Five Weeks To Under A Day:
Scaling Vulnerability Remediation For a Global Data-Analytics Company
A global Data-Analytics company used Backline’s autonomous remediation platform to resolve a six-figure vulnerability backlog across thousands of repositories, without the security team waiting in anyone’s queue to do it.
Time To Fix
Leverage
Hands-Free
Offload
The Challenge
A backlog that grew faster than the team fixing it
Our client ships products across thousands of repositories, the kind of scale that comes with running global data infrastructure for mobile marketers and brands worldwide. Security debt compounded alongside it, growing into a backlog the team could triage but never close.
By the time the security team brought in Backline, that backlog had crossed into six figures: hundreds of thousands of open vulnerabilities spread across a codebase too large, and too interconnected, for manual triage to keep pace with.
Backlog Volume
Hundreds of thousands of open vulnerabilities across thousands of distinct repositories.
OSS Complexity
Multi-version open-source dependency upgrades tangled inside deeply nested projects.
Production sensitivity
Legacy dependencies and high-stakes attribution APIs that couldn't tolerate a bad patch.
Triage bottleneck
A one-to-one, vulnerability-to-patch model consuming roughly a quarter of the team's capacity, and still losing ground.
The security team needed a way to fix vulnerabilities at the speed and pace the codebase grew.
The Solution
From flagged to fixed
Backline’s trusted team of agents took over the fixing work itself, running reachability, exploitability and fixability analysis on top of the existing toolchain to trace which of the hundreds of thousands of flagged issues sat on a path to discriminating exposure.
From there, Backline’s agents generated, tested, and shipped production-ready fixes instead of tickets. Whenever possible, one fix cleared a cluster of related issues at once.
The Results
Drastically Cut Exposure, Risk and MTTR
The clearest result showed up in the fix ratio. A traditional one-to-one approach would have meant hundreds of separate patches, pull requests and reviews. Backline’s agents clustered related issues and shipped few dozen fixes instead, cutting review volume by more than 90% without skipping a single vulnerability.
Reclaiming 25% of the team’s cycle changed what that cycle was spent on. Hours that had gone to triage and patch review now go to the security work only people can do.
The bigger test measured risk tolerance. Major version upgrades are usually the remediation work everyone avoids, because a bad upgrade breaks production faster than an unpatched CVE does. Backline’s verification step ran existing tests and regression checks before any fix reached a human reviewer, clearing the way for upgrades the team had previously ruled out as too risky to touch.
Why It Matters
What changed for the team
Major version upgrades and legacy, high-sensitivity code shipped clean, without breaking production.
The manual triage bottleneck no longer set the ceiling on how fast the security posture could scale with deployment
Every fix ran through regression checks before a human saw the pull request, so review time went to judgment calls, not ‘defect-hunting’.
A quarter of engineering capacity moved from patch triage back to product work, a gain that holds every sprint after this one.
INDUSTRY
SaaS, mobile data & analytics
COMPANY SIZE
~5000 employees
Headquarters
California, US
ENGAGEMENT
Enterprise autonomous remediation
Curious what this looks like against your own backlog?
