Hey everyone, I've been deep in the weeds tuning our Panther setup for Kubernetes event monitoring, and I've hit a wall with alert noise. We're getting flooded—everything from normal pod evictions to transient image pull issues ends up triggering pages, and the team is starting to ignore the channel.
Our current setup is pretty standard: we're ingesting Kubernetes audit logs and a bunch of standard system detections, but the volume of low-severity alerts is drowning out the actually important stuff. I know Panther has some powerful tools for this, but I'm looking for practical strategies that have worked for you all.
Here’s what I’ve tried so far with mixed results:
- Increasing severity thresholds in the detection itself, but that feels too blunt.
- Adding more specific filters to exclude certain namespaces (like `kube-system`), but we still get noise from our application namespaces during deployments.
- Playing with alert grouping/deduplication settings, which helped a bit but didn't solve the core issue of too many *types* of alerts firing.
What I'm really curious about is how you're structuring your detection logic and severity models. Specifically:
- How do you differentiate between a "normal" pod failure during a rollout and a genuine problem that needs eyes?
- Are you using custom Python detectors to add more context (like correlating events over time) before deciding to alert?
- Have you found success in suppressing alerts during known maintenance windows or specific times?
I'm hoping we can share some concrete patterns. The goal is to keep the high-fidelity signal for actual incidents—like a node going down or a suspicious API call—without the chatter. Any workflow tips or Panther features I might be overlooking would be awesome.
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