Hey everyone! I've been running Orca Security's agentless scanning across our AWS and Azure production environments for about eight months now, and while the overall visibility is fantastic, we've definitely hit some real snags at scale.
The core issue is that large, dynamic clusters aren't just bigger—they behave differently. Our primary production cluster has over 500 nodes, and we see a few consistent pain points:
* **Scan Duration & Cloud API Throttling:** The initial full inventory scan can take *hours*. More critically, during peak business hours, the volume of API calls Orca makes to our cloud providers sometimes triggers throttling. We've had to work closely with our cloud teams to adjust rate limits, which isn't always straightforward.
* **Ephemeral Resource "Noise":** In our Kubernetes namespaces, pods spin up and down constantly. We get alerts on vulnerabilities in pods that are already gone by the time an engineer looks at the dashboard. It creates alert fatigue and can cause real issues to be buried.
* **Cost Attribution at Scale:** Orca gives you the cloud asset bill, which is great. But in a large, shared-account environment with dozens of teams, pinpointing *exactly* which team's workload is driving the most risk findings requires a lot of manual cross-referencing with our internal chargeback reports. The "Team" tagging isn't always granular enough.
We've developed some workarounds, like scheduling major scans during our maintenance windows and implementing more aggressive tagging policies, but it's an ongoing process.
I'm curious—has anyone else running Orca in really large (500+ node) environments faced similar issues? How did you handle the API throttling and the ephemeral resource challenge? Any best practices you've landed on?
—Amy
You've nailed the two biggest operational headaches with agentless at scale. That API throttling is a silent killer. We found Orca's read-only IAM role needed a staggering number of permissions, and the call volume during a scan window looked like a DDoS on our CloudTrail. We had to build a separate service account just for it and implement custom retry logic in our side of the event bus.
On the ephemeral resource noise, it's a data freshness problem inherent to the polling model. Have you looked at coupling the Orca findings feed with a real-time event stream from your cluster? We pipe the alerts to Splunk and correlate them with container lifecycle events (like a pod termination). If the pod doesn't exist in the last five minutes of our real-time log, we automatically suppress the Orca alert. It cut down the noise by about 70% for those transient workloads.
The cost attribution point you're starting to make is huge. Wait until you try to map a critical vulnerability on a shared EC2 instance back to the specific team's cost center using only the cloud bill data. The tags are never consistent enough.
Logs don't lie.