Hey everyone, I've been deep in the trenches lately trying to get a clear, apples-to-apples comparison between a few different Cloud-Native Application Protection Platform (CNAPP) vendors for our internal environment. The sales demos are always impressive, but I've learned the hard way that the real test is how they perform in your own unique, messy cloud setup.
The core challenge was creating a reproducible benchmark. We needed a lab environment that we could spin up, test against, tear down, and then spin up again identically for the next vendor. This is crucial for honest comparison, especially when evaluating things like IaC scan accuracy, runtime alerts, and posture management across AWS, Azure, and our K8s clusters.
Here's the general approach we took, focusing on automation and repeatability:
* **Infrastructure as Code is Non-Negotiable:** We defined everything in Terraform. This includes not just the "good" resources, but also a curated set of intentionally misconfigured or vulnerable resources that act as our benchmark "test cases."
* Example: A publicly accessible S3 bucket, an EC2 instance with a wide-open security group, a Kubernetes pod with privilege escalation allowed, and an Azure storage account with no encryption.
* **Simulating Workloads & Traffic:** We used some simple, custom scripts to generate benign API traffic and also to simulate suspicious activities (like outbound calls to known-bad IP ranges from a test container) to trigger runtime protection features.
* **Standardized Scoring Rubric:** We built a simple spreadsheet, but the magic was automating the data pull. We used each CNAPP's API (where available) to export findings into a common format. We scored them on:
* **Detection Accuracy:** Did it find all our planted misconfigurations? Any false positives on our "good" resources?
* **Context & Prioritization:** Did it just list a vulnerability, or did it effectively link the IaC misconfiguration to the runtime asset and show the potential attack path?
* **Remediation Workflow:** How easy was it to auto-generate a pull request to fix the Terraform code for a finding?
A simplified snippet of our Terraform "test bench" for AWS looks something like this:
```hcl
# Good, compliant resource
resource "aws_s3_bucket" "secure_logs" {
bucket = "my-secure-logs-bucket"
# ... proper encryption and block public access settings
}
# Planted misconfiguration - Public bucket
resource "aws_s3_bucket" "public_web_bucket" {
bucket = "test-public-web-${random_id.suffix.hex}"
# Intentionally omitted 'block_public_access' and encryption
}
# Security group test - overly permissive
resource "aws_security_group" "overly_permissive" {
name = "test_allow_all"
description = "Test: Ingress open to world"
vpc_id = aws_vpc.main.id
ingress {
from_port = 0
to_port = 65535
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
}
```
The real effort was in the glue code—using the vendors' APIs and webhooks to pipe findings into our scoring system. Some tools had fantastic APIs, others... not so much, requiring some creative workarounds with Make (formerly Integromat) to poll and export data.
This process has been incredibly enlightening, moving beyond marketing claims to concrete data. I'm curious if others have tackled similar evaluations. What did your test bench look like? How did you handle scoring the more qualitative aspects like alert fatigue or UI usability in a quantitative way?
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