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Case Study: Starburst achieves 98.3% noise reduction and consolidates security tools with Endor Labs

Starburst Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Starburst
Industry
Data Analytics
Challenge
The previous SCA tool gave no rationale for false positives, missed transitive dependencies, and could not scan pre-deployment.
Headline result
Starburst reports a 98.3% noise reduction with Endor Labs, using function-level reachability analysis across direct and transitive dependencies inside its existing GitHub workflows.

Key results

98.3%
Noise reduction
with Endor Labs reachability-based SCA

The challenge

Starburst's previous SCA tool did not provide rationale for false positives, lacked support for transitive dependencies, and could not perform pre-deployment scanning, which forced vulnerability discovery to happen after deployment. The team needed accurate prioritization it could act on inside its existing developer workflows.

The solution

Starburst implemented Endor Labs for function-level reachability analysis across direct and transitive dependencies, with pre-deployment scanning integrated into its existing GitHub workflows. It also consolidated SCA, SAST, and secret detection onto the platform.

Endor Labs is doing reachability analysis on transitive dependencies, which is really important to us and a huge deciding factor.

AO
Alex Olea
DevSecOps Engineer, Starburst

The results, in context

Starburst reported a 98.3% noise reduction with Endor Labs and consolidated SCA, SAST, and secret detection into a single platform. Reachability analysis on transitive dependencies was cited as a key deciding factor.

Products used

Endor Labs Open Source SCA with ReachabilityEndor Labs AI SASTEndor Labs Secrets Detection