Bio-behavior system and method

A system for monitoring a secure network comprising:

• a first plurality of processors having artificial intelligence machine learning (AI/ML) capabilities forming a smart data hub and a second plurality of hardware processors forming a risk engine, wherein both the smart data hub and risk engine are coupled to a network interface, the first and second plurality of processors configured to:
• continuously capture contextual and behavioral factors of a plurality of user entities at the smart data hub to develop a bio-behavioral model of the plurality of user entities through machine learning;
• separate the contextual and behavioral factors of the plurality of user entities into categories;
• receive a transaction request from a relying party server at the risk engine;
• retrieve the bio-behavioral model of at least one of the categories;
• compare allocentric and egocentric factors of the transaction request with the at least one of the categories to determine the level of abnormalities associated with the transaction request and determine a risk score;
• send the risk score back to the risk engine;
• at the risk engine, combine the risk score with a plurality of risk analyzers to determine whether an approval of the transaction request should be sent to the relying party or request of an out of band authorization should be made from the user entity device;
• and
• if the out of band authorization is successful, the risk score is reset and the approval of the transaction request is sent to the relying party and if the out of band authorization is unsuccessful, a disapproval of the transaction request is sent to the relying party.

A method for monitoring a secure network comprising:

• continuously capturing contextual and behavioral factors of a one user entity at the smart data hub to develop a bio-behavioral model of the user entity through machine learning;
• receiving a transaction request from a relying party server at the risk engine;
• contacting a user entity device to collect contextual and behavioral data of the user entity from the user entity device;
• receiving the contextual and behavioral data of the user entity at the risk engine;
• sending the contextual and behavioral data of the user entity to the smart data hub;
• retrieving the bio-behavioral model of the user entity and update with the contextual and behavioral data of the user entity to form an updated bio-behavioral model of the user entity;
• comparing allocentric and egocentric factors of the transaction request with the update bio-behavioral model of the user entity to determine the level of abnormalities associated with the transaction request and determine a risk score;
• sending the risk score back to the risk engine to determine whether to approve the transaction request;
• wherein the risk engine calculates the risk score based on both the user entity contextual factors and the network contextual factors throughout an active session of the client device;
• and
• dynamically changing the level of assurance required of the user entity based on the risk score.