Okay, I'm going to say it. I've been using Delinea's Privilege Manager for about eight months now, and I've come to dread the risk analysis scores in our reports. They feel less like actionable intelligence and more like... well, just noise.
I get the *idea* in theory. Quantifying risk helps prioritize. But in practice? The scores seem to fluctuate based on factors that don't actually change our real-world risk posture. We'll get a "high risk" flag because a server hasn't had its password rotated in 45 days—even though it's in a tightly controlled segment with no external access and that's within our policy. Meanwhile, a contractor account with overly broad, active entitlements might get a "medium" score because it's brand new. It completely misses the intent and context.
This creates two big problems for user adoption and actual security:
1. **Alert Fatigue:** My team starts ignoring the scores because they cry wolf too often. The truly critical stuff gets lost in the shuffle.
2. **False Confidence:** Leadership sees a dashboard full of "medium" and thinks we're okay, when we might have a critical, simple misconfiguration that the algorithm undervalues.
I've had more success just running simple reports on:
* Accounts with privileged group membership
* Sessions exceeding X hours
* Password age *in combination with* the sensitivity of the system
Am I the only one who feels this way? I'd love to hear how others are using (or ignoring) these scores. Maybe there's a configuration tweak I'm missing, but right now, they're more of a distraction than a help.
Happy evaluating!
Yeah, I see this a lot with project risk scores too. The numbers feel arbitrary without human context.
> misses the intent and context
That's exactly it. A raw score can't capture why a risk is acceptable in one situation but critical in another. Have you been able to adjust the weighting of factors in Delinea, or are you stuck with their defaults?
Your example about the server password rotation hits home. We had a similar issue with a PostgreSQL service account scoring "critical" for age, while a Redis instance with a weak, shared password in a staging environment was "low." The algorithm weighed the *metric*, not the *exposure*.
This often boils down to the data model behind the scoring engine. If it's just ticking compliance boxes (password age, MFA enabled) without modeling the actual attack path, it's noise. You need to weight the score by the asset's context - network segmentation, sensitivity of data, and *current* activity, not just static attributes.
Have you tried creating custom risk indicators in their system? Sometimes you can build a more useful score by combining their raw events with your own context, treating their default score as just one input among many.
sub-100ms or bust
You've zeroed in on the core problem: the data model. Treating static attributes as risk factors without considering their position in a graph of potential access is a fundamental modeling error. A service account password age is just a date field; the actual risk is a function of that node's connections to sensitive data and internet-facing systems.
Building a custom composite score by joining their raw event log to an external CMDB or network topology dataset can help. I've done this by piping logs into Snowflake and scoring with a simple SQL view that applies context-aware weights. The vendor's score becomes a single column in a much wider fact table.
The real challenge is maintaining that external context. Their system likely won't natively consume it, so you're building a separate pipeline. That duplication often reveals why the out-of-the-box scores feel so disconnected.
Garbage in, garbage out.