Dynamic unauthorized activity detection and control system
Systems for dynamically detecting and controlling unauthorized events are presented. In some examples, data may be received from one or more computing systems. In some examples, the computing systems may each be associated with an enterprise unit within an enterprise organization. The data may include, in some examples, processed unauthorized activity event data, such as account takeover event data. The data received may be aggregated and analyzed (e.g., using machine learning) to identify potential threats and threat outputs. In some examples, the threat output may include a user interface indicating the threat or potential threat, systems or applications potentially impacted, enterprise units impacted, and the like. Based on the threat output, one or more mitigation actions may be identified and executed. The mitigation actions may include modifying operation of one or more systems, modifying authentication requirements, and the like.
1. A computing platform, comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
a memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive, in real-time and from a first enterprise unit of an enterprise organization, first unauthorized activity event data, the first unauthorized activity event data including login data received by a computing system of the first enterprise unit and analyzed by the computing system of the first enterprise unit to identify unauthorized activity;
receive, in real-time and from a second enterprise unit of the enterprise organization, second unauthorized activity event data, the second unauthorized activity event data including login data received by a computing system of the second enterprise unit and analyzed by the computing system of the second enterprise unit to identify unauthorized activity;
aggregate the first unauthorized activity event data and the second unauthorized activity event data including formatting at least one of: the first unauthorized activity event data or the second unauthorized activity event data to generate aggregated data;
analyze, using machine learning, the aggregated data, the analyzing the aggregated data including mining data logs at pre-determined intervals;
generate, based on the analyzed aggregated data, a threat output, the threat output including a vulnerability in one of: the first enterprise unit or the second enterprise unit based on the analysis of the aggregated data;
identify, based on the threat output and using machine learning, at least one mitigating action to execute; and
execute the at least one mitigating action.
2. The computing platform of claim 1 , wherein the at least one mitigating action includes modifying authentication requirements to access at least one system of the enterprise organization.
3. The computing platform of claim 1 , wherein the at least one mitigating action includes modifying operation of at least one system of the enterprise organization.
4. The computing platform of claim 1 , wherein the first unauthorized activity event data and the second unauthorized activity event data include data associated with account takeover attempts.
5. The computing platform of claim 1 , wherein generating the threat output further includes generating a user interface including data identifying a potential threat.
6. The computing platform of claim 1 , further including instructions that, when executed, cause the computing platform to:
after executing the at least one mitigating action, receive mitigation data; and
validate one or more machine learning datasets based on the received mitigation data.
7. The computing platform of claim 1 , wherein analyzing, using machine learning, the aggregated data, further includes determining a total amount of data received from different types of data streams.
8. A method, comprising:
receiving, by a computing platform having a memory and at least one processor, in real-time and from a first enterprise unit of an enterprise organization, first unauthorized activity event data, the first unauthorized activity event data including login data received by a computing system of the first enterprise unit and analyzed by the computing system of the first enterprise unit to identify unauthorized activity;
receive, by the at least one processor in real-time and from a second enterprise unit of the enterprise organization, second unauthorized activity event data, the second unauthorized activity event data including login data received by a computing system of the second enterprise unit and analyzed by the computing system of the second enterprise unit to identify unauthorized activity;
aggregating, by the at least one processor, the first unauthorized activity event data and the second unauthorized activity event data including formatting at least one of: the first unauthorized activity event data or the second unauthorized activity event data to generate aggregated data;
analyzing, by the at least one processor and using machine learning, the aggregated data, the analyzing the aggregated data including mining data logs at pre-determined intervals;
generating, by the at least one processor and based on the analyzed aggregated data, a threat output, the threat output including a vulnerability in one of: the first enterprise unit or the second enterprise unit based on the analysis of the aggregated data;
identifying, by the at least one processor, based on the threat output and using machine learning, at least one mitigating action to execute; and
executing, by the at least one processor, the at least one mitigating action.
9. The method of claim 8 , wherein the at least one mitigating action includes modifying authentication requirements to access at least one system of the enterprise organization.
10. The method of claim 8 , wherein the at least one mitigating action includes modifying operation of at least one system of the enterprise organization.
11. The method of claim 8 , wherein the first unauthorized activity event data and the second unauthorized activity event data include data associated with account takeover attempts.
12. The method of claim 8 , wherein generating the threat output further includes generating a user interface including data identifying a potential threat.
13. The method of claim 8 , further including
after executing the at least one mitigating action, receiving, by the at least one processor, mitigation data; and
validating, by the at least one processor, one or more machine learning datasets based on the received mitigation data.
14. 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, in real-time and from a first enterprise unit of an enterprise organization, first unauthorized activity event data, the first unauthorized activity event data including login data received by a computing system of the first enterprise unit and analyzed by the computing system of the first enterprise unit to identify unauthorized activity;
receive, in real-time and from a second enterprise unit of the enterprise organization, second unauthorized activity event data, the second unauthorized activity event data including login data received by a computing system of the second enterprise unit and analyzed by the computing system of the second enterprise unit to identify unauthorized activity;
aggregate the first unauthorized activity event data and the second unauthorized activity event data including formatting at least one of: the first unauthorized activity event data or the second unauthorized activity event data to generate aggregated data;
analyze, using machine learning, the aggregated data, the analyzing the aggregated data including mining data logs at pre-determined intervals;
generate, based on the analyzed aggregated data, a threat output, the threat output including a vulnerability in one of: the first enterprise unit or the second enterprise unit based on the analysis of the aggregated data;
identify, based on the threat output and using machine learning, at least one mitigating action to execute; and
execute the at least one mitigating action.
15. The one or more non-transitory computer-readable media of claim 14 , wherein the at least one mitigating action includes modifying authentication requirements to access at least one system of the enterprise organization.
16. The one or more non-transitory computer-readable media of claim 14 , wherein the at least one mitigating action includes modifying operation of at least one system of the enterprise organization.
17. The one or more non-transitory computer-readable media of claim 14 , wherein the first unauthorized activity event data and the second unauthorized activity event data include data associated with account takeover attempts.
18. The one or more non-transitory computer-readable media of claim 14 , wherein generating the threat output further includes generating a user interface including data identifying a potential threat.
19. The one or more non-transitory computer-readable media of claim 14 , further including instructions that, when executed, cause the computing platform to:
after executing the at least one mitigating action, receive mitigation data; and
validate one or more machine learning datasets based on the received mitigation data.