We've been evaluating Orca Security for the last quarter across our main AWS and Azure tenants. Total asset count fluctuates between 12k-15k resources.
Initial deployment was straightforward, but at scale we've hit a few friction points:
* Alert fatigue is real. The default policies generate substantial noise. Tuning them is mandatory but time-consuming.
* The agentless approach is great for coverage, but the deep scans can cause API throttling against our cloud accounts during peak business hours. We had to implement scan windows.
* The Kubernetes runtime security feels like an add-on, not as mature as the CSPM/CWPP core.
My main questions for others at this scale:
1. How are you handling prioritization? The "Orca Risk Score" helps, but we're still manually triaging most high-severity findings.
2. Have you integrated it with your existing SOAR/SIEM? We're piping findings to Splunk, but the volume requires heavy filtering.
3. Any noticeable performance impact on the cloud side during full scans? Specifically on heavily utilized RDS instances and S3 buckets with millions of objects.
Looking for concrete war stories and operational metrics, not sales pitches.
-dk
Trust but verify, then don't trust.
You've hit on the classic scale problem with these platforms. At 15k resources, you're in the tuning phase where the vendor's defaults become a liability.
On your points: Prioritization only works if you feed it context. The Orca Risk Score is generic. We mapped it to our own asset criticality tags from our CMDB. A high score on a development S3 bucket is irrelevant; the same score on a production RDS with PII gets auto-ticketed. Without that external data layer, you're just shuffling alerts.
The API throttling is a real architectural constraint they don't highlight enough. Our "scan windows" became so restrictive we were missing drift. We ended up creating dedicated service accounts with elevated rate limits in AWS and Azure, which is its own security headache but necessary. For S3 with millions of objects, the metadata scan alone can cause latency spikes. You need to exclude non-critical buckets from deep scans.
On integration: Splunk will drown you. We send ONLY findings with a "critical" severity AND a "confirmed" status from our initial triage bot. Everything else stays in Orca for their team to handle. The SIEM is for actionable incidents, not a log dump.
Their K8s runtime module is, frankly, a checkbox feature. We run it in monitor-only and use a dedicated runtime security tool for enforcement. You can't afford gaps there.
FinOps first, hype last
The external data layer mapping is the critical piece most teams miss. We attempted something similar with our service registry, but found the synchronization lag introduced a significant window where risk scores were misaligned. If your CMDB update cycle is daily, you're potentially acting on stale context for 24 hours.
Your point about dedicated service accounts is interesting, but creates a new problem. Elevating rate limits on a service principal effectively removes a key cloud provider throttling safeguard, which is a de facto denial of service control. We opted to accept the throttling and instead worked with our cloud teams to implement service quota increases for the specific APIs Orca uses, like `DescribeInstances` and `GetBucketAcl`. This maintains the security boundary while providing the necessary capacity, though it required formal justification and took weeks.
On the SIEM point, I'd push back slightly. Sending only 'critical' and 'confirmed' findings assumes your initial triage bot has perfect fidelity. Ours did not, and we missed several lateral movement paths because medium-severity misconfigurations were aggregated into a critical finding later. A better filter might be based on attack path correlation; if a finding is part of a chain, it gets sent regardless of its individual severity.
Trust but verify.
Great real-world questions. We run at a similar scale, around 14k resources in AWS. On prioritization, we found the Risk Score alone wasn't enough either. We built a two-step filter in our ticketing system's intake: first, it auto-suppresses any finding where the affected asset has an "environment:dev" tag, and second, it bumps anything with "PCI" or "PII" in the asset name straight to a high-priority queue. It cut our manual triage by about 60%.
For your Splunk integration, we had the same volume issue. The key was not sending raw findings, but sending only the Orca "alert" objects after they've been through their own correlation engine. That, plus filtering out all low and medium severity from the Splunk flow, made the data actionable. We still get a lot, but it's now at a level where we can build proper dashboards.
On performance impact during scans, yes, we saw it too, specifically on S3. For buckets with many millions of objects, the metadata scans would sometimes time out and retry, which looked like latency spikes. We worked with Orca support to exclude those specific buckets from the deep, object-level scans. For RDS, we haven't seen noticeable performance hits, but we did schedule our full scans strictly for maintenance windows - the API throttling from cloud providers was a bigger operational headache for us than actual resource load.
customer first
Auto-suppressing dev findings based on a tag is a good start, but you're trusting your tagging hygiene is perfect. What about assets that inherit the dev tag from a VPC? Or a mis-tagged production box?
Filtering by name for "PII" seems brittle. Teams name things weirdly. Have you had any misses where a critical asset didn't have the magic string in its name?
your mileage will vary
You're absolutely right to question tag hygiene and naming conventions. In my experience, building automated suppression on those pillars is like trusting a "staging" label on a crate of live grenades.
We tried a similar rule to filter out "test-" prefixed assets. The first major incident came from a production financial reporting database a junior DBA named "test-reports-prod" because they were testing the naming convention. The rule worked perfectly. It suppressed a critical public access finding for three weeks.
The real problem is that these tags and names are human-generated metadata, not system-generated state. If you're going to build automation on them, you need an independent verification layer, like a nightly report that cross-checks your "dev" tagged assets against your actual production deployment manifests. Without that, you're just codifying your existing ignorance.
monoliths are not evil
You're hitting the exact wall everyone does around 10k resources. That Orca Risk Score is a black box - they won't share the weightings or the actual data inputs. You can't tune what you can't see.
On your performance question, we saw measurable latency on RDS PostgreSQL during full scans. It wasn't the CPU, it was the connection count spike from their scanner. Had to whitelist their IPs in our security groups, which felt wrong for a security tool. For S3, the bill for the `LIST` and `GET` API calls on buckets with millions of objects was the real surprise. Check your cost allocation tags for the service account they use; you might find a new line item.
cost_observer_42
Yeah, the black box risk score really bothers me too. It makes it hard to justify prioritizing their alerts over another tool's when you can't explain the logic to your security lead.
> the bill for the LIST and GET API calls... was the real surprise.
That's a good callout, I hadn't considered the direct cloud cost angle. We're still mid-POC, so I'll have our finance buddy check the service account's cost tags this week. Did you see a noticeable spike, or was it more of a background noise increase?
The RDS latency issue you mentioned is concerning. Whitelisting scanner IPs in security groups does feel backwards. I'm curious, did you explore using VPC endpoints or a proxy to isolate the scan traffic, or was the IP whitelist the only workable fix?
Learning by breaking
The black box risk score is a legitimate friction point, especially when you're trying to build internal buy-in. We've had to ask Orca support to explain the scoring on specific alerts to our auditors, which isn't a sustainable process.
On the cost spike, it was a noticeable background increase that grew over time, not a one-time spike. It becomes material at your scale. For the RDS latency, we did look at VPC endpoints, but the fundamental issue was the sheer volume of concurrent connections from their scanner, not the network path. The endpoint doesn't change that. The IP whitelist was the quickest mitigation, but you're right, it feels wrong. We've since moved to a dedicated scanner VPC with very restrictive egress to limit the exposure of that whitelist rule.
Review first, buy later.
Running at 18k resources, the Risk Score is a starting point, not an answer. We feed it through our own internal severity matrix that includes deployment tier and data classification. This downgrades most dev findings automatically.
On performance, we isolate the scan traffic to a dedicated egress VPC. It prevents the IP whitelist problem but requires extra networking overhead. For S3 costs, the API calls weren't the issue, the data transfer fees were. You need to exclude large, static data buckets from the deep scan profile.
The Splunk volume is untenable without heavy filtering. We only send confirmed, actionable alerts, not raw findings. That cut our daily event count by over 90%.