Detection Is Only as Fast as the Data Underneath It
Identity Threat Detection and Response (ITDR) tools watch for identity-based attacks in progress — credential theft, privilege escalation, anomalous access patterns — and trigger automated or analyst-driven response. That detection logic depends on knowing what normal looks like for a given identity, which depends on having accurate, current data about that identity in the first place.
The blind spot shows up exactly where identity data is thinnest: accounts an ITDR tool never had a baseline for, because they were never discovered as part of its monitored inventory. A local admin account, an orphaned service account, or an AI agent session that begins, touches multiple systems, and completes before the next data refresh — none of these generate the historical signal ITDR needs to recognize when something's wrong.
An IVIP closes that gap by continuously discovering and correlating identities as they change, so ITDR has a complete, current picture to detect anomalies against — instead of a fast response built on an incomplete view.