Truist Bank

Joseph Aguayo

Amy L Rose

David Stone

Justin Tyler Dubs

Michael Brantley Lewis

Doyoung Park

Yasmin Martinez

Account permissions and data accessibility can be modified based on level of confidence for a login attempt to the account. User activity observations corresponding to one or more login attempts to access a user account can be stored. A confidence score associated with a successful login attempt of the user account can be determined. The confidence score is based on the user activity observations. A level of access to an application with functions and data for the user account can be determined. The level of access is based on the confidence score. The level of access is associated with the functions and the data that are executable and accessible subsequent to the successful login attempt.

Modifying application function based on login attempt confidence score

Identification of an electronic communication containing specific information is provided. Content of the electronic communication may be evaluated by a machine-learning model, and based on an evaluation of the content, it may be determined that the electronic communication contains the specific information. The electronic communication may be tagged with tag information indicating that the electronic communication contains the specific information, and transmission of the electronic communication may be blocked based on the tag information.

Classifying types of sensitive events for data loss prevention

A source of a login attempt to a user account can be classified using machine learning. For example, a computing system can input user activity observations associated with one or more login attempts to one or more user accounts into a trained machine learning model. One or more distinguishing factors for the one or more login attempts can be received from the trained machine learning model. The computing system can determine a source of a current login attempt by applying a clustering algorithm to current values of the one or more distinguishing factors. The current values may be derived from current user activity observations associated with the current login attempt. The computing system can determine an authentication level for the current login attempt to the user account based on the source of the current login attempt.

Classifying a source of a login attempt to a user account using machine learning

Targeted lockdown of a computer system for an identified vulnerability is provided. The targeted lockdown includes configuring a vulnerability lockdown module implemented on a computer system to perform targeted actions to change a configuration of the computer system. The targeted actions may be configured based at least in part on a type of data stored on the computer system and a potential severity of an impact on the computer system if the vulnerability is exploited. The vulnerability lockdown module may implement a vulnerability lockdown mode by causing the computer system to perform the targeted actions to change the configuration of the computer system by restricting functionality of portions of the computer system affected by the identified vulnerability. The targeted actions performed by the computer system may include altering a way in which a user interacts with the computer system.

Computer system vulnerability lockdown mode

Targeted lockdown of a computer system for an identified vulnerability is provided. The targeted lockdown includes configuring a vulnerability lockdown module implemented on a computer system to perform targeted actions to change a configuration of the computer system. The computer system may be scanned by a vulnerability scanner configured to identify vulnerabilities. In response to identifying a vulnerability, the vulnerability may be communicated to the vulnerability lockdown module and the vulnerability lockdown module may implement a vulnerability lockdown mode by causing the computer system to perform the targeted actions to change the configuration of the computer system by restricting functionality of portions of the computer system affected by the identified vulnerability.

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Fuquay Varina, NC, United States of America

Joseph Aguayo