Hey folks, looking for some real-world data points here. Our DevOps team (around 50 engineers) is fully on AWS, managing a mix of EC2, ECS, and Kubernetes workloads. We're evaluating next-gen endpoint protection and have narrowed it down to VMware Carbon Black and CrowdStrike Falcon.
Our main criteria are:
* **Agent footprint & performance impact** – Can't have scans tanking our CI/CD pipeline runners.
* **AWS-native integration** – How well does it play with CloudTrail, GuardDuty, or S3 for log shipping?
* **API-first approach** – We need to automate everything, from deployment to policy exceptions.
* **Threat hunting for containers** – Runtime protection for ECS tasks and K8s pods is a big plus.
I've done some initial querying of their docs, but I'm curious about the day-to-day operational experience.
For those who've implemented either in a similar cloud-native environment:
* How painful was the initial agent rollout? Any gotchas with auto-scaling groups or immutable infrastructure?
* What's the actual query language like for hunting? I've seen Carbon Black's `process_name:cmd.exe`-style syntax. Is it flexible enough?
* Do you find yourself constantly tuning exclusions, and how granular can you get?
We're leaning towards a solution that gives us strong detection but also clean, actionable data we can pipe into our own SIEM. The cost models are also... interesting to decipher.
Any insights, especially around managing false positives in a dev-heavy environment, would be super helpful. Bonus points if you've built any cool automation with their APIs using Python or Terraform.
--diver
Data is the new oil - but it's usually crude.
For agent rollout, our experience with Falcon on auto-scaling groups was straightforward because the agent install was baked into the AMI via Packer. The main gotcha was ensuring the sensor pulled its initial config from a pre-provisioned S3 bucket, not from a local file, to maintain immutability. If you bake a config file into the image, you lose the ability to push policy changes without a new bake cycle.
On tuning exclusions, yes, you will be tuning constantly initially, especially for CI/CD runners. We ended up creating separate policy groups for build hosts with heavier path-based exclusions for our toolchain directories. The API for managing these exclusions is solid, but I'd budget a two-week stabilization period for fine-tuning.
Regarding container runtime, CrowdStrike's Falcon Container was an add-on module for us. It did add cost, but the runtime visibility into pod activity was valuable. It integrates at the node level, not per-pod, which kept the overhead manageable.
Less spend, more headroom.