Abstract: Systems and methods for enhanced rule-based querying of unstructured text using graph analysis are provided. Some embodiments apply machine learning to the analysis of unstructured text to identify regulatory, privacy, and cybersecurity risks. In some embodiments, a query string is provided to identify desired content in the unstructured text. The query string is tokenized and the tokenized parts are converted into nodes of a query graph and relationships are defining between the nodes. After identifying matches between terms in target text with nodes of the query graph, a graph is generated using matched terms as nodes. Based on the defined relationships, a determination is made whether any group of nodes on the graph satisfy the query string. Any matched nodes are mapped to the target text, and this information is provided to an analyst or reviewer for further consideration or action.

Abstract: 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.

Abstract: Systems and methods are disclosed for using natural language processing to generate sound-alikes and look-alikes for potentially relevant terms that could be mis-transcribed in a transcript. The disclosed framework combines the advantages of several measures aimed at tackling a wide range of transcription errors including but not limited to word boundary errors, phonetic confusion of words, spelling mistakes, grammatical errors, character drops, etc. In some examples, morphological similarity and phonetic similarity are used to address the word boundary errors and phonetic confusion of words. In some examples, a spell checker, word formation, and look-alike sound-alike generator are used to address errors such as phonetic confusion, spelling mistakes and grammatical errors. The generated sound-alikes can be ranked, which enables a flexible application of the generated phrases.

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.