Alright, so I've been benchmarking AI code assistants for months, but now my team has me running a different kind of benchmark: measuring the performance impact of various EDR agents on our developer workstations and CI/CD servers. The results are... chaotic.
We built a simple test harness that simulates common dev workloads (file I/O bursts, process creation, network calls) and measures CPU, memory, and disk latency overhead. We're testing in a controlled lab environment, but the variance between runs—and between vendors—is huge. Sometimes Vendor A adds a 2% CPU overhead, sometimes it's 8% on the same workload. Vendor B might be light on idle but introduces significant latency during compilation.
Here's a simplified snippet of our timing logic for file operations:
```python
import time
import pathlib
def file_ops_overhead(base_path, iterations=1000):
times = []
for i in range(iterations):
start = time.perf_counter()
test_file = base_path / f"test_{i}.tmp"
test_file.touch()
test_file.write_bytes(b"dummy data")
test_file.unlink()
end = time.perf_counter()
times.append(end - start)
return sum(times) / iterations
```
My questions for the detection engineering crowd:
* Is there a *definitive*, industry-accepted benchmark for EDR performance impact, or is it all vendor-specific marketing? I've seen the MITRE evaluations for detection, but less for pure overhead.
* What's your methodology for performance testing in production? Do you just trust vendor specs, or do you have a standard set of tools/processes?
* How much variance do you consider acceptable for, say, a developer machine vs. a critical server?
I'm used to LLM benchmarks having their flaws, but at least there's a common frame of reference (like HumanEval). With EDR, I feel like we're comparing apples to oranges to mysterious fruit no one has defined yet. Any insight would be awesome.
--experiment
Prompt engineering is the new debugging.
Yeah, this is a famously tricky area. That variance you're seeing is why so many vendors can claim to be "the lightest" - they're all picking the benchmark that makes them look best.
Your harness is a good start, but EDR impact is incredibly workload and timing dependent. They often scan in batches or have learning periods, so a burst of file ops might get hit harder on the first pass vs. the tenth. Idle vs. busy system state matters a ton for CPU measurements too.
You might want to incorporate some "steady state" calibration runs before each timed test to try and account for that. Also, are you testing with their default policies or fully tuned ones? The difference between default and a policy tailored to a dev environment can be night and day for performance.
Keep it real
Honest question - are your CI/CD servers also running on k8s? If they are, you could isolate the EDR to a dedicated sidecar or a specific node pool. That way your benchmarks might reflect the actual tax on your workloads instead of the whole host.
I've seen similar variance when I was testing service mesh sidecars. The overhead depended entirely on whether the traffic pattern hit a hot or cold path in the proxy cache. Could be similar with EDR's scanning engine.
Your python snippet is a good start, but you're measuring *throughput*, not *latency* for a single file op. The EDR's real bite is usually on that first file access, not the average of a thousand. Try capturing the p90 or p99 of each individual operation, not just the mean. That's where my team saw the real compile-time slowdowns.
We ended up using eBPF to measure the actual syscall delays injected by the security layer. That gave us a clearer picture than just overall CPU%. Might be overkill for your case though.
yaml all the things
The sidecar idea is a decent workaround for k8s, but it feels like an architectural capitulation to bad software. You're now maintaining extra pods and configurations because your security vendor can't write efficient code.
You're spot on about latency being the real killer, though. Measuring p99 of single operations is the only way to see the compile-time hit developers actually complain about. That "throughput" average they love to publish is borderline dishonest.
eBPF for syscall delays isn't overkill, it's the only way to get objective truth. Otherwise you're just trusting the vendor's own telemetry, which miraculously always shows minimal impact.
Just my 2 cents
Interesting angle on this - benchmarking EDRs feels like the exact opposite of benchmarking code assistants. With assistants, you're trying to measure capability. Here, you're trying to measure the absence of impact, which is way more subtle.
I'm curious about your setup. Are you testing on bare metal or VMs? My team tried this briefly and found hypervisor-level variance swamped the EDR signal until we moved to isolated physical hosts. Virtualization adds another layer of chaos.