An unusual authentication event fires in the IdP. An endpoint behavior flag surfaces in the EDR. A SaaS notification lands in an inbox that nobody is monitoring. Three tools, three alerts. No tool connects them. The attacker moved across all three surfaces in sequence, and a stack full of best-in-class detectors still missed it because the attack is the sequence, and no single-surface tool was built to see that.
One Frame of the Film
An IdP sees a login, an EDR sees an endpoint, and a SaaS security tool sees a permission change. Each is accurate, and each is one frame of a film that plays out across multiple surfaces.
Single-surface vendors invest heavily in their frame: better anomaly detection on authentication, tighter behavioral baselines on endpoints, sharper rules on SaaS activity. The investment is real, but the limitation is architectural. A modern identity attack doesn't announce itself on a single plane. It routes through an inbox, escalates through an authentication event, and lands in a SaaS app minutes later. No single-surface tool sees the full sequence.
Why the Model Has to Be Continuous
Detecting identity attacks requires holding email, IdP activity, and SaaS behavior in a single continuous behavioral model, where each signal informs the others. Abnormal ingests every email in an organization and builds a behavioral baseline for every identity through PeopleBase.
When an anomalous authentication event follows an unusual email pattern, and a SaaS permission change appears minutes later, those three signals connect into a single correlated finding that the individual tools, operating independently, would each have dismissed as ambiguous.
Single-surface vendors are not a step behind. They are architecturally excluded from the signal that matters.
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