Abstract: Methods and systems to detect and manage access to sensitive information. The system retrieves a dataset of entitlements for a plurality of users. The dataset of entitlements specifies one or more respective exposure vectors. The system detects a triggering event, including a release of sensitive information to a first subset of users of the plurality of users via a first exposure vector. The system then determines a second subset of users of the plurality of users that have access to the first exposure vector based on a comparison of the first exposure vector to the dataset of entitlements. The system monitors electronic communications of the second subset of users for activity related to the sensitive information to detect first activity of a first user of the second subset of users. The system generates for display a first notification to a second user based on detecting the first activity.

Abstract: Methods, systems, and apparatuses are described herein for using machine learning processes to improve synthetic monitoring of, e.g., websites. Training data may comprise sitemap information for a plurality of different websites and monitoring information that indicates whether, for each of a plurality of different portions of those websites, one or more synthetic monitoring scripts are executed. A machine learning model may be trained using that training data to output whether one or more portions of an input website should be monitored using synthetic monitoring. A sitemap of a first website may be determined and provided as input to the trained first machine learning model. Based on output from the trained first machine learning model, a synthetic monitoring script may be determined and executed to monitor at least a first portion of the first website.

Abstract: In some implementations, a testing system may receive a request for generation, based on a first dataset, of a second dataset, wherein the first dataset is associated with execution of a set of tests on an application, wherein the first dataset includes one or more data elements that satisfy one or more criteria for classification as private information. The testing system may process, using a machine learning model, the first dataset to identify one or more characteristics of the first dataset. The testing system may generate, using the machine learning model, the second dataset based on the first dataset, wherein the second dataset includes artificially generated data elements. The testing system may transmit an output identifying the second dataset.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a testing system may receive a request for testing of a first application. The testing system may identify a testing environment for the first application. The testing system may identify a stored instance of the testing environment. The testing system may instantiate a first testing lane based on the stored instance of the testing environment. The testing system may determine a set of resources or a set of dependencies for testing the first application in the testing environment. The testing system may configure the first testing lane based on the set of resources or the set of dependencies. The testing system may execute a set of tests on the first application using the first testing lane and provide an output identifying a result. Numerous other aspects are described.

Abstract: Methods and systems to detect and manage access to sensitive information. The system retrieves a dataset of entitlements for a plurality of users. The dataset of entitlements specifies one or more respective exposure vectors. The system detects a triggering event, including a release of sensitive information to a first subset of users of the plurality of users via a first exposure vector. The system then determines a second subset of users of the plurality of users that have access to the first exposure vector based on a comparison of the first exposure vector to the dataset of entitlements. The system monitors electronic communications of the second subset of users for activity related to the sensitive information to detect first activity of a first user of the second subset of users. The system generates for display a first notification to a second user based on detecting the first activity.

Abstract: In at least some embodiments, systems and methods of the present disclosure enable content selections for securing communications between agent devices and user devices. A request is received for a communication between an agent device and a user device. A user record associated with the user device is determined based on the characteristic. The security system may obtain content items for authenticating the agent device for the user device with the user record. A set of the plurality of content items are determined for display to the agent device. The set of the plurality of content items are transmitted to the user device associated with the user record. A content item identified by a content item selection is received. A match is determined between the selected content item and the set of content items. The communication is transmitted, responsive to the match, with a predetermined type data request for the user device to satisfy the authentication request.

Abstract: Systems and methods are disclosed herein for training machine learning models using precision data based on movement and timing of data collection. The system trains the machine learning model to predict object locations in an environment. The training data includes location data, timing data, and motion data, collection of which is triggered by a user input indicating that a particular object was located. The system generates precision parameters for the locations by assigning initial values to entries within the training data and may lower precision parameters based on the timing data indicating that an object was located faster than a threshold time or based on the motion data indicating that a rate of motion exceeded a threshold rate when a corresponding object was located. The system may update the training data with the lowered precision parameters and train the machine learning model to predict the object locations within the environment.

Abstract: A method includes a system for improving machine-learning-based resource allocation by calibrating resource-related sentiments used to configure a dialogue generation model and updating a prior sentiment based on a response to a generated dialogue item, including a set of processors. Embodiments may also include a non-transitory, machine-readable media storing program instructions that, when executed by the set of processors, performs operations including retrieving a historical record associated with a user and a first natural language input provided by the user for a resource. Embodiments may also include determining, with a first machine learning model, an intermediate sentiment score based on the first natural language input. Embodiments may also include modifying, with the first machine learning model, the intermediate sentiment score based on the historical record to produce a new sentiment score.

Abstract: The invention relates generally to systems and methods for tracking a document by generating document variables, creating multiple new documents with different variables, and sending those new documents to different recipients. The document itself and the source of the document can be verified when a security leak occurs.

Abstract: Methods and systems are described herein for a resource allocation system. The resource allocation system may obtain a corresponding quantity of resources (e.g., memory, processor, storage, etc.) needed to be allocated for each resource class (e.g., for a given performance class) for a particular time period (e.g., for one month). Furthermore, the resource allocation system may track allocation of each class of resources and may predict that some classes of resources will be oversubscribed. Based on the prediction, the resource allocation system may, using a machine learning model, identify supplemental classes for each resource class predicted to be oversubscribed and generate a warning when a resource of a supplemental class is predicted to be used.

Abstract: Aspects of the present disclosure relate to systems and methods for providing instructions (e.g., work instructions) to employees. Specifically, aspects of the present disclosure relate to a network service that dynamically determines instructions for each employee and provides the determined instructions to the employees while executing their duties. The network service can determine the instructions based on a set of inputs generated from each computing device and sensor data received from a plurality of sensors deployed at the workspace.

Abstract: Aspects of the present application relate to systems and methods for generating run-time workflows and assigning employees to the workflows by identifying attributes for each workflow. The workflow assignment can include determining a master plan, processing the master plan to generate a hierarchical data structure, periodically identifying workflows based on the lowest level of the data structure, determining attributes for each workflow in each period, and determining available employees for each workflow identified at each period.

Abstract: Aspects of the present disclosure relate to systems and methods for generating and providing training instructions. Specifically, aspects of the present disclosure relate to a network service that dynamically generates training instructions The network service can generate the training instructions based on a set of inputs generated from each computing device, employees' profile information, dynamically changed working environments, and/or sensor data received from a plurality of sensors deployed at the workspace. The network service can provide potential answers, having a data range to score each answer provided by the employees. The network service can also generate the training instructions sequentially or in random order. In addition, these training instructions can be provided once or recurrently. The network service can also utilize machine learning model to automatically generate the training instructions.

Abstract: Disclosed are systems and methods for detecting compromise of a plurality of co-located interaction cards; including: receiving interaction information indicating that an interaction has been processed, the interaction information indicating that one of the plurality of co-located interaction cards was used in the interaction, the one of the plurality of co-located interaction cards used in the interaction having limited authorized interaction modalities. Based on a determination that the interaction was processed via an unauthorized modality, it is determined that all of the plurality of co-located interaction cards are potentially compromised. In response to determining that all of the plurality of co-located interaction cards are potentially compromised, corrective actions may be taken including: sending an alert to a cardholder; sending an alert to an issuer of at least one of the plurality of co-located interaction cards; or locking each of the plurality of co-located interaction cards.

Abstract: In some embodiments, a computing system may monitor authentication input and modify authentication requirements based on detection of user-specific input errors. The computing system may use machine learning or other techniques to detect whether an incorrect authentication input corresponds to a common input mistake of a user. If the incorrect authentication input does correspond to a common input mistake of the user, a computing system may modify one or more authentication requirements to make the authentication process easier for the user.

Abstract: Methods, systems, and apparatuses are described herein for using machine learning processes to improve synthetic monitoring of, e.g., websites. Training data may comprise sitemap information for a plurality of different websites and monitoring information that indicates whether, for each of a plurality of different portions of those websites, one or more synthetic monitoring scripts are executed. A machine learning model may be trained using that training data to output whether one or more portions of an input website should be monitored using synthetic monitoring. A sitemap of a first website may be determined and provided as input to the trained first machine learning model. Based on output from the trained first machine learning model, a synthetic monitoring script may be determined and executed to monitor at least a first portion of the first website.

Abstract: Methods and systems that use a plurality of machine learning models to both monitor user-generated data entries corresponding to differences in network traffic that may be evidence of a disruption and determine severity levels based on: (i) current and historic differences in average network traffic over the plurality of communication networks; (ii) current and historic user-generated data entries; and (iii) labeled severity levels for historic differences in average network traffic over the plurality of communication networks.

Abstract: In at least some embodiments, systems and methods of the present disclosure enable content selections for securing communications between agent devices and user devices. A request is received for a communication between an agent device and a user device. A user record associated with the user device is determined based on the characteristic. The security system may obtain content items for authenticating the agent device for the user device with the user record. A set of the plurality of content items are determined for display to the agent device. The set of the plurality of content items are transmitted to the user device associated with the user record. A content item identified by a content item selection is received. A match is determined between the selected content item and the set of content items. The communication is transmitted, responsive to the match, with a predetermined type data request for the user device to satisfy the authentication request.

Abstract: Methods and systems to detect and manage access to sensitive information. The system retrieves a dataset of entitlements for a plurality of users. The dataset of entitlements specifies one or more respective exposure vectors. The system detects a triggering event, including a release of sensitive information to a first subset of users of the plurality of users via a first exposure vector. The system then determines a second subset of users of the plurality of users that have access to the first exposure vector based on a comparison of the first exposure vector to the dataset of entitlements. The system monitors electronic communications of the second subset of users for activity related to the sensitive information to detect first activity of a first user of the second subset of users. The system generates for display a first notification to a second user based on detecting the first activity.

Abstract: In some implementations, a remote server may receive a request for authentication including a set of credentials associated with a user. The remote server may transmit, to a user device associated with the user, a request for a status associated with a browsing history of the user device or associated with recent emails viewed on the user device. The remote server may receive, from the user device, an indication of the status. The remote server may transmit a message based on the indication of the status and may determine whether to authenticate the user, using the set of credentials, based on the indication of the status.