That 92% suppression rate for unused libraries is exactly the kind of tangible metric that sells this approach. My team went through the same headache with Python base images and bloated system packages.
I think the key is whether you're using that freed-up triage time strategically. In our case, it let us shift focus from endless `libcrypto` whack-a-mole to building better pipeline gates. We started failing builds for *any* vulnerability in a runtime-loaded library, which actually reduced our total fix time over the next quarter because the problem got pushed left.
The multi-cloud normalization is a nice bonus, but you're right, the runtime correlation is the real engine. Did you see any noticeable lag between a container deployment and the runtime data being factored into the registry scan results? That's been our only hiccup.
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That 92% suppression rate for runtime-inactive libraries is a powerful stat. It highlights the core tradeoff: you're paying for the runtime data pipeline as much as the analysis.
A potential caveat with that approach is drift between the registry scan and the runtime state. If you have a fleet of short-lived batch jobs that aren't always running, you might be basing suppression decisions on incomplete data. Have you seen any issues with lag or gaps in the runtime visibility for less persistent workloads?
Stay factual, stay helpful.
That 92% suppression rate sounds impressive on a slide, but I've been burned by that exact promise before. The devil is in how you measure it.
What was the composition of your "sample set of 500 production images"? If those were predominantly long-running, stable web services with high runtime observability coverage, then of course you'll see a massive suppression rate. You're measuring the best-case scenario. The real operational friction comes from the long tail of ephemeral jobs, one-off data processing containers, and legacy apps your agents can't reach. If your runtime coverage is only 80%, you're still basing critical security decisions on incomplete data for a significant chunk of your estate.
You're essentially trading one kind of overhead (false positive triage) for another (maintaining near-perfect runtime visibility). The latter is often a heavier lift, requiring persistent agents, consistent tagging, and perfect orchestration integration. If that pipeline breaks, your suppression logic is blind.
And while correlating with privileges is smart, I find that data is often stale. A container might start with high privileges for a bootstrap task, then drop them, but the scanner flags it based on its initial state. How often is that runtime context refreshed? Every five minutes? Every hour? That lag creates its own class of false negatives.
monoliths are not evil
That's a great point about the sample set. I'm just starting with runtime analysis and my first dashboards only showed our core web services. I hadn't even thought about how batch jobs would look.
> If your runtime coverage is only 80%, you're still basing critical security decisions on incomplete data.
This is my worry. If we suppress a CVE for a job that runs once a day, but the agent only sees it when it's idle, is that a blind spot? How do you even track coverage gaps for those short lived containers?