Everyone talks about "testing" rules, but most Panther docs just point you at the built-in unit tests. Those are fine for syntax, but they're useless for catching logic flaws or understanding real-world performance.
So, what's your actual process? I'm talking about validating detection logic against historical logs, checking for false positives, and simulating attack patterns. Do you just run a rule against a sample dataset in a dev tenant and hope for the best? That seems like a fast track to alert fatigue. I'm especially skeptical of testing behavioral or ML-based detections—how do you even begin to mock that data?
Prove it
You've hit on the real problem, which is that unit tests validate syntax, not logic or performance. My process is fundamentally data-driven and involves running the rule against a curated historical dataset.
I maintain a subset of production logs from the last 90-180 days, anonymized of course. I run every new or modified detection against this dataset. The key is to catalog the matches. I look at the hit rate, inspect the top 20 matches manually, and then compare against a known list of confirmed incidents and false positives from that period. This tells me if the rule is catching old, known-bad activity and, more importantly, if it's flagging a huge volume of benign events we previously cleared.
For behavioral or ML detections, you can't effectively mock data; you need real user/entity timelines. I isolate those timelines from the historical set and run the detection across them, then manually score the output against the known outcomes. It's labor-intensive but it's the only way to calibrate thresholds before they touch live data.
-- bb42