OpenText Core Threat Detection and Response

The solution uses unsupervised behavioral analytics to baseline every user and device across Defender for Endpoint and Entra ID telemetry, then flags even slight drift. Peer-group context surfaces malicious, negligent, or compromised insiders early—before privilege abuse or data theft can unfold.

Patented ML pipelines build multi-dimensional behavioral baselines for every entity and update continuously. This adaptive AI uncovers zero-day TTPs and low-noise anomalies missed by signatures, rules, or SIEMs, delivering higher-fidelity detections with almost no tuning.

SaaS onboarding through native Microsoft APIs is agentless and fast. Point to your tenants, backfill 30 days of history, and actionable detections appear within hours. Full behavioral maturity lands after about two weeks, giving SOCs insight long before traditional rule tuning finishes.

Integrated risk scoring suppresses benign anomalies, clusters related indicators, and elevates only high-impact events. The result: up to 90 percent fewer false positives, drastically reduced alert fatigue, and analysts who can focus energy on genuine threats instead of drowning in noise.

Online learning refreshes baselines daily, automatically absorbing role changes, shift rotations, mergers, seasonal peaks, and travel patterns. Detection precision remains tight without rule rewrites, keeping insider-threat coverage accurate as the business and its workforce evolve over time.

Every alert is tagged with ATT&CK tactic, technique, and step; an LLM-generated narrative links precursor activity to follow-on actions. Analysts know their exact kill-chain position, prior context, and recommended next moves, shortening triage and speeding containment.