Vulnerability scanners produce findings faster than manual triage can process them. Your developers ship more code with more dependencies, and the volume of candidate findings grows with it.
Many findings a scanner produces are unlikely to be exploited. The ones that matter need to reach an engineer fast, with enough evidence that they can act immediately rather than repeat the analysis. The challenge is separating signal from noise at the speed your pipeline demands.
This post shows you how to close the gap between detection and action. We built a three-layer pipeline that takes raw scanner findings and narrows them to a small, prioritized set with documented evidence of exploitability. The companion post, Configuring your AI vulnerability harness, will cover the steering file that drives the model’s behavior inside this pipeline. This post covers the layers around it.
Because the pipeline’s value comes from its filtering logic and evidence…
https://aws.amazon.com/blogs/security/building-your-ai-vulnerability-harness-part-1/
