So we're all on the same page, Exabeam's identity modeling is pretty decent for the steady-state FTE. It learns the rhythm of the 9-to-5 lifer after a few weeks. But what's the play when you've got a revolving door of contractors or consultants who are only around for 30, 60, maybe 90 days?
Their entire tenure is basically the "learning period." By the time the model has a faint idea of their normal behavior, they're already packing up. This seems to create a lot of noise—either the system never really flags anything for them (making them a potential blind spot) or it throws low-fidelity alerts because everything they do is "new."
Are you just excluding those accounts from behavioral analytics entirely and relying solely on rule-based detections for them? Or is there a trick to shortening the model training window without making it uselessly trigger-happy? I'm curious how others are tuning this, especially if you're in a sector that runs on a lot of short-term specialist roles.
Feels like a scenario where the product's core strength becomes a bit of a liability.
Data over dogma.
Yeah, that's a classic problem with these learning models. We ran into the same issue with a burst of cloud migration contractors last year.
Our stopgap was to segment them into a separate "short-tenure" group in the IAM system and apply a different detection profile. We still feed their logs to the model, but we heavily weight rule-based detections and peer group comparisons over their individual baseline. So instead of asking "is this normal for *Jane Contractor*?", we ask "is this normal for *any contractor* in this network segment working on the billing API?".
It cuts down the noise, but you're right, it feels like working around the tool. I've heard some teams manually seed a baseline using the contractor's stated role and project, but that's a lot of admin overhead.
Latency is the enemy, but consistency is the goal.