Abstract: A computer-implemented method for secure transaction processing using machine learning and quantum computing includes receiving input parameters corresponding to a transaction at a processor. A quantum computing processor processes the input parameters using one or more sets of quantum resistive cryptography instructions and further generates encrypted transaction data from the input parameters. The quantum computing processor further generates an indication of whether a transaction is fraudulent based upon the encrypted transaction data by applying a machine learning algorithm or model. The processor then transmits the indication via an electronic network.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Abstract: The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Abstract: Machine learning methods and systems for developing security governance recommendations are disclosed.

Abstract: Machine learning methods and systems for developing security governance recommendations are disclosed.

Abstract: A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Abstract: Systems and methods for improving an onboarding process of a use case received from a user device using machine learning by automatically evaluating a potential to implement machine learning on the use case and automating the onboarding process, which may include receiving and processing an initial use case data set using a trained tractability machine learning model to generate a first determination whether the use case is machine learning tractable. The trained tractability machine learning model is trained using historical tractability data. Generating an onboarding machine learning model for solving the use case based at least upon the first determination the use case is machine learning tractable. Receiving a feedback data set and processing the initial use case data set and the feedback data set using the trained onboarding machine learning model.

Abstract: A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Abstract: A method includes receiving a computing system future state description in response to prompting a user; determining specific properties; predicting a solution architecture based on the specific properties; and generating infrastructure-as-code. A computing system includes a processor; and a memory having stored thereon instructions that, when executed, cause the computing system to: prompt a user to describe a future state of a computing system; receive a description of the future state; determine specific properties; predict a solution architecture based on the specific properties; and generate infrastructure-as-code.

Abstract: A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Abstract: A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.

Abstract: A method includes receiving user inputs; receiving codified knowledge management information; receiving engine data; and processing the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A computing system includes one or more processors; and a memory comprising instructions that, when executed, cause the computing system to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document.

Abstract: A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.