Disaster scenario based inferential analysis using feedback for extracting and combining cyber risk information
First Claim
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1. A method, comprising:
- assessing risk of an entity, using a computer agent configured to collect information from at least publicly accessible Internet elements, wherein the assessing of risk comprises;
generating a disaster scenario that comprises elements of a disaster event;
modeling the disaster scenario against a profile of the entity; and
determining theoretical damage based at least in part on the modeling;
automatically recommending, based at least in part on the assessed risk, changes to reduce the assessed risk to mitigate the theoretical damage;
providing a user interface for receiving from an end user selections of disaster events from a plurality of disaster events; and
based at least in part on the selections, generating an updated disaster scenario;
wherein the selections are inputs for machine learning and generating the updated disaster scenario is based, at least in part, on the machine learning.
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Abstract
Various embodiments of the present technology include methods of assessing risk of a cyber security failure in a computer network of an entity. Some embodiments include generating a disaster scenario that includes elements of a disaster event, modeling the disaster scenario against a profile of the computer network and the entity, determining theoretical damage based on the modeling, and updating a cyber security policy or a network change to mitigate the theoretical damage.
121 Citations
19 Claims
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1. A method, comprising:
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assessing risk of an entity, using a computer agent configured to collect information from at least publicly accessible Internet elements, wherein the assessing of risk comprises; generating a disaster scenario that comprises elements of a disaster event; modeling the disaster scenario against a profile of the entity; and determining theoretical damage based at least in part on the modeling; automatically recommending, based at least in part on the assessed risk, changes to reduce the assessed risk to mitigate the theoretical damage; providing a user interface for receiving from an end user selections of disaster events from a plurality of disaster events; and based at least in part on the selections, generating an updated disaster scenario;
wherein the selections are inputs for machine learning and generating the updated disaster scenario is based, at least in part, on the machine learning. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system, comprising:
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one or more processors configured to; assess risk of an entity, using a computer agent configured to collect information from at least publicly accessible Internet elements, wherein to assess the risk comprises to; generate a disaster scenario that comprises elements of a disaster event; model the disaster scenario against a profile of the entity; and determine theoretical damage based at least in part on the modeling of the disaster scenario; automatically recommend, based at least in part on the assessed risk, changes to reduce the assessed risk to mitigate the theoretical damage; and provide a user interface for receiving from an end user selections of disaster events from a plurality of disaster events; and based at least in part on the selections, generating an updated disaster scenario; wherein the selections are inputs for machine learning and generating the updated disaster scenario is based, at least in part, on the machine learning; and one or more memories coupled to the one or more processors, configured to provide the one or more processors with instructions. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer program product embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:
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assessing risk of an entity, using a computer agent configured to collect information from at least publicly accessible Internet elements, wherein the assessing of risk comprises; generating a disaster scenario that comprises elements of a disaster event; modeling the disaster scenario against a profile of the entity; and determining theoretical damage based at least in part on the modeling; automatically recommending, based at least in part on the assessed risk, changes to reduce the assessed risk to mitigate the theoretical damage; providing a user interface for receiving from an end user selections of disaster events from a plurality of disaster events; and based at least in part on the selections, generating an updated disaster scenario;
whereinthe selections are inputs for machine learning and generating the updated disaster scenario is based, at least in part, on the machine learning.
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Specification