Tuning a UEBA Deployment
Every deployment starts with unusable volume. Raising thresholds destroys detection along with noise; there is a better sequence.
Running a deployment
Tuning, alert volume, triage, feedback loops, drift and measurement. Review capacity is the binding constraint on every deployment, and most of this section is about respecting that.
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Alert Volume and Review Capacity
Review capacity is the binding constraint on every deployment. Sizing the queue to it decides whether anything is detected at all.
How to Investigate a Risk Score
An anomaly is a question, not an accusation. A repeatable order of investigation prevents wasted hours and wrong conclusions.
Feedback Loops That Improve Detection
Most deployments are static after month three. The mechanisms that let one improve are cheap and depend on capturing adjudications.
Measuring Quality Without Ground Truth
Precision is measurable. Recall is not. Knowing which claims your data supports prevents both false confidence and bad reporting.
Base Rates: The Arithmetic Nobody Shows
An accurate detector on a large population produces mostly false alerts. This is arithmetic, not a tuning problem you can fix.
Model Drift and Retraining
Environments change and models do not notice. Drift is continuous, silent, and detectable only if you instrument for it deliberately.
Running a UEBA Proof of Concept
Most evaluations are demonstrations on vendor data with a predetermined outcome. How to run one that is capable of failing.
Fitting UEBA Into an Existing SOC
Behavioural output does not fit the alert-and-close workflow a SOC runs. Fitting it badly makes a queue nobody ever touches.
Testing Detection With Red Teams
Without labelled data, an authorised adversarial exercise is the only ground truth available — and most organisations waste it.