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White Paper

Inside the data science of OpenText Core Threat Detection and Response

Inside the data science of OpenText Core Threat Detection and Response

Inside the data science of OpenText Core Threat Detection and Response

OpenText
Pages 17 Pages

This paper explains the machine-learning and behavioral-analytics models behind Core Threat Detection & Response. Page 1 outlines the challenge of detecting subtle attacker behaviors across large, noisy telemetry streams. The paper details anomaly detection, adversary-signal modeling, supervised learning, and feedback loops using MITRE ATT&CK. It explains correlation methods across endpoints, networks, and identities. Visuals show scoring models, clustering, and signal-to-noise reduction workflows. The system prioritizes high-value alerts, accelerates investigations, and improves accuracy over traditional SIEM rule-based systems.

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