Abstract: The disclosed techniques automatically ingest new documents and store data extracted from the documents in a database for conversion into a different format. The disclosed techniques identify, via a backend API, newly released documents that include data for users and, based on the identifying, automatically ingest, via an ingestion call executed made by the backend API, the newly released documents. The disclosed techniques extract, using a computer vision model trained on different types of documents, a data from the newly released documents, where the extracting includes identifying locations within the documents from which to extract data. The disclosed techniques store the extracted data in the database storing data extracted from previously ingested documents for users in a text-based object format and convert, using a machine learning model trained on a plurality of metatags, data corresponding to a given user from the text-based object format to a queryable file format.

Abstract: The disclosed techniques automatically ingest new documents and store data extracted from the documents in a database for conversion into a different format. The disclosed techniques identify, via a backend API, newly released documents that include data for users and, based on the identifying, automatically ingest, via an ingestion call executed made by the backend API, the newly released documents. The disclosed techniques extract, using a computer vision model trained on different types of documents, a data from the newly released documents, where the extracting includes identifying locations within the documents from which to extract data. The disclosed techniques store the extracted data in the database storing data extracted from previously ingested documents for users in a text-based object format and convert, using a machine learning model trained on a plurality of metatags, data corresponding to a given user from the text-based object format to a queryable file format.

Abstract: The disclosed techniques include the generation of a summary event log from multiple event logs that are identical. Event logs may be identical when the event logs include the same action by the same user using the same source. In various instances, multiple event logs accumulated over a predetermined period of time are summarized in the summary event log. The summary event log includes the information that is identical among the multiple event logs along with additional information about the time period the event logs are accumulated, the number of events that are identical, and memory usage by the event logs. Other information may also be annotated to the summary event log as necessary.

Abstract: The disclosed techniques identify and prevent suspicious activity occurring within a messaging software executed via an enterprise system. The disclosed techniques detect a current message sent from a particular user account of the messaging software executed via the enterprise system at a user device. The techniques compare the current message and user account activity associated with the current message with a behavior profile of the particular user account, where the behavior profile is generated based on prior user account activity that includes one or more prior messages sent from the user account via the enterprise system, and where the prior account activity occurs before the current message. Based on the comparing and predetermined risk levels, the disclosed techniques determine a risk level of the current message. Based on the determined risk level of the current message, the disclosed techniques perform preventative actions with respect to the particular user account.

Abstract: Techniques are disclosed relating to multi-factor authentication for data security. In some embodiments, a computer system receives, from a user device, a database operation request that specifies a set of query data, where the computer system supports multiple different security levels requiring different subsets of a set of authentication factors supported by a known device of a user of the user device. Various devices may determine current contextual information for the database operation request, where the contextual information indicates the set of query data. In some embodiments, the computer system selects, based on the current contextual information, a security level from the multiple different security levels. In some embodiments, the computer system revokes, based on the selected security level, access privileges of the user for accessing a database corresponding to the database operation request.

Abstract: Techniques are disclosed relating to multi-factor authentication for data security. In some embodiments, a computer system receives, from a user device, a database operation request that specifies a set of query data, where the computer system supports multiple different security levels requiring different subsets of a set of authentication factors supported by a known device of a user of the user device. Various devices may determine current contextual information for the database operation request, where the contextual information indicates the set of query data. In some embodiments, the computer system selects, based on the current contextual information, a security level from the multiple different security levels. In some embodiments, the computer system revokes, based on the selected security level, access privileges of the user for accessing a database corresponding to the database operation request.

Abstract: Techniques are disclosed relating to determining risk associated with automated authentication decisions for a multi-factor authentication scheme. In disclosed embodiments, a server system sends requests corresponding to factors in a current multi-factor authentication procedure to a mobile device. The system receives, from the mobile device, automatically generated responses for the factors, where the responses are automatically generated at the mobile device using a machine learning model based on a current set of parameters for the current procedure and a previous set of parameters for a prior procedure. Based on a current state of the mobile device received with the automatically generated responses and prior states of the mobile device stored at the server computer system, the system determines a risk score for the automatically generated responses.

Abstract: Techniques are disclosed relating to multi-factor authentication for data security. In some embodiments, a computer system receives, from a user device, a database operation request that specifies a set of query data, where the computer system supports multiple different security levels requiring different subsets of a set of authentication factors supported by a known device of a user of the user device. Various devices may determine current contextual information for the database operation request, where the contextual information indicates the set of query data. In some embodiments, the computer system selects, based on the current contextual information, a security level from the multiple different security levels. In some embodiments, the computer system revokes, based on the selected security level, access privileges of the user for accessing a database corresponding to the database operation request.

Abstract: Techniques are disclosed relating multi-factor authentication for data security. In some embodiments, a computer system receives, from a user device, a database operation request that specifies a set of query data, where the computer system supports multiple different security levels requiring different subsets of a set of authentication factors supported by a known device of a user of the user device. Various devices may determine current contextual information for the database operation request, where the contextual information indicates the set of query data. In some embodiments, the computer system compares the current contextual information with prior contextual information for database requests and then selects a security level from the different security levels. The computer system then initiates verification of a subset of authentication factors corresponding to the selected security level.

Abstract: Techniques are disclosed relating multi-factor authentication for data security. In some embodiments, a computer system receives, from a user device, a database operation request that specifies a set of query data, where the computer system supports multiple different security levels requiring different subsets of a set of authentication factors supported by a known device of a user of the user device. Various devices may determine current contextual information for the database operation request, where the contextual information indicates the set of query data. In some embodiments, the computer system compares the current contextual information with prior contextual information for database requests and then selects a security level from the different security levels. The computer system then initiates verification of a subset of authentication factors corresponding to the selected security level.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: Techniques are disclosed relating to authenticating a second mobile device for participation in a multi-factor authentication process. In disclosed embodiments, a server generates an authentication decision, based on communicating with a first mobile device as a factor in the multi-factor authentication process. After receiving a request from the first mobile device to authorize participation of a second mobile device in the multi-factor authentication process, the server may generate a secret and transmit the secret to the first mobile device. The server may receive information from the second mobile device, based on the second mobile device capturing an image of a display by the first mobile device, where the display is based on the transmitted secret. In some embodiments, the server then verifies the content of the information using the secret and verifies that the information is received within a determined time interval from transmitting the secret.

Abstract: Techniques are disclosed relating to automating authentication decisions for a multi-factor authentication scheme based on computer learning. In disclosed embodiments, a mobile device receives a first request corresponding to a factor in a first multi-factor authentication procedure. Based on user input approving or denying the first request, the mobile device sends a response to the first request and stores values of multiple parameters associated with the first request. The mobile device receives a second request corresponding to a factor in a second multi-factor authentication procedure where the second request is for authentication for a different account than the first request. The mobile device automatically generates an approval response to the second request based on performing a computer learning process on inputs that include values of multiple parameters for the second request and the stored values of the multiple parameters associated with the first request.

Abstract: Techniques are disclosed relating to authenticating a second mobile device for participation in a multi-factor authentication process. In disclosed embodiments, a server generates an authentication decision, based on communicating with a first mobile device as a factor in the multi-factor authentication process. After receiving a request from the first mobile device to authorize participation of a second mobile device in the multi-factor authentication process, the server may generate a secret and transmit the secret to the first mobile device. The server may receive information from the second mobile device, based on the second mobile device capturing an image of a display by the first mobile device, where the display is based on the transmitted secret. In some embodiments, the server then verifies the content of the information using the secret and verifies that the information is received within a determined time interval from transmitting the secret.

Abstract: Embodiments are disclosed relating to authenticating a user of a client computer system. One embodiment may include an application, executing on a client computer system, receiving an indication that a user is requesting access to a server computer system. In response to the receiving, the application may detect that a mobile device of the user is connected to a wireless network to which the client computer system is also connected. The application may receive authentication information for the request from the detected mobile device. The application may then send an authentication request to the server computer system. The authentication request may include data based on the authentication information received from the detected mobile device.