We've been evaluating several AI SOC agents (Cortex XSIAM, Microsoft Sentinel with Copilot, Tines, etc.) for automated triage. A key metric for us is the time from alert ingestion to a proposed action or disposition. Vendor benchmarks often use synthetic data, which doesn't reflect our noisy, complex environment.
To get a real comparison, I built a script that replays a week's worth of our actual, anonymized alert backlog (from Microsoft Defender and CrowdStrike) through each vendor's API. It measures the latency for each agent's analysis and logs the consistency of the output. The goal was to see which agent could handle our volume with the lowest operational delay.
The script's core loop works like this:
* Loads the historical alert batch (JSON format).
* Iterates through each alert, sending it to the configured endpoints for each vendor agent.
* Starts a timer upon sending the HTTP POST request.
* Stops the timer upon receiving a valid JSON response containing a "summary" or "recommendation" field.
* Records the latency and a hash of the response for comparison.
Initial findings were surprising. One agent averaged under 2 seconds per alert but provided near-identical, templated responses for different alert types. Another took 8-10 seconds but delivered highly specific investigative steps. The fastest wasn't the most useful for our analysts.
I'm considering adding a scoring component that weights speed against the actionable quality of the output, perhaps using a simple rubric. Has anyone else attempted a similar real-world performance comparison? I'm particularly interested in how you controlled for variables like network latency to cloud-hosted agents versus on-premise solutions.
Love the approach of testing with real backlog data, that's where the rubber meets the road! 😄
>near-identical responses for vastly different alert types
That's a fascinating, and honestly worrying, find. Makes me think the agent might be leaning heavily on pre-canned templates instead of actual analysis. Did you also capture any metrics on the *quality* of those responses? Low latency is great, but if the output is generic, it just creates alert fatigue for the analyst.
Have you considered adding a step to measure the vendor's agent resource usage (like API rate limiting or compute cost) during your replay? That operational delay sometimes comes from throttling when you hit a certain volume, which might not show up in a single-alert test.
Dashboards or it didn't happen.
Your point about output quality versus speed is critical. I ran a similar test last quarter and found that high-speed, low-quality responses effectively shift the workload from triage to validation, negating any efficiency gain. We attempted to quantify quality by scoring the relevance and specificity of the agent's proposed action against our ground-truth analyst dispositions, but it's a messy, subjective process.
Adding resource usage metrics is a smart suggestion. In our case, the latency spikes weren't from throttling but from the agents' own internal batch processing - they'd queue alerts silently during high volume, then return fast-but-stale responses. You'd only see it if you measured end-to-end latency per alert and plotted it against the concurrent request count.
p-value < 0.05 or bust