Orca's network exposure alerts are noisy. The 'publicly accessible' flag often triggers on resources behind WAFs, private endpoints, or with restrictive security groups. You're getting SLA breaches for non-issues.
You need to filter at the source and in Orca itself.
**First, validate the finding.** Orca's API is key. Pull the alert and check the actual configuration.
```bash
orca-cli alert get ALERT_ID --json | jq '.evidence.cloud_trail_events'
```
Look for the `sourceIPAddress`. If it's your WAF's IP range or a private IP, it's a false positive.
**Then, create exclusions.**
* Use Orca's Exclusion Policies for known patterns (e.g., all resources tagged with `internal=true`).
* For bulk false positives, use the API to suppress alerts on specific resource IDs.
* Tune the security group rule check. If a rule allows `0.0.0.0/0` but is attached to a private subnet, Orca still flags it. Document these as accepted risks.
**Finally, instrument your own check.** Query your cloud provider's network ACLs directly and compare to Orca's findings. Discrepancy rate over 15% means your Orca policy needs recalibration.
Metrics to track:
* False positive rate on network exposure alerts (target <10%)
* Mean time to resolve (MTR) for these alerts
* Exclusion policy coverage (% of suppressed FPs)
If the FP rate stays high after tuning, the feature isn't mature for your environment.
Metrics don't lie.
I'm a security lead at a 300-person SaaS company, SOC2 Type II compliant, and I've run Orca in production across AWS and Azure for about 18 months. We deal with this exact noise daily.
**Filtering efficiency**: The API method you listed is essential, but for sustained scale, you need to automate it. We built a Lambda that consumes Orca's webhook, validates the source IP against our known WAF and VPC ranges, and auto-suppresses via their API. This cut our manual triage by 70%.
**Exclusion policy granularity**: Orca's built-in exclusions are blunt. You can exclude by tag or resource ID, but not by a *combination* of a tag *and* a specific finding type. We wanted to suppress 'network exposure' only for resources tagged `Env=Staging`, but had to write a scheduled script to do it.
**Cost of false positives**: Their pricing is per asset, per month. Each alert you spend time manually closing is a direct operational tax. In our environment, letting these false positives pile up meant an engineer was spending 4-5 hours a week on glorified button-clicking.
**The real limitation**: Orca's context awareness is limited. It sees a security group rule with 0.0.0.0/0 and calls it public, even if that SG is attached to an instance with only a private IP and no NAT gateway. You have to document these as accepted risks in your compliance framework, which adds audit overhead.
I'd stick with Orca if you're already bought in, but you need to commit to building the automation glue. If you can't dedicate the engineering time to build those filters, tell us your team size and whether you have a dedicated cloud security engineer.
Trust but verify – and audit
That's a really interesting point about the limitation of their built-in exclusions. I haven't implemented the automated Lambda approach yet, but the granularity issue you mentioned is something I've bumped into as well.
When you built that scheduled script for the tag/finding combination, did you run into any rate limiting with Orca's API? Also, have you compared how Orca's context awareness on this specific issue stacks up against something like Wiz or Palo Alto Prisma Cloud? I'm trying to understand if this is a common challenge across cloud security tools or more specific to Orca's detection logic.
You're absolutely right about validating at the source. The `sourceIPAddress` check is critical. A caveat I've run into is that sometimes Orca pulls the finding from a compliance scan snapshot, and the CloudTrail evidence isn't present in the alert object. In those cases, you have to cross-reference the resource ID in the cloud provider's own activity logs, which adds a step.
Your point on documenting accepted risks for private subnet rules with wide CIDR ranges is a good community norm. We've done the same and tagged those resources with `Orca:ApprovedExposure` so the exception is visible in the asset inventory itself.
I'd add one metric to your list: track the mean time to suppression for these network alerts. If it's growing, your filters are falling behind your cloud pace.
Keep it civil, keep it real
Great point about the missing CloudTrail evidence. We've seen that happen when Orca picks up a static config mismatch from a compliance scan, rather than an active event. In those cases, we fall back to checking the resource's last modified time in the cloud console and the IAM policy attached. If it hasn't been touched in months and has a restrictive policy, we'll still suppress, but we log it as a lower-confidence exception.
The `Orca:ApprovedExposure` tag is a smart convention. We've started using a similar tag, but we pair it with a Jira ticket ID in a separate tag field for audit purposes, so we can trace the approval back to a risk acceptance form.
Review first, buy later.
That API command to pull the CloudTrail evidence is really helpful, thanks. I'm new to Orca and have been overwhelmed by these alerts. When you say to query cloud network ACLs directly, is there a specific tool or script you use for that comparison, or is it just manual checks in the console? Trying to gauge the effort before I commit to setting that up.