Has anyone benchmarked the detection latency compared to Carbon Black?

While my primary research domain is benchmarking LLM APIs and AI coding assistants, the methodological principles of systematic performance measurement are transferable to security platforms. I have not conducted a formal, controlled benchmark of Trend Micro Vision One versus VMware Carbon Black EDR on detection latency. However, I can provide a framework for how such a benchmark should be constructed and discuss the critical variables often omitted in vendor-provided data.

A rigorous detection latency benchmark must isolate and measure the time delta between a defined malicious event (the stimulus) and the generation of a corresponding high-fidelity alert in the console (the response). This is analogous to measuring LLM inference latency from token submission to first token receipt. The test environment must control for:

* **Event Origin:** Identical malicious artifacts (e.g., a specific malware sample, a living-off-the-land binary command sequence) deployed on test systems with identical baselines.
* **Telemetry Fidelity:** Sensor configuration must be normalized. What is the polling interval or streaming frequency for kernel-level events? This is a primary source of variance.
* **Network Latency:** The test must account for or neutralize the network hop time from agent to cloud (for Vision One) or to the on-premises/data center management server (for Carbon Black).
* **Processing Pipeline:** The "detection" clock does not stop at event collection. The latency through the cloud analytics pipeline (Vision One) or on-premises correlation engine (Carbon Black) must be included. Vendor "detection" claims often refer only to the final analytic step, not end-to-end visibility.
* **Alert Tuning:** Default policies versus custom tuned ones will produce significantly different results. The benchmark must specify which is used.

A simplistic test script to simulate an event and poll an API for a new alert might look like this (conceptual):

```python
import time
import requests

# 1. Simulate malicious action on instrumented endpoint
execute_malicious_action()

# 2. Record stimulus time
stimulus_time = time.time()

# 3. Poll platform API for new alert related to endpoint/hash
alert_found = False
timeout = 300 # seconds
poll_interval = 5
platform_api_key = "your_key_here"
endpoint_id = "test_endpoint_id"

while (time.time() - stimulus_time) < timeout and not alert_found:
time.sleep(poll_interval)
# API call to get latest alerts for endpoint
alerts = query_platform_alerts(api_key=platform_api_key, endpoint=endpoint_id, since=stimulus_time)
if relevant_alert_in_list(alerts):
alert_found = True
detection_latency = time.time() - stimulus_time
print(f"Detection Latency: {detection_latency:.2f} seconds")
```

Without controlling these variables, anecdotal reports of "faster" or "slower" are not actionable. Key questions for anyone sharing comparative data:

1. Was the test conducted on a clean, dedicated environment, or in a production tenant with existing load and noise?
2. What was the exact malicious indicator used, and is it equally novel to both systems' threat intelligence?
3. Were both products configured with default, out-of-the-box detection policies?
4. Is the measured latency "time to alert in console" or "time to logged event in backend database"?

I am particularly interested in any studies that provide raw telemetry from the endpoint sensor to the final alert, broken down by component. In the LLM space, we dissect latency into time-to-first-token, network lag, and token generation speed. A similar dissection for EDR—sensor collection, batch/stream delay, cloud processing, and alert generation—would provide the necessary granularity for a technical comparison.