Proofpoint, Inc.

Theta Lake, Inc.

Nexgate, Inc.

Daniel Oshiro Nadir

Richard Banks Sutton

Devin Hugh Redmond

Anthony Lawrence Dorie

Harold Nguyen

Wenchang Yan

Raymond Kruck

Ali Mesdaq

Derek G Brenner

Sharon Hüffner

Sharon Huffner

Jorge Iriso

Michael Lee

A spammy app detection system may search a database for any new social media application discovered during a recent time period. A spammy app detection algorithm can be executed on the spammy app detection system on an hourly basis to determine whether any of such applications is spammy (i.e., posting to a social media page anomalously). The spammy app detection algorithm has a plurality of stages. When a new social media application fails any of the stages, it is identified as a spammy app. The spammy app detection system can update the database accordingly, ban the spammy application from further posting to a social media page monitored by the spammy app detection system, notify an entity associated with the social media page, further process the spammy application, and so on. In this way, the spammy app detection system can reduce digital risk and spam attacks.

Spammy app detection systems and methods

A data security system interfaces with a plurality of heterogeneous online platforms, each having its own platform specific set of data compliance or security settings. The data security system maps a set of common data compliance or security settings to the platform specific data compliance or security settings and monitors a state of the platform specific set of data compliance or security settings. If the data security system determines that the state of a platform specific data compliance or security settings do not comply with a specified common data compliance or security setting state, the system automatically changes the state of the platform specific data compliance or security settings to comply with the common data compliance or security settings setting state.

Systems and methods for monitoring and enforcing controls across heterogeneous platforms

Disclosed is a new location threat monitoring solution that leverages deep learning (DL) to process data from data sources on the Internet, including social media and the dark web. Data containing textual information relating to a brand is fed to a DL model having a DL neural network trained to recognize or infer whether a piece of natural language input data from a data source references an address or location of interest to the brand, regardless of whether the piece of natural language input data actually contains the address or location. A DL module can determine, based on an outcome from the neural network, whether the data is to be classified for potential location threats. If so, the data is provided to location threat classifiers for identifying a location threat with respect to the address or location referenced in the data from the data source.

Systems and methods for location threat monitoring

A data security system interfaces with a plurality of heterogeneous online collaboration platforms, each having its own platform specific set of collaboration settings to control access to collaborative data. The data security system maps a set of common collaboration settings to the platform specific set of collaboration settings of each heterogeneous online collaboration platform and monitors a state of the platform specific set of collaboration settings of each heterogeneous online collaboration platform. If the data security system determines that the state of a platform specific collaboration setting does not comply with a specified common collaboration setting state, the data security system automatically changes the state of the platform specific collaboration setting to comply with the common collaboration setting state.

Systems and methods for monitoring and enforcing collaboration controls across heterogeneous collaboration platforms

Technology is disclosed for detecting imposters of a brand account. The technology can store a brand profile of the brand account, detect that a message has been publicly communicated to the brand account from a social media account, monitor messages sent publicly to the social media account from other social media accounts by repeatedly comparing the brand profile to metadata of each of the monitored messages, and identify at least one of the other social media accounts as an imposter account based on the comparing. The technology can cease the comparing at predetermined expiration time occurring after the detection of the message that was sent publicly to the brand account.

Detection and prevention of fraudulent activity on social media accounts

Technology is disclosed for detecting, classifying, and/or enforcing policies on social networking activity. The technology can store a content policy, classify content posted to a social network, determine that the classification corresponds to the content policy, and apply an action specified by the content policy to the posted content.

Detecting, classifying, and enforcing policies on social networking activity

A data security system collects collaboration platform collaboration settings through interfaces and analyzes those settings against a policy of common collaboration settings for security and compliance. The data security system interfaces with a plurality of heterogeneous online collaboration platforms, each having its own platform specific set of collaboration settings to control access to collaborative data. The data security system maps the set of common collaboration settings to the platform specific set of collaboration settings of each heterogeneous online collaboration platform from the plurality of heterogeneous online collaboration platforms and monitors a state of the platform specific set of collaboration settings of each heterogeneous online collaboration platform. Based on a determination that a platform specific collaboration setting does not comply with the collaboration setting policy, the data security system can take a remedial action or other action.

Technology is disclosed for detecting, classifying, and/or enforcing rules on social networking activity. The technology can scan and collect social content data from one or more social networks, store the social content data, classify content data posted to a social network, create and apply a set of social data content rules to future posted social content data.

Growing community of inventors

Carlsbad, CA, United States of America

Daniel Oshiro Nadir