Abnormal Security Corporation

Carlos Daniel Gasperi

Sanjay Jeyakumar

Kai Jing Jiang

Jeremy Kao

Jeshua Alexis Bratman

Yu Zhou Lee

Cheng-Lin Yeh

Abhijit Bagri

Su Li Debbie Tan

Dmitry Chechik

Kevin Lau

Evan James Reiser

Sanny Xiao Yang Liao

Yea So Jung

Sanny Xiao Lang Liao

Justin Anthony Young

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

Techniques for producing records of digital activities that are performed with accounts associated with employees of enterprises are disclosed. Such techniques can be used to ensure that records are created for digital activities that are deemed unsafe and for digital activities that are deemed safe by a threat detection platform. At a high level, more comprehensively recording digital activities not only provides insight into the behavior of individual accounts, but also provides insight into the holistic behavior of employees across multiple accounts. These records may be stored in a searchable datastore to enable expedient and efficient review.

Investigation of threats using queryable records of behavior

Multistage analysis of emails to identify security threats

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

Introduced here are computer programs and computer-implemented techniques for producing records of digital activities that are performed with accounts associated with employees of enterprises. Such an approach ensures that records are created for digital activities that are deemed unsafe and for digital activities that are deemed safe by a threat detection platform. At a high level, more comprehensively recording digital activities not only provides insight into the behavior of individual accounts, but also provides insight into the holistic behavior of employees across multiple accounts. These records may be stored in a searchable datastore to enable expedient and efficient review.

Growing community of inventors

New York, NY, United States of America

Carlos Daniel Gasperi