Fortune 500 Retail Company Cuts SCA Noise by 93%

This Fortune 500 retail company operates at massive scale with over 80,000 employees worldwide. Like many enterprises of this size, they had invested heavily in security tooling, including Black Duck Polaris for software composition analysis (SCA). However, the sheer volume of findings was creating more problems than solutions, drowning their security teams in false positives and making it nearly impossible to identify truly critical vulnerabilities that needed immediate attention.

The company's security team was facing a classic enterprise problem: their SCA tools were generating far too many alerts. The high false positive rate meant security engineers were spending countless hours triaging findings that posed no real risk, while developers were pulled away from feature work to fix vulnerabilities that weren't actually exploitable in their specific context.

Critical vulnerabilities were getting buried in an avalanche of false positives, leading to longer mean time to resolution (MTTR) and missed SLA timelines. This created a dangerous cycle: the more findings they received, the less trust they had in their tools, and the more likely they were to miss something truly important.

The company had two clear objectives: dramatically reduce noise through automatic triage, and ensure that truly exploitable, critical vulnerabilities were surfaced with clear evidence to support remediation decisions. They needed a solution that could work with their existing Black Duck Polaris investment, provide transparency into triage decisions, and integrate seamlessly into their current workflows.

Konvu deployed AI agents that integrated directly with the company's existing Black Duck Polaris and GitHub workflows. No new dashboards, no disruption to existing processes. Just intelligent automation that worked behind the scenes to separate signal from noise.

The key differentiator was evidence-backed decision making. Rather than simply providing another black box score, Konvu's AI agents analyzed each vulnerability in context, examining the actual code, dependencies, and usage patterns to determine exploitability. Every triage decision came with clear evidence that security teams could review and trust.

The system pushed its decisions directly back into the existing workflow, automatically dismissing non-exploitable findings while elevating truly critical vulnerabilities with the context and evidence needed for rapid remediation.