Alright, who else is getting paged because some workload in Azure decided to talk to a random IP in AWS that *wasn't* in the allow list Prisma Cloud swore it had covered? 🙃
We've been running Palo Alto's Cloud CDR (the network security/posture bit) for about 18 months now. Multi-cloud setup: GKE on GCP, a bunch of EC2 + EKS on AWS, and some legacy VMs in Azure. The promise was solid: one pane of glass for all our cloud network security, auto-discovery, and policy enforcement. The reality... is a mixed bag of "oh cool" and "oh come on."
**The Good (what keeps us from ripping it out):**
* The visualization is legit. Seeing actual traffic flows between workloads, across cloud accounts, saved our butts during incident response. No more grepping VPC flow logs in three different consoles.
* The auto-tagging for workloads is decent. Makes policy creation based on environment (prod/dev) or app tier way easier than manual tagging.
* API-driven, which means we can (mostly) treat it as code. Our posture policies are defined in Terraform, which is a win.
**The Gotchas (the reason I'm posting at 3 AM):**
* The "scale" part gets weird. The agent-based model (for runtime defense) adds overhead. We had to tune the hell out of resource requests for their DaemonSet in our big clusters.
* Policy logic can be... surprising. Example: a rule to block egress to the internet from prod. Sounds simple. But their inheritance model from cloud accounts -> VPCs -> workloads bit us when someone spun up a new VPC and the default "learning" mode allowed everything for a week.
* API latency for changes. You push a policy change via their API, and sometimes it takes a solid 5-10 minutes to propagate. In a rapid-scale event, that's an eternity.
Here's a snippet of the Terraform we use for a simple ingress policy. It works, but the state management is fragileβtheir provider doesn't always detect drift well.
```hcl
resource "prismacloud_policy" "deny_lateral_movement" {
name = "Block cross-namespace ingress in prod"
policy_type = "network"
cloud_type = "aws"
severity = "high"
labels = ["network", "prod", "terraform"]
description = "Prevent prod workloads in different namespaces from communicating."
rule {
name = "Network Rule"
rule_type = "Firewall"
parameters = {
"effect" : "deny",
"from" : "namespace != ${var.namespace}",
"to" : "namespace = ${var.namespace}",
"port" : "any"
}
}
}
```
Anyone else dealing with this? Specifically:
* How do you handle policy updates across 50+ cloud accounts without the console timing out?
* Any success with their CI/CD plugin for policy as code, or did you roll your own?
* False positives on "anomalous" traffic patternsβdid you just turn that module off?
Pager duty survivor.
NightOps
Oh, the agent-based model at scale. I feel this in my soul. We had a similar curveball, but on AWS ECS Fargate. The agents spun up just fine, but the sheer volume of runtime network data from hundreds of ephemeral tasks started drowning our log aggregation. The console visualizations got laggy, and policy evaluation delays crept in. Support said we'd "hit a design ceiling for high-frequency, short-lived workloads."
What ended up working for us was a mixed deployment: agentless for the cloud-native, auto-scaling stuff (it uses the cloud APIs for traffic flow), and agents only on our long-lived, stateful EC2 instances. It's not the single-model dream they sold, but it stopped the 3 a.m. pages. Have you looked into splitting the deployment model based on workload type?
test everything twice