Abnormal Security Corporation

Sanny Xiao Yang Liao

Jeshua Alexis Bratman

Sanjay Jeyakumar

Yu Zhou Lee

Kai Jing Jiang

Cheng-Lin Yeh

Abhijit Bagri

Jeremy Kao

Su Li Debbie Tan

Dmitry Chechik

Kevin Lau

Evan James Reiser

Carlos Daniel Gasperi

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.

Programmatic discovery, retrieval, and analysis of communications to identify abnormal communication activity

Threat detection platforms for detecting, characterizing, and remediating email-based threats in real time

Retrospective learning of communication patterns by machine learning models for discovering abnormal behavior

Multistage analysis of emails to identify security threats

Growing community of inventors

San Francisco, CA, United States of America

Sanny Xiao Yang Liao