Abstract: Methods and systems are described herein for leveraging artificial intelligence to sanitize sensitive data and prevent the data from leaving the mobile device and/or be exposed to unauthorized third parties. More specifically, methods and systems are described for a novel and unconventional architecture for a data sanitization application, a novel and unconventional delivery format for the data sanitization model, and a novel and unconventional output format of the data sanitization model.

Abstract: Methods and systems are described herein for leveraging artificial intelligence to sanitize sensitive data and prevent the data from leaving the mobile device and/or be exposed to unauthorized third parties. More specifically, methods and systems are described for a novel and unconventional architecture for a data sanitization application, a novel and unconventional delivery format for the data sanitization model, and a novel and unconventional output format of the data sanitization model.

Abstract: Methods and systems are described herein for leveraging artificial intelligence to sanitize sensitive data and prevent the data from leaving the mobile device and/or be exposed to unauthorized third parties. More specifically, methods and systems are described for a novel and unconventional architecture for a data sanitization application, a novel and unconventional delivery format for the data sanitization model, and a novel and unconventional output format of the data sanitization model.

Abstract: Disclosed herein are system, method, and computer program product embodiments for determining a probability of a device at risk. The device may be associated with a plurality of security parameters. For a security parameter, the device can be in multiple states. A probability value corresponding to a security parameter can indicate the security parameter being in a state among the multiple states. A probabilistic graphical model may be used to represent dependences of the plurality of security parameters. A device security risk prediction module may determine a probability of the device at risk based on the probabilistic graphical model and the probability assignments to the plurality of nodes of the probabilistic graphical model, and further determine a user action instruction to be provided to a user of the device based on the probability of the device at risk.

Abstract: Systems and methods for securing data for outbound communication may include: determining a risk profile for the outbound communication based, the outbound communication comprising data modules including at least a payload, an origination endpoint, and a destination endpoint; determining a scanning policy from a plurality of scanning policies, based on the determined risk profile; determining a secure machine learning model from a plurality of secure machine learning models, based on the determined risk profile, wherein the secure machine learning model is determined based on an authentication level corresponding to the determined risk profile; providing one or more data modules to the secure machine learning model, based on the determined scanning policy; receiving a sanitized version of the payload based on an output of the secure machine learning model; and providing the sanitized version of the payload for the outbound communication.

Abstract: Provided herein are systems and methods for sanitizing logged data packets in a distributed system prior to storing them in a remote or third-party data server. Interactions with an application are monitored and values in a data packet are extracted from the interaction. The values are classified based on a classification configuration and respective labels of the values. The values are then sanitized based on the classification to prevent exposure of secure or private data. The sanitized data packets are then logged into the remote data server. The logged data can be used to help resolve events occurring in the application. The classification configuration can be iteratively updated and the interactions repeated to capture data that was previously sanitized to aid in resolution of events. The logged data can also be used in research or analysis, such as for identifying potential improvements to the application.

Abstract: Systems for partitioning text are disclosed. The system can receive a text string. A delimiter can be identified based on the text string. Based on identifying the delimiter, a character sequence to the left and/or right of the delimiter can be identified. The identification can occur up to a predetermined number/length of characters. Using a trained model, the system can determine whether the character sequence indicates the delimiter is part of a continuous string of text. Based on determining whether or not the delimiter is part of the continuous string of text, the system can generate a token representing the continuous string of text or the delimiter.

Abstract: Provided herein are systems and methods for sanitizing logged data packets in a distributed system prior to storing them in a remote or third-party data server. Interactions with an application are monitored and values in a data packet are extracted from the interaction. The values are classified based on a classification configuration and respective labels of the values. The values are then sanitized based on the classification to prevent exposure of secure or private data. The sanitized data packets are then logged into the remote data server. The logged data can be used to help resolve events occurring in the application. The classification configuration can be iteratively updated and the interactions repeated to capture data that was previously sanitized to aid in resolution of events. The logged data can also be used in research or analysis, such as for identifying potential improvements to the application.

Abstract: Provided herein are systems and methods for sanitizing logged data packets in a distributed system prior to storing them in a remote or third-party data server. Interactions with an application are monitored and values in a data packet are extracted from the interaction. The values are classified based on a classification configuration and respective labels of the values. The values are then sanitized based on the classification to prevent exposure of secure or private data. The sanitized data packets are then logged into the remote data server. The logged data can be used to help resolve events occurring in the application. The classification configuration can be iteratively updated and the interactions repeated to capture data that was previously sanitized to aid in resolution of events. The logged data can also be used in research or analysis, such as for identifying potential improvements to the application.