Authorization concepts decide what a user may do. Sage AI learns what each user and system actually does, and raises the activity that breaks the pattern — with the reason attached.
The audit trail itself is the training data: what each user runs, in which system, when and how much.
Every new event is weighed against the history of that user, their peers and that system.
Unusual activity raises a risk score on the user, so several small oddities add up.
Each anomaly states what was expected, what happened and why it matters.
Activity at hours when this user has never been active.
A logon from a terminal or an RFC call from a system new to this user.
Many times more records displayed, changed or exported than the user's own norm.
Transaction codes this user and their team have never started.
Authorizations granted or used in a way that does not match past administration.
Individually ordinary steps that together look like preparation for something else.
Each one is written to be read by the person who has to decide what to do next.
An administrator assigned a broad profile to a dialog user in production. They have never done so before and have no night-time activity in their history.
A vendor account was changed by a user outside accounts payable, shortly before the payment run, and restored the next morning.
The user normally displays a handful of records a day. Today they displayed thousands, from tables their team has never opened.
Keeping records satisfies half of most requirements. The other half is showing that someone, or something, is watching them.
Baselines adapt as roles change, where static thresholds have to be rewritten.
Every anomaly keeps its explanation and its outcome, which is itself audit evidence.
Confirmed anomalies can be forwarded to your SIEM or case management for investigation and response.
The first baselines form within days of connecting a system.
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