Abstract: Systems and methods for detecting and using semantic relatedness to classify segments of digital text are disclosed. More particularly, embodiments determine the semantic relatedness of segments of text to abstract categories where the abstract categories are not defined by a single word or semantic concept. Detecting semantic relatedness includes analyzing text, embedding the text, and determining semantic relatedness to a set of concepts for a category where each concept may include a set of words/phrases embedded in a similar fashion. The text embedding can be projected onto each concept embedding and reduced to a score representing semantic relatedness. The text is classified based on the semantic relatedness.

Abstract: 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.

Abstract: 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.

Abstract: The disclosed invention provides methods supporting systems that processes video communication(s) provided by an organization subject to internal or external regulations such as a specific financial services firm or a specific consumer product goods company. In various embodiments, video content is analyzed through a pipeline process of multiple extraction scripts, programs, and/or services and processes the content for participant likeness through facial recognition, voice recognition, and voice-to-text and stores the output in a normalized, searchable data store. A policy analysis is performed based on the stored data and external regulatory compliance, internal rules such as acceptable use policies, community-based regulations, or any combination thereof, to determine a risk score.

Abstract: Methods of and systems for identifying a discrete participant among a plurality of participants in an audio/video communication (e.g., video content, digital video content, audio content, and audio-visual content) that include: receiving the audio/video communication; identifying from the audio/video communication content (e.g., using metadata from the broadcast content to identify each participant) multiple identification-related features for each participant; associating a first identification-related feature of a first participant to a second identification-related feature of the first participant; organizing the associated identification-related features of the first participant into a feature class(es); logically relating a first feature class to a second feature class; and attributing a grouping of logically-related feature classes to the discrete participant.