but add a timestamp for the *next* required review, not just the approval date. That way, your report query is a simple date comparison against `current_date`. Otherwise, you're parsing strings to calculate the next quarter.
--perf
Love the three-bucket approach. Smart.
The **Net-New & Critical** bucket is where your process really proves its value. But it can still get noisy if you're not ruthless. We treat anything that lands there as a build-blocker for *new* code paths only. Legacy gets a grace period. That keeps the team focused.
Also, how do you handle findings that move between buckets? Like when a "legacy" library finally gets updated in a patch, but the scan still flags the old CVE because the version string is cached somewhere. That's been a time-sink for us.
Demo or it didn't happen
That three bucket split makes a ton of sense. It's basically what we tried to do by feel in our spreadsheets.
How do you decide which bucket a finding goes into? Is it a team lead call, or do you have a checklist? Setting that rule seems like the hardest part.
The bucket system falls apart the minute you do your first dependency update. You label a library as "Legacy Debt," then patch it next sprint. Your tool still flags the old CVE because its internal cache or the NVD data hasn't refreshed. Now you're manually hunting down ghosts in the report to move it out of the bucket.
The real baseline needs to be machine-readable and version-locked. If you suppress a CVE for library-X at version 1.2, that suppression should be invalidated automatically when the manifest shows library-X at version 1.3. Otherwise your buckets are just a fancy todo list that gets stale.
-- bb
If you're deciding by feel in spreadsheets, you've already lost. That doesn't scale.
You need a rule engine, even a simple one. We set ours as a checklist in our ticketing system so the decision is documented.
* **Net-New & Critical:** Any finding in code merged *after* the baseline date. No exceptions.
* **Legacy Debt:** Library or module version hasn't changed in the last 12 months. Requires a link to the deprecation ticket.
* **False Positive:** Confirmed not exploitable in our context. Requires a snippet from the SAST tool's query to show why.
The team lead just applies the rules; they aren't making judgment calls from scratch. The real fight is getting everyone to agree on the rules once.
Integration is not a project, it's a lifestyle.
That "time zero" snapshot is a great first step. It's exactly how I started making sense of my own Grafana alert floods.
I hit a similar wall trying to build dashboards without first deciding what "normal" even looked like. Grabbed a full week of noisy metrics, warts and all, as a baseline. It made the important signals stand out.
For your three buckets, how do you actually tag or annotate them in the tools? Do you use a label or a custom field, or just keep a separate spreadsheet?
That "time zero" snapshot is such a crucial move. We did the same thing, but we immediately imported that full report into a simple database. It let us run queries to spot patterns we'd have missed in the UI, like which specific libraries were responsible for 80% of the "Legacy Debt" bucket.
Keeping the snapshot as a living artifact, rather than a static export, makes those bucket migrations others mentioned much easier to track over time. You can query for findings that were in "Legacy" last quarter but aren't present in this week's scan, automatically closing them out.
api first
You're spot on about the rule engine being non-negotiable for scaling. That checklist approach is exactly what saved us from endless debates.
We had the same fight about getting consensus on the rules, especially that "Net-New & Critical: No exceptions" clause. People hated it until we framed it differently: those are the *only* findings the dev team needs to look at right now. The rule didn't create more work, it created a shield from the historical noise. Agreeing on the rules became easier once they saw it as a filtering mechanism, not a punishment.
One caveat on the "Legacy Debt" rule we learned the hard way: locking it to a 12-month version change can backfire if your dependencies are truly ancient. We had to add a severity threshold, so a critical CVE in a 13-month-old library still gets forced into a "Net-New" review. Otherwise, you're just rubber-stamping dangerous stuff.
don't spam bro
Yeah, that's exactly what I'm worried about too! It seems like the baseline idea only works if you actually have a process to clean the buckets out later. Otherwise, you're just hiding the mess in a different closet.
How do teams make sure they *don't* just ignore that legacy bucket? Is there a way to set up a nagging reminder or something? I'd probably forget after a couple of sprints.
I'm really glad you laid out that three-bucket structure, especially defining "Net-New & Critical" as anything after the baseline date. That clarity from the start is what I've been missing.
My question is about that initial export. When you export that "time zero" snapshot, how do you handle the format? Are you taking the raw JSON/XML from the tool, or are you using a standardized report template? I've found that if you don't capture the tool's internal ID for each finding right from that first export, linking findings back to it later for bucket classification can become a manual nightmare.