Just rolled out Vision One and the User Risk Score is driving my team nuts. We see a user flagged as "High Risk" for one event, like a single failed login from a new city, while another user with a pattern of weird after-hours data access stays "Low." The logic feels opaque.
Has anyone cracked the code on what actually moves the needle? I'm looking for:
* Real-world triggers you've seen that cause big score jumps.
* Any way to weight the factors that matter to *us*?
* Is it just noisy until it has months of baseline data?
Trying to build automation off this, but it's hard to trust when it feels so random.
Automate the boring stuff.
Yeah, we saw that too. A single event spiking the score feels overblown. For us, a big jump came from a user downloading a ton of files right before a known vacation - the system didn't factor in the approved out-of-office.
Does the baseline period thing actually help? I'm wondering if we just need to wait it out for the noise to settle down.
>downloading a ton of files right before a known vacation
Had that exact scenario. The baseline does eventually smooth things out, but you have to feed it context. For us, the "noise" didn't go away until we integrated our HR calendar for terminations and PTO.
Without that data layer, you're just waiting for the system to learn what's normal for each user, which takes forever. Even then, it's still just behavioral, not logical.
—cp
Oh, the baseline period helps, but maybe not how you'd think. In our setup, it took a solid 6-8 weeks for the really obvious false positives (like the vacation download spike) to calm down. But it didn't magically become perfect.
The catch is, the baseline just learns *that user's* normal rhythm. If Bob always downloads huge files on Friday afternoons, it'll eventually accept that. But it can't know *why* he does it. That's where you still need to feed it context, like user1005 mentioned with the HR calendar. Without that extra data layer, you're just waiting for it to stop flagging predictable anomalies, not understanding intent.
K8s enthusiast
That baseline learning period is exactly what makes it hard to build automation on top of. You're stuck in this limbo for two months where you can't really trust the scores.
We tried to bypass it by feeding it our VPN gateway logs and corporate IP ranges first, so it at least knew what a "normal" login location was from day one. It helped a bit with the geographic false positives, but you're right, it didn't touch the "why." The system just learned that Bob always downloads from the corporate network on Fridays, not that it's for the weekly sales report.
It feels like a tuning problem. You have to decide if you want a sensitive system that flags everything early (and requires manual review) or a calm one that's useless for the first quarter.
terraform and chill
It helps, but only on a per-user basis. It's learning patterns, not policy.
Your example with the approved vacation is the core issue. The baseline will eventually learn that Alice downloads files before she's inactive for two weeks every August. But it won't know it's *approved* PTO, just that it's a pattern. You still get that initial high-risk alert every single time.
The baseline doesn't fix arbitrariness, it just codifies an individual's personal arbitrariness into "normal."
Five nines? Prove it.
You're hitting on the core limitation of any out-of-the-box risk engine: it uses generic heuristics, not your business logic. The single failed login from a new city is a perfect example of a high-fidelity, low-context signal that these systems overweight because they can't assess *likelihood*, only *anomaly*.
To answer your questions directly:
- The biggest score jumps we see are from concurrent logins from geographically impossible locations and first-time access to data classifications marked as sensitive. The after-hours access pattern likely hasn't crossed a *rate* threshold the system cares about, which is why it stays low.
- You can weight factors, but not within Vision One's native scoring. You have to pull the raw signals via API into a separate system (we use a small Python middleware) and apply your own weighting logic before feeding a *derived* score back into your workflows.
- The baseline data reduces noise for personal quirks, but it doesn't address the arbitrariness of the underlying rules. A six-month baseline won't make that single failed login from a new city less of an event, it'll just make the user's score decay back to normal faster.
Building automation directly on the native score is a trap. You have to treat it as just one of several inputs.
IntegrationWizard
Exactly. Pulling the raw signals via API is the only way to make it useful. We do the same, sinking events into ClickHouse.
The key isn't just weighting, it's joining. We cross-reference the login signals with our data warehouse to check if the user's team had a project deadline that week, or if the "sensitive" data they accessed was actually part of their new assigned dataset.
The score becomes arbitrary when it's just a stream of isolated events. It stops feeling random when you join it against business context.
Numbers don't lie.