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CrowsNest uses data payload inspection and active machine learning

to identify, analyze, and track files as they are used across the network.

CrowsNest catalogs data content and structures

without modifying files in any way, enabling it to determine normal data patterns.

A rolling baseline of normal data patterns

enables instant identification of anomalies—without manually generated policies or historical analysis.

Patented techniques isolate known threats

such as malware, botnets, Bitcoin, back doors, and command-and-control software.

Suspicious data behavior triggers an alert.

Contextual analysis identifies possible connections among suspicious behavior clusters to “connect the dots.”

The result?

Real-time data forensics, analytics, and a data chain of custody for accelerated response.