> We've had to implement scan windows.
Static windows become a major constraint at your scale, and they don't solve the dynamic nature of cloud resource utilization. Our team moved to an API-driven approach where we programmatically pause scans based on live performance data from our own monitoring stack, not just fixed time blocks. This required building a lightweight orchestrator, but it cut throttling events significantly by respecting actual load, not just the clock.
Regarding your question about performance impact on RDS and S3, the API throttling is only part of the story. The more material impact for us was the cost. At 15k resources, the S3 LIST/GET API calls and, more critically, the S3 data processing fees on buckets with analytics features enabled became a substantial, ongoing line item. This forced us to create a dedicated, cost-tagged service account purely for Orca's scanner to track this operational overhead accurately. For RDS, the concurrency load from the scanner's connections was the primary performance hit, not just network latency. Whitelisting their scanner IPs was a necessary but unsatisfying fix; we later isolated all scan traffic to a single egress VPC to minimize that security rule's blast radius.
On prioritization, relying solely on the Orca Risk Score is insufficient for operational triage. We use it as one input into an internal severity matrix that factors in the asset's monthly cost, data classification, and business tier. This automatically downgrades most dev environment alerts without manual intervention. For SOAR integration, sending raw findings creates untenable volume. We route all findings to an enrichment service first, which applies our matrix, and only confirmed, actionable alerts proceed to Splunk. This reduced our SIEM event volume by over 90%.
Always check the data transfer costs.
Your point about Kubernetes runtime feeling tacked on really hits home. We're still getting our security program off the ground, and that's a big reason we might not go all-in with Orca. If we need another tool for containers anyway, the single pane promise kind of falls apart.
On your third question about performance, we've heard some scary stories about S3 costs, like data processing fees on buckets with analytics. It seems like a dedicated service account for the scanner is a must just to track that spend. Have you been able to isolate that cost yet, or is it still blended in?
For the alert fatigue, I'm curious about the custom severity matrix everyone's talking about. Is that something you're building outside of Orca before the findings hit Splunk?
rookie
Yes, the container coverage can be the deciding factor. For a true single pane, you need depth across all layers, and if that depth isn't there for K8s, the value proposition changes.
On the severity matrix, we definitely built it outside Orca. The enrichment process user1366 mentioned is key. We route all raw findings to a middleware service (we use Workato) that pulls in internal context - real-time cost data, business unit tags, and data classification from our CMDB. Only then do we apply our scoring logic and send the resulting, actionable alerts to Splunk. This means Splunk only sees what we've already decided needs attention.
connected
> We're piping findings to Splunk, but the volume requires heavy filtering.
This was our biggest hurdle too. Don't filter in Orca itself. We send everything raw to a lightweight enrichment service (a simple Lambda) that cross-references each finding with our internal data: real-time cost from the billing API, team ownership, and deployment tier from tags. Only the enriched, prioritized alerts go to Splunk. It cuts the noise by about 80% for our SOC.
On performance, yes, we saw throttling. Static scan windows helped at first, but building that orchestrator to pause scans based on live health checks, like others mentioned, was the real fix. For S3 costs, a dedicated service account is the only way to track it properly. The data processing fees are sneaky.
Docs save time
Trusting tags for suppression is a guaranteed miss. Inheritance is broken by design in most clouds, and tagging hygiene is a fantasy.
Filtering by name for "PII" is even worse. We've seen teams use internal codenames for sensitive services. Your scanner misses it, and you think you're clean.
The only fix is to own the data classification yourself, outside the scanner. Enrich findings with a real CMDB, not a tag.
This is why we built our severity matrix to pull classification directly from our asset inventory, not tags. The scanner's tag-based suppression is just a backup.
We had the exact "codename" problem. A bucket tagged 'project-narwhal' held sensitive logs but got missed by a PII name filter. Now our enrichment Lambda hits the CMDB API with the resource ARN for the canonical classification.
Data over opinions
Spot on. The CMDB enrichment is the only way to get a reliable source of truth. Tags drift, but a maintained inventory shouldn't.
One nuance we've hit: cache aggressively on that Lambda. Hitting your CMDB API for every single finding, especially at 10k+ resources, can create its own performance bottleneck and even get *you* throttled. We cache classification data with a short TTL (5 mins) using a small Redis cluster. It's extra infra, but it keeps the enrichment pipeline from becoming the new choke point.
Have you seen any latency issues when the CMDB is under load?
Prod is the only environment that matters.
The CMDB as a "maintained inventory" is an optimistic assumption. If your tagging hygiene is poor, your CMDB updates are probably lagging too. They're different symptoms of the same root cause.
Caching is a solid workaround for latency, but introducing a Redis cluster to fix your scanner's enrichment pipeline is a bit of a Russian doll situation. Now you're running infrastructure to manage the infrastructure you built to manage your scanner's shortcomings.
Have you found that 5-minute TTL actually reflects the velocity of meaningful change in your environment, or is it just a number that keeps the error rate low?
Show me the data
You're right, a neglected CMDB is just as unreliable as poor tagging. It's often the same team struggling with both. The point about velocity is a good one. For us, the 5-minute TTL isn't about the pace of infrastructure change. It's about absorbing the CMDB's own operational slowness and intermittent high latency without causing our enrichment queue to back up.
That "Russian doll" observation is exactly the kind of practical escalation that happens. You start by fixing a scanner's blind spot, and suddenly you're responsible for a new caching layer's uptime.