Security scanners are designed to be paranoid. They will report the same issue every time a pipeline runs. If you have 50 microservices running 10 builds a day, a single persistent vulnerability can generate 500 "new" alerts in a week.

If you push all 500 alerts to your engineering team, you have lost them. They will view security as a spam cannon.

The solution is not to scan less; it is to process better. By implementing Auto-Triage and Deduplication, you filter out the noise so engineers only see the net new risks that actually require action.

The 3 Sources of AppSec Noise

To fix alert fatigue, we first have to understand where the duplicates come from:

• Pipeline Repetition: A SAST tool scans the same code on Monday, Tuesday, and Wednesday. It reports the same XSS vulnerability three times. Without deduplication, this looks like three separate tickets.
• Tool Overlap: You use Snyk for code analysis and Wiz for container scanning. Both find an outdated OpenSSL library. That is one problem, but two findings.
• False Positives: A scanner flags a test file as "Critical." You mark it as a False Positive. Next week, the scanner finds it again. Do you have to triage it again?

How Auto-Triage and Deduplication Work

Mature vulnerability management platforms act as a noise filter between your scanners and your developers. Here is how the logic works in a modern 2026 stack:

1. Intelligent Deduplication

Instead of treating every scan output as a new list, the system compares incoming findings against the existing database. It uses hashing algorithms (looking at file path, line number, and vulnerability type) to recognize identity.

The Result: That persistent XSS vulnerability? It is now one single finding record that updates its "Last Seen" date, rather than spawning 100 new tickets.

2. "Mark Once, Ignore Forever" (False Positive Memory)

Triage is expensive. You should never have to make the same decision twice. Auto-triage systems remember your history.

If an engineer marks a specific finding as a "False Positive" or "Accepted Risk," the system applies a fingerprint to that decision. When the scanner reports it again next week, the system automatically suppresses it, keeping your backlog clean without human intervention.

3. Contextual Severity Adjustment

Not all findings are equal. Auto-triage rules can automatically downgrade severity based on context. For example, a "Critical" vulnerability found in a repository tagged `archive` or `test` can be automatically downgraded to "Info" or "Low," ensuring that PagerDuty only rings for production risks.

The DefectDojo Pro Approach

DefectDojo Pro is built on the premise that Deduplication is a core feature, not an afterthought. It serves as the intelligent layer that cleans your data before it ever touches Jira.

• Native Deduplication: DefectDojo automatically merges duplicate findings from the same scanner. It can also be configured to merge findings across different scanners (e.g., merging a DAST finding with a corresponding SAST finding).
• SLA-Aware Status: When a finding is duplicated, DefectDojo preserves the original discovery date. This prevents "SLA Cheating"—you can't reset the clock just because a new scan ran.
• Granular Triage Rules: Users can write Python-based rules or use the GUI to define logic like: "If Scanner = Checkmarx AND CWE = 79 AND File Path contains /test/, automatically mark as False Positive."

Conclusion: Respect Your Engineers' Time

Alert fatigue is not a personnel problem; it is a process problem. By implementing auto-triage and deduplication, you transform your security program from a noise generator into a signal generator.

When you send a ticket to engineering in 2026, it should mean one thing: "This is real, it is new, and it needs fixing."

Silence the Noise

Stop drowning in duplicates. See how DefectDojo Pro's deduplication engine reduces finding volume by up to 90%.

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