|Primary Use Case
|Collect and analyze log data from various security systems and devices for correlation.
|Combine endpoint, network and cloud data to detect, investigate and respond to threats.
|Primarily focuses on log data from security devices, applications, and infrastructure.
|Collects data from endpoints, networks, cloud environments and other security solutions, along with infrastructure and applications.
|Often lack context needed to detect patterns and understand threat severity, impeding security team’s ability to prioritize investigation and response.
|A blend of threat detection techniques, including signatures, threat intel, behavioral analysis and machine learning/AI-powered analytics for superior incident correlation and advanced threat detection.
|Security teams manually investigate incidents by correlating events from multiple sources.
|Provides a unified analyst-centric platform, with rich contextual data and investigative and analytical tools, to investigate incidents, correlate data and respond to threats.
|Limited automation capabilities; relies on manual response actions or third-party integrations.
|Automated response actions, such as isolating compromised devices, based on detected threats.
|May struggle with high volume and variety of log data, leading to excessive noise and false positives.
|Designed to handle massive volumes of data in a hyper-scalable, cloud-based data lake, with nothing to deploy.
|Limited to the logs and data collected from SIEM-connected sources, resulting in blind spots that limit ability to detect threats.
|Comprehensive visibility into endpoints, including IT, OT, IoT and IoMT, along with networks and cloud environments.
|Typically integrates with a wide range of security tools to collect and correlate log data.
|Integrates with other security solutions for improved threat detection, response and visibility.
Every next-gen SIEM/XDR is different. Forescout XDR combines vendor- and EDR-agnostic support for 180+ data sources with predictable, endpoint-based pricing; automated data normalization and enrichment; 1,500+ verified rules and models; and a two-stage threat detection engine to weed out false positives and identify true threats.