Alright, I’ve been living in the Orca console for the past three months trying to get our serverless sprawl under control. Overall, it’s a massive step up from trying to wrangle this manually, but—and there’s always a but—there are some… *interesting* quirks when it comes to Lambda.
The promise is solid: automatic, deep scanning of your Lambda functions without needing an agent. Orca pulls the layers, the runtime, the package dependencies, the function code itself, and even checks IAM roles and cloud resource risks. That’s the theory. In practice, here’s what I’ve stumbled over:
* **The "Bill of Materials" can be deceivingly noisy.** If your function uses a base layer (like AWS’s standard Python 3.11), Orca will flag every single CVE in *that* entire OS package universe, even if the vulnerable package isn’t actually executable in the Lambda context. You end up with a list of 50+ "critical" CVEs that are, frankly, irrelevant. It forces you to become an expert in filtering.
* **Cold start scans mean gaps.** Orca needs your function to be invoked to get a fresh snapshot. If you have low-traffic or scheduled Lambdas, the findings can get stale. We had a case where we updated a dependency to patch something, but the old, vulnerable version kept showing up for 48 hours until the function naturally triggered again. Not ideal for compliance dashboards.
* **IAM role analysis is surface-level.** It'll point out overly permissive policies (which is good!), but the real devil is in the conditional statements and resource constraints. Orca will shout about `"Action": "s3:*"` but it won't catch the more subtle, dangerous combinations that could lead to privilege escalation within your account. You still need a dedicated IAM tool for deep policy analysis.
* **The "Fix" suggestions are… optimistic.** For a vulnerable library in your function package, it might suggest updating to the latest version. Sounds good, but if that latest version breaks your function's compatibility? Tough luck. The advice is generic, not contextual to your actual code and dependencies.
My biggest gripe? The **segregation of findings**. Vulnerabilities in the runtime/layer are in one bucket, your function code dependencies in another, and the cloud misconfigurations (like the IAM stuff) are elsewhere. There’s no single, unified "Risk Story" for a *specific Lambda function*. You have to manually correlate data from three different views to understand the total exposure of `my-process-payments` function. It’s a workflow tax.
So, my question for the room: Is this just my setup, or are you all seeing the same? More importantly, have you found any clever workarounds—maybe via tags, custom policies, or using the API—to stitch this picture together and reduce the noise?
chloe
Demos are just theater. Show me the real workflow.
Totally agree on the noise from base layers. We had the same headache, especially with the AWS-provided runtimes. The trick that saved us was creating a custom ignore rule for the entire `amazonlinux` package source in the BOM settings. It cut our critical findings by 80% overnight.
On the cold start point, that's a real one. For our scheduled jobs, we set up a CloudWatch Event to trigger a no-op invocation right after deployment, just to force a fresh scan. Adds a tiny bit of orchestration, but it keeps the dashboard honest.
Keep automating!
Oh, the cold start scan gap is real. We ran into that with our monthly report functions - Orca's snapshot was weeks old. We ended up tagging those low-traffic functions and setting a custom scan schedule for them in the Orca policy.
But honestly, the BOM noise is my bigger headache. Even ignoring the base OS layer, we still get flooded from transitive dependencies in layers. It feels like we're constantly tuning the filters instead of reviewing actual risks.
Data-driven decisions.
Exactly. That "deep scanning" claim starts to wobble when you realize the context problem. Flagging every CVE in the base OS layer isn't a feature, it's a vendor failing to model the actual runtime environment.
The real gotcha? You're now paying them to educate their engine. All that time spent building custom ignore rules and understanding Lambda's stripped-down execution environment is work Orca should have done before selling this as a serverless solution. Makes you wonder what the premium is for.
Trust but verify.