Abstract: Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.

Abstract: Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.

Abstract: A component risk control generator includes a set of machine learning models, an automatic command set generator, and a model quality assessment engine. Using a set of received input items, the automatic command set generator generates a component activity generator command set. A trained upstream machine learning model generates a component activity output set, which is used to generate a set of cascading command sets. Using at least one command set in the set of cascading command sets, a set of trained downstream machine learning models generate a plurality of cascading command sets and downstream output sets based thereon. The output sets can be validated using the model quality assessment engine.

Abstract: The present invention relates to a method, system and computer program product for enabling the remote authentication of fingerprints over an insecure network using a client-server architecture by generation of a set of random queries relating to fingerprint patterns based on stored fingerprint data at the server, to which the client responds based on the observed fingerprint patterns, followed by the issuing of a randomly generates set of challenges pertaining to geometrical relationships between the fingerprint patterns for which confirm responses are received by their server, the final authentication being determined by the proportion of correct responses by the client to said challenges.

Abstract: The present invention relates to a method, system and computer program product for enabling the remote authentication of fingerprints over an insecure network using a client-server architecture by generation of a set of random queries relating to fingerprint patterns based on stored fingerprint data at the server, to which the client responds based on the observed fingerprint patterns, followed by the issuing of a randomly generates set of challenges pertaining to geometrical relationships between the fingerprint patterns for which confirm responses are received by their server, the final authentication being determined by the proportion of correct responses by the client to said challenges.