Palantir Technologies Inc.

Jacob Albertson

Peter Maag

Harkirat Singh

Rick Ducott

Shyam Sankar

Marissa Kimball

Melody Hildebrandt

Sean Hunter

Aditya Kumar

Jared Newman

Matthew Lynch

Maciej Albin

Viktor Nordling

Parvathy Menon

Adam Borochoff

Michael Harris

Michael Kross

Matthew Sprague

Alexander Visbal

Christopher Rogers

Pierre Cholet

Daniel Campos

Trevor Austin

Corentin Petit

Paul Baseotto

Timur Iskhakov

Victoria Galano

Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, automatically tag and group those clustered data structures, and provide results of the automated analysis and grouping in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria, rules, indicators, or scenarios so as to generate scores, reports, alerts, or conclusions that the analyst may quickly and efficiently use to 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 in the context of, for example, a risky trading investigation.

Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data

Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Cyber security sharing and identification system

Methods, devices, systems and computer program products enable monitoring and responding to cyber security attacks. One such system relates to a consortium of monitoring companies and an infrastructure including one or more central monitoring stations or local handling stations for a monitoring company are provided. A central monitoring station of a monitoring company detects a cyberattack that has been launched against a client computer system, and requests a local station to respond to the cyberattack via onsite visits or requests additional resources from other monitoring companies through the consortium system. The central monitoring station also sends to the consortium system updates on a cyberattack that is detected or mitigated by a central monitoring station or local handling station of the monitoring company. The monitoring consortium enables stronger capabilities than any individual monitoring company can offer by the combination and coordination of the efforts and resources of the members.

Joined and coordinated detection, handling, and prevention of cyberattacks

Discussed herein are embodiments of methods and systems which allow engineers or administrators to create modular plugins which represent the logic for various fault detection tests that can be performed on data pipelines and shared among different software deployments. In some cases, the modular plugins each define a particular test to be executed against data received from the pipeline in addition to one or more configuration points. The configuration points represent configurable arguments, such as variables and/or functions, referenced by the instructions which implement the tests and that can be set according to the specific operation environment of the monitored pipeline.

Pluggable fault detection tests for data pipelines

In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Tax data clustering

Security sharing system

Growing community of inventors

New York, NY, United States of America

Jacob Albertson