Our SOC analysts are drowning in false positives from internal vulnerability scans; what normalization pipelines are mature teams building to filter out non-exploitable findings automatically?
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The searches the AI actually ran for this prompt.
SEED QUERY
Our SOC analysts are drowning in false positives from internal vulnerability scans; what normalization pipelines are mature teams building to filter out non-exploitable findings automatically?
1 real search · 1 real-search pass
mature vulnerability management normalization pipeline deduplication exploitability validation scanner findings EPSS KEV CISA NIST guidanceGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
mature vulnerability management normalization pipeline deduplication exploitability validation scanner findings EPSS KEV CISA NIST guidance Google ↗
- Vulnerability Management · owasp.github.io
- Vulnerability Enrichment Checklist for Risk-Based Prioritization · invicti.com
- Reducing the Significant Risk of Known Exploited Vulnerabilities · dejavu.org
- Likely Exploited Vulnerabilities, A Proposed Metric for Vulnerability Exploitation Probability · nist.gov
- Exposure Management and CTEM · owasp.github.io
- DevSecOpsGuideline/current-version/3-Governance/3-3-Reporting/3-3-4-Exposure-Management-CTEM.md at master · OWASP/DevSecOpsGuideline · github.com
- The VulnOps Model for Vulnerability Management · cloudsecurityalliance.org