Abstract: A neural dialogue system has been designed to present a conversational user interface for managing security posture of an organization. The neural dialogue system determines intent and extracts entity names from a user input. The neural dialogue system grounds the entity names to an organizational context and maps the intent to a defined functionality related to security posture management for intent realization. Some of these defined functions involve editing the ordered ruleset. When realization of an intent involves editing the ordered ruleset, the neural dialogue system determines various ruleset command sequences that implement the ruleset editing and corresponding impacts on the ordered ruleset (e.g., creation of ruleset anomalies). The dialogue system collects the information retrieved based on the intent and grounded entities, including any ruleset impact assessment, and generates a response.

Abstract: An organization-specific cybersecurity dialogue system (“system”) intelligently generates responses to user utterances for cybersecurity by employing documentation and playbook agents for an associated cybersecurity organization. The system extracts an intent and an intent category from a user utterance, extracts relevant entities from the intent and uses them to determine relevant user telemetry data, and matches the intent, intent category, and user telemetry data to documents of the cybersecurity organization. The system then determines whether to generate a response based on matching document, matching playbook agents, or to prompt a user to create a ticket according to logic applied to the intent, intent category, user telemetry data, and matching documents and generates a corresponding response with large language models.

Abstract: A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Abstract: A device receives image data that depicts an individual, identifies the individual by using a target recognition technique to analyze the image data, and identifies human attributes of the individual. The device determines a risk level that represents a likelihood of the individual being or becoming involved in a harmful act. The risk level may be determined by using the human attributes as part of a machine-learning-driven emotional analysis of the individual. The device provides a message, selected based on whether the risk level satisfies at least one of the one or more threshold risk levels, to another device, to cause the other device to perform actions that are indicative of the risk level being associated with a low amount of risk, or other actions indicative of the risk level being associated with a high amount of risk and that are associated with assisting in prevention of the harmful act.

Abstract: A device receives image data that depicts an individual, identifies the individual by using a target recognition technique to analyze the image data, and identifies human attributes of the individual. The device determines a risk level that represents a likelihood of the individual being or becoming involved in a harmful act. The risk level may be determined by using the human attributes as part of a machine-learning-driven emotional analysis of the individual. The device provides a message, selected based on whether the risk level satisfies at least one of the one or more threshold risk levels, to another device, to cause the other device to perform actions that are indicative of the risk level being associated with a low amount of risk, or other actions indicative of the risk level being associated with a high amount of risk and that are associated with assisting in prevention of the harmful act.

Abstract: A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Abstract: A mobile application and computer-implemented method for facilitating in-person social introductions among other users that include logging into the application, accessing a map showing the location of the user and member locations, entering a member location to determine which other users are present at the member location, viewing other member information on a handheld device, communicating with at least one other user to make an in-person face-to-face meeting with the other user, and meeting the other user face-to-face. Invoices are generated and sent to member locations based on the amount of time a user is present at the member location.