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ORDRANISecurity

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

010203040learned baselineapproved release v2.14baseline re-learned33 resources · run 41822.3× above upper boundsession 1session 5session 10latest

How the models work

From raw activity to a precise deviation.

  1. 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.

  2. 02

    Learn

    Learn normal ranges and patterns across each behavioral dimension, weighted by how stable that identity has historically been.

  3. 03

    Adjust

    Absorb approved change — releases, infrastructure updates and maintenance windows — and re-learn after verified change instead of reporting it indefinitely.

  4. 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

Schedules, cadence, burst patterns and the windows in which an identity is normally active.

02

API usage

Which operations are invoked, in what proportion, and in what order within a session.

03

Resource scope

The services, accounts, regions and individual resources an identity regularly touches.

04

Permission utilization

The subset of granted permissions actually exercised — and the dormant remainder.

05

Identity relationships

Roles assumed, roles chained and identities that habitually operate together.

06

Execution context

Repository, branch, environment, workflow and runner associated with activity.

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.