Identity behavior models
A learned model of how every machine identity behaves.
Automation is far more regular than people. Ordrani’s behavior models learn that regularity for each machine identity, so that when an authorized identity starts acting differently, the change is visible, specific and explainable.
Overview
Behavior is the signal that permissions cannot give you.
Permissions describe what an identity is allowed to do. They say nothing about what it normally does. A deployment role may hold the right to read every secret in an account and never once use it — until the day its credentials are used by someone else.
Ordrani maintains a behavior model for every monitored machine identity. Each model is learned from that identity’s own history: when it runs, which APIs it calls and in what order, which resources it touches, which permissions it actually exercises and which identities it works alongside.
Because a pipeline or service account tends to repeat the same work on the same schedule, departures from that routine carry real information. The difficulty is precision — telling a misused credential apart from a legitimate release. The models are designed around that distinction.
What it produces
- A behavioral profile for every monitored machine identity
- Deviation signals naming the dimension, magnitude and baseline involved
- Permission utilization: what is granted versus what is exercised
- Baselines that adjust for approved releases and planned change
- Inputs to correlation and investigation, never isolated alerts
Baseline and deviation
One identity, observed over time.
How a learned baseline makes a departure from routine stand out.
Identity behavior model
prod-deploy-role · distinct resources accessed per session
How the models work
From raw activity to a precise deviation.
01
Profile
Build a profile for each identity from its own activity, kept separate from every other identity’s, so that one noisy service cannot blur another’s baseline.
02
Learn
Learn normal ranges and patterns across each behavioral dimension, weighted by how stable that identity has historically been.
03
Adjust
Absorb approved change — releases, infrastructure updates and maintenance windows — and re-learn after verified change instead of reporting it indefinitely.
04
Explain
Measure new activity against the model and express any deviation in terms an analyst can read: which dimension, how far, compared with what.
Behavioral dimensions
The dimensions each model learns.
A single deviation is weak evidence. Several at once, on an identity that is normally precise, is strong evidence. Where telemetry allows, request and transfer volumes are modeled as well.
01
Execution timing
02
API usage
03
Resource scope
04
Permission utilization
05
Identity relationships
06
Execution context
Continue exploring
Related reading.
Platform
Workflow provenance
Judge behavior against the purpose of the workflow that produced it, not only against history.
Platform
Investigation
How deviations are correlated into a single, evidence-grounded case.
Technology
Behavioral modeling
The layered detection stack behind the behavior models, in technical detail.
See how behavior models apply to your identities.
We are glad to walk through how Ordrani would model the service accounts, cloud roles and pipelines in your environment.