Hi everyone! 👋 I've been lurking here for a bit, trying to learn as much as I can. I'm currently at a startup where our engineering team is growing fast and we're all-in on Kubernetes (EKS, if that matters). Right now, our cloud security is... let's call it "reactive" and mostly manual. Not great.
We're looking at a dedicated CSPM/CNAPP platform and have narrowed it down to Lacework and Orca Security based on some initial demos. Since we're a team of about 50 engineers, I'm really worried about picking something that will either create a ton of overhead or get ignored because it's too noisy.
Does anyone have direct experience with either tool in a similar environment? I'm especially curious about:
* How easy is it to integrate into a CI/CD pipeline (we use GitLab)? Do the findings feel actionable for developers, or are they just more alert fatigue?
* The Kubernetes-specific coverage. We've heard Orca's side-scanning is unique, but does it actually give you better visibility into misconfigurations *inside* the cluster compared to an agent-based approach?
* For those who've made a choice between these two, what was the deciding factor that you didn't see coming during the sales cycle?
Our main goals are to shift left without drowning in false positives and get a clear picture of our compliance posture (SOC 2 is a big focus). Any real-world stories or gotchas would be incredibly helpful!
Thx!
Having just gone through a similar evaluation at my own company, I can relate to your concern about alert fatigue. For the CI/CD integration, we found the way findings are categorized to be more critical than the integration mechanism itself. Both tools can push results into GitLab, but the taxonomy they use for severity and the context provided alongside a vulnerability made the real difference for developer adoption.
On your point about Kubernetes coverage and side-scanning, our team had a long debate on that. While the agentless model promises easier deployment, we observed that certain runtime behaviors and intra-cluster traffic patterns were only surfaceable with an agent. The side-scan is excellent for a compliance snapshot, but it can miss the chain of events leading to a misconfiguration.
What ultimately surprised us wasn't a feature, but the operational cost of tuning out false positives. The initial setup and customization period required more dedicated security engineering time than we'd budgeted for, regardless of the vendor. How are you planning to handle that initial curation phase - is there a dedicated platform team, or will that responsibility be distributed?
That's a good point about developers ignoring it if it's just more noise. In my last job, we trialed a tool where the alerts were so vague engineers would just mark them as false positives and move on. The categorization of the finding really needs to tie back to a specific fix in the IaC or Dockerfile, otherwise it's just another ticket.
On your second point, we also looked at agentless scanning. For compliance checks on cluster configs, it was fine. But it completely missed a case where a workload was pulling from an unapproved internal registry during runtime. An agent would have seen that network call. So maybe it depends on whether you need that runtime context or just a config baseline.
What did your sales reps say about tuning the alert rules before rollout? That was a big factor for us.
Hey, welcome! That "reactive and manual" phase is a tough spot to be in, but it's great you're looking at these tools early.
We went with Lacework about a year ago, also on EKS with a team your size. The deciding factor for us was actually the data presentation for engineers. Orca's side-scanning gave us a fantastic compliance snapshot, but Lacework's Polygraph engine created a timeline of events that developers could actually understand. Instead of just "here's a high-severity alert," it was "this pod started, made this unexpected network call, which triggered this policy." That context cut down on the "ignore it as noise" problem you're worried about.
For your CI/CD question, both integrate, but actionable findings are key. Lacework's ability to show the exact line in a Dockerfile or the specific Terraform resource that caused the drift made it stick for our devs. The noise level is high initially, but their suppression rules based on resource tags let us quiet things down after the first few weeks.
Ship fast. Learn faster.
Totally agree that the event timeline is a game changer for dev buy-in. We saw the same thing.
Our team hit a snag with Lacework's early noise, though. The tagging-based suppression you mentioned worked for AWS resources, but it was trickier for our Kubernetes service accounts and some internal services. We ended up having to write a few custom policies to filter them out.
How long did it take your crew to get through that initial tuning phase? We were buried for a solid month before it felt manageable.
data over opinions
Right there with you on the worry about alert fatigue. For us, the CI/CD integration boiled down to whether a finding could link to a specific commit or merge request. Lacework's ability to flag the exact line in a Dockerfile or Helm chart was a huge win for developer trust, because the fix was right there. Orca's reporting felt more like a separate audit step we had to manually triage.
That timeline context user1473 mentioned is legit. It turns a scary "critical vulnerability" alert into a story your on-call engineer can actually follow, which stops them from just snoozing it.
I will say, the initial tuning phase was a slog. About three weeks of tweaking suppression rules in our GitOps workflows before the signal-to-noise ratio was palatable. Did your demos give you a feel for how customizable those alert policies are out of the gate?
git push and pray
Yeah, the initial tuning phase you mentioned is what I'm nervous about. We don't have a dedicated platform security team, so it's gonna fall on like three of us senior devops folks to handle that curation. That's a big time sink we didn't budget for.
> operational cost of tuning out false positives
This is the real hidden cost, isn't it? The demos always show the perfect, clean alerts. Did you find one vendor's taxonomy made that tuning phase *faster*, even if it was still painful? Or were they about the same?
You're right to focus on operational cost. In my experience, Lacework's taxonomy and the Polygraph data model made the initial tuning more systematic, but it wasn't *fast*. You're still looking at a few weeks of dedicated effort. The key was that once we tuned a suppression rule, it tended to stick because it was based on behavioral patterns, not just static tags.
So while both require that upfront investment, one factor you might not have seen coming is how each platform's data model either fights you or helps you during that phase. Lacework's timeline made it easier for us to understand *why* something was noisy, which sped up the decision-making on what to suppress. With Orca's excellent snapshot, we found we were often guessing at the root cause of a false positive, leading to more trial and error.
Architect first, buy later
That's an insightful observation about the data model being a key differentiator during the tuning phase. A related, and often overlooked, aspect is how this early investment affects your total cost of ownership over a typical three-year contract.
If the data model allows suppression rules to be more durable and based on behavior, you're not just saving time in month one. You're reducing the recurring operational burden of re-tuning as your environment evolves. The trial and error cycle you describe with a snapshot model can become a hidden, recurring cost that isn't captured in the initial PoC but shows up in quarterly platform reviews.
This makes the upfront weeks of effort less of a pure cost and more of an investment with a clear payback period, which changes the financial calculus.