Multi-Computer System for Providing Continuous Authentication and Secure Access Control

A method, comprising:

• receiving, by a computing platform, the computing platform having at least one processor and memory, and from a plurality of data sources, user data captured based on user interactions with each data source of the plurality of data sources;
• training, by the at least one processor and using the user data captured based on the user interactions with each data source of the plurality of data sources, a machine learning model;
• generating, by the at least one processor and using the machine learning model, user specific baseline data;
• receiving, by the at least one processor and from the plurality of data sources, first user data, the first user data including a plurality of data types;
• executing, by the at least one processor and using the first user data as inputs, the machine learning model to output whether an anomaly is detected in at least one data type of the plurality of data types and from at least one data source of the plurality of data sources, between the first user data and the user specific baseline data;
• responsive to not detecting an anomaly in the at least one data type of the plurality of data types and from the at least one data source of the plurality of data sources, receiving, by the at least one processor, second user data and execute, using the second user data as inputs, the machine learning model to output whether an anomaly exists in the second user data;
• responsive to detecting an anomaly in the at least one data type of the plurality of data types and from the at least one data source of the plurality of data sources:
• determining, by the at least one processor, a first confidence factor for the at least one data type of the plurality of data types and the at least one data source of the plurality of data sources;
• determining, by the at least one processor, a second confidence factor for a remainder of the data types of the plurality of data types and a remainder of the data sources of the plurality of data sources;
• comparing, by the at least one processor, the first confidence factor to the second confidence factor to determine a higher confidence factor;
• responsive to determining the second confidence factor is higher than the first confidence factor, authenticating, by the at least one processor, the user;
• responsive to determining the first confidence factor is higher than the second confidence factor:
• identifying, by the at least one processor, a response action;
• sending, by the at least one processor and to at least one computing device, the identified response action for execution, wherein sending the identified response action causes the at least one computing device to execute the response action;
• and
• updating, by the at least one processor, the machine learning model based on the detecting the anomaly and the response action.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to:

• receive, from a plurality of data sources, user data captured based on user interactions with each data source of the plurality of data sources;
• train, using the user data captured based on the user interactions with each data source of the plurality of data sources, a machine learning model;
• generate, using the machine learning model, user specific baseline data;
• receive, from the plurality of data sources, first user data, the first user data including a plurality of data types;
• execute, using the first user data as inputs, the machine learning model to output whether an anomaly is detected in at least one data type of the plurality of data types and from at least one data source of the plurality of data sources, between the first user data and the user specific baseline data;
• responsive to not detecting an anomaly in the at least one data type of the plurality of data types and from the at least one data source of the plurality of data sources, receive second user data and execute, using the second user data as inputs, the machine learning model to output whether an anomaly exists in the second user data;
• responsive to detecting an anomaly in the at least one data type of the plurality of data types and from the at least one data source of the plurality of data sources:
• determine a first confidence factor for the at least one data type of the plurality of data types and the at least one data source of the plurality of data sources;
• determine a second confidence factor for a remainder of the data types of the plurality of data types and a remainder of the data sources of the plurality of data sources;
• compare the first confidence factor to the second confidence factor to determine a higher confidence factor;
• responsive to determining the second confidence factor is higher than the first confidence factor, authenticate the user;
• responsive to determining the first confidence factor is higher than the second confidence factor:
• identify a response action;
• send, to at least one computing device, the identified response action for execution, wherein sending the identified response action causes the at least one computing device to execute the response action;
• and
• update the machine learning model based on the detecting the anomaly and the response action.