Palantir Technologies Inc.

Joseph Staehle

Samuel Jones

Timothy Yousaf

Ezra Spiro

Juan Ricafort

Andrew Levine

Alvaro Morales

Vivek Lakshmanan

Drew Dennison

Maxim Kesin

Samuel Kremin

Lucy Cheng

Taylor Heroux

Lucas Lemanowicz

Michael Glazer

Robin Lim

Matthew Falk

Sebastien Noury

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computer network security risk assessment. One of the methods includes obtaining compromise likelihoods for user accounts. Information describing a network topology of a network is obtained, with the network topology being nodes each connected by an edge to other nodes, each node being associated with a compromise likelihood, and one or more nodes are high value nodes associated with a compromise value. Unique paths to each of the high value nodes are determined for a particular user account. An expected value for each path is determined based on the compromise likelihood of the particular user account, the compromise likelihood of each node included in the path, the communication weight of each edge included in the path, and the compromise value associated with the high value node. User interface data is generated describing at least one path.

Systems for computer network security risk assessment including user compromise analysis associated with a network of devices

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for network monitoring, user account compromise determination, and user behavior database system. The system monitors network actions of user accounts including user account access across multitudes of network accessible systems, determines user account transitions, and determines different types of high-risk user behavior indicative of compromise. Network actions can be obtained from generated information by the network accessible systems, and correlated across additional data sets including contextual ones. User interfaces are generated describing network actions of user accounts, and are configured for user interaction, which cause generation of updated user interfaces and access to electronic data sources to determine information relevant to the user interaction.

Anomalous network monitoring, user behavior detection and database system

A data analysis system receives potentially undesirable electronic communications and automatically groups them in computationally-efficient data clusters, automatically analyze those data clusters, automatically tags and groups those data clusters, and provides results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the data clusters may include an automated application of various criteria or rules so as to generate an ordered display of the groups of related data clusters such that the analyst may quickly and efficiently evaluate the groups of data clusters. In particular, the groups of data clusters may be dynamically re-grouped and/or filtered in an interactive user interface so as to enable an analyst to quickly navigate among information associated with various groups of data clusters and efficiently evaluate those data clusters.

Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications

Embodiments of the present disclosure relate to a data analysis system that may automatically analyze a suspected malware file, or group of files. Automatic analysis of the suspected malware file(s) may include one or more automatic analysis techniques. Automatic analysis of may include production and gathering of various items of information related to the suspected malware file(s) including, for example, calculated hashes, file properties, academic analysis information, file execution information, third-party analysis information, and/or the like. The analysis information may be automatically associated with the suspected malware file(s), and a user interface may be generated in which the various analysis information items are presented to a human analyst such that the analyst may quickly and efficiently evaluate the suspected malware file(s). For example, the analyst may quickly determine one or more characteristics of the suspected malware file(s), whether or not the file(s) is malware, and/or a threat level of the file(s).

Malware data item analysis

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New York, NY, United States of America

Joseph Staehle