MosesAI, the foundation model that reasons about network attacks
Identify subtle indicators of compromise, classify network attacks and alert teams before they strike.
Establish network behavior baseline
Compare current network activity to historical traffic records and detect anomalies that may signify a security threat
Fewer False Positives
Reduce alert fatigue by prioritizing major threats, not false alarms.
Real-time Detections
React quickly to indicators of compromise. Identify and respond to cyber threats before they strike
24/7 Monitoring
Moses AI reviews every single traffic record for patterns of malicious behavior.
Continuously Learning
Moses AI's network model continually learns from all network traffic; learning what ‘normal’ looks like from networks across the world.
Prioritize real attacks, not false alarms
Moses AI is a transformer based foundation model trained to inspect network traffic across multiple dimensions and is capable of self reasoning about network traffic to determine if network events are anomalous or is it a network attack.
Our advanced AI analyzes all network traffic to create unique fingerprints, distinguishing between normal and malicious behavior. This results in fewer alerts, allowing teams to focus critical time on real threats, not false alarms.
Spot network attacks before they strike
Moses AI is trained to identify critical stages of network attacks in the event of a breach with precision. During network attacks, attackers will use command and control (C2) techniques to communicate in and out of a network without detection. Disrupting and cutting off these communication channels prevents attacks from carrying out their attacks including protecting organizations from data exfiltration and encryption.
Moses AI is trained on recognizing the subtle features of command and control on a network such as identifying Rare external targets, predictable time intervals and unusual packet size.