Granted Patent B2

US 12,367,292 · App. 18/399,422 · Granted Jul 22, 2025

Inventors: Prithvi Narayana Rao (Allen, TX); Pramod Goyal (Ahmedabad, IN)

Assignee: CITIBANK, N.A.

G06F21/577G06F21/552

Abstract

Claims (61)

1. A system for providing variable identification of end-to-end computing system security impact information via user interfaces, the system comprising:

at least one processor; and

at least one memory coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations comprising:

receiving, at a user interface, a user calibration of a security vulnerability element, wherein the user calibration adjusts the security vulnerability element to a position indicative of a security condition related to identifying impacted assessment domains of computing system threats;

determining, based on a processing of a network operation from a first device to a second device, a set of computing system components that are associated with the processing of the network operation;

receiving response data from each computing system component of the set of computing system components associated with one or more security-attributes of the respective computing system component;

determining a set of security vulnerabilities associated with each computing system component of the set of computing system components using (i) a third-party resource and (ii) the response data;

applying a model to generate a set of impacted assessment domains associated with the set of computing system components using the set of security vulnerabilities; and

generating for display at the user interface, a visual representation of the set of impacted assessment domains satisfying the security condition of the user calibration.

2. The system of claim 1 , further storing instructions that, when executed by the at least one processor, perform operations comprising:

training the model on training data comprising a set of labeled feature vectors, wherein each labeled feature vector of the set of labeled feature vectors indicates labels of (i) a given security vulnerability, (ii) a given computing system component, and (iii) a given impacted assessment domain.

3. The system of claim 1 , further storing instructions that, when executed by the at least one processor, perform operations comprising:

applying a second model to generate a set of security mitigation actions using (i) the set of impacted assessment domains and (ii) the set of security vulnerabilities, wherein each security mitigation action of the set of security mitigation actions is associated with a respective impacted assessment domain and a respectively corresponding security vulnerability;

determining an assessment domain impact level for each impacted assessment domain of the set of impacted assessment domains; and

in response to the assessment domain impact level for a respective impacted assessment domain of the set of impacted assessment domains satisfying a threshold assessment domain impact level, configuring a network component to apply a respective security mitigation action to one or more computing system components associated with the respective impacted assessment domain.

4. The system of claim 3 , further storing instructions that, when executed by the at least one processor, perform operations comprising:

training the second model on second training data comprising a second set of labeled feature vectors, wherein each labeled feature vector of the second set of labeled feature vectors indicates labels of (i) a given impacted assessment domain, (ii) a given security vulnerability, and (iii) a given security mitigation action.

5. The system of claim 4 , wherein the given security mitigation action is based on a platform-specific policy.

6. The system of claim 1 , further storing instructions that, when executed by the at least one processor, perform operations comprising:

receiving, at the user interface, a second user calibration of the security vulnerability element, wherein the second user calibration adjusts the security vulnerability element to a second position indicative of the security condition related to identifying security vulnerabilities; and

in response to receiving the second user calibration of the security vulnerability element, updating the visual representation of the set of impacted assessment domains satisfying the security condition of the second user calibration.

7. The system of claim 1 , wherein the response data is security-response data received in response to the processing of the network operation.

8. A method for providing user-induced variable identification of end-to-end computing system security impact information via user interfaces, the method comprising:

receiving a user calibration of a security vulnerability element, wherein the user calibration adjusts the security vulnerability element to a position indicative of a security condition related to identifying impacted computing-aspects of computing system threats;

determining, based on a processing of a network operation associated with a computing system, a set of computing system components that are associated with the processing of the network operation;

determining a set of security vulnerabilities associated with each computing system component of the set of computing system components using a third-party resource;

generating a set of impacted computing-aspects associated with the set of computing system components using the set of security vulnerabilities; and

generating for display at a user interface, a visual representation of the set of impacted computing-aspects satisfying the security condition of the user calibration.

9. The method of claim 8 , further comprising:

training a model on training data comprising a set of labeled feature vectors, wherein each labeled feature vector of the set of labeled feature vectors indicates labels of (i) a given security vulnerability, (ii) a given computing system component, and (iii) a given impacted computing-aspect; and

generating the set of impacted computing-aspects associated with the set of computing system components, via the model, using the set of security vulnerabilities.

10. The method of claim 8 , further comprising:

applying a model to generate a set of security mitigation actions using (i) the set of impacted computing-aspects and (ii) the set of security vulnerabilities, wherein each security mitigation action of the set of security mitigation actions is associated with a respective impacted computing-aspect and a respectively corresponding security vulnerability;

determining an computing-aspect impact level for each impacted computing-aspect of the set of impacted computing-aspects; and

in response to the computing-aspect impact level for a respective impacted computing-aspect of the set of impacted computing-aspects satisfying a threshold computing-aspect impact level, configuring a network component to apply a respective security mitigation action to one or more computing system components associated with the respective impacted computing-aspect.

11. The method of claim 10 , further comprising:

training the model on second training data comprising a second set of labeled feature vectors, wherein each labeled feature vector of the second set of labeled feature vectors indicates labels of (i) a given impacted computing-aspect, (ii) a given security vulnerability, and (iii) a given security mitigation action.

12. The method of claim 8 , further comprising:

receiving, at the user interface, a second user calibration of the security vulnerability element, wherein the second user calibration adjusts the security vulnerability element to a second position indicative of the security condition related to identifying security vulnerabilities; and

in response to receiving the second user calibration of the security vulnerability element, updating the visual representation of the set of impacted computing-aspects satisfying the security condition of the second user calibration.

13. The method of claim 8 , wherein security-response data is received in response to the processing of the network operation and used to further determine the set of security vulnerabilities.

14. One or more non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause operations comprising:

receiving a user calibration of a security vulnerability element, wherein the user calibration adjusts the security vulnerability element to a position indicative of a security condition related to identifying impacted computing-aspects of computing system threats;

determining, based on a processing of a network operation associated with a computing system, a set of computing system components that are associated with the processing of the network operation;

determining a set of security vulnerabilities associated with each computing system component of the set of computing system components using a third-party resource;

generating a set of impacted computing-aspects associated with the set of computing system components using the set of security vulnerabilities; and

generating for display at a user interface, a visual representation of the set of impacted computing-aspects satisfying the security condition of the user calibration.

15. The medium of claim 14 , wherein the operations further comprise:

training a model on training data comprising a set of labeled feature vectors, wherein each labeled feature vector of the set of labeled feature vectors indicates labels of (i) a given security vulnerability, (ii) a given computing system component, and (iii) a given impacted computing-aspect; and

generating the set of impacted computing-aspects associated with the set of computing system components, via the model, using the set of security vulnerabilities.

16. The medium of claim 14 , wherein the operations further comprise:

applying a model to generate a set of security mitigation actions using (i) the set of impacted computing-aspects and (ii) the set of security vulnerabilities, wherein each security mitigation action of the set of security mitigation actions is associated with a respective impacted computing-aspect and a respectively corresponding security vulnerability;

determining an computing-aspect impact level for each impacted computing-aspect of the set of impacted computing-aspects; and

in response to the computing-aspect impact level for a respective impacted computing-aspect of the set of impacted computing-aspects satisfying a threshold computing-aspect impact level, configuring a network component to apply a respective security mitigation action to one or more computing system components associated with the respective impacted computing-aspect.

17. The medium of claim 16 , wherein the operations further comprise:

training the model on second training data comprising a second set of labeled feature vectors, wherein each labeled feature vector of the second set of labeled feature vectors indicates labels of (i) a given impacted computing-aspect, (ii) a given security vulnerability, and (iii) a given security mitigation action.

18. The medium of claim 17 , wherein the given security mitigation action is based on a platform-specific policy.

19. The medium of claim 14 , wherein the operations further comprise:

receiving, at the user interface, a second user calibration of the security vulnerability element, wherein the second user calibration adjusts the security vulnerability element to a second position indicative of the security condition related to identifying security vulnerabilities; and

in response to receiving the second user calibration of the security vulnerability element, updating the visual representation of the set of impacted computing-aspects satisfying the security condition of the second user calibration.

20. The medium of claim 14 , wherein security-response data is received in response to the processing of the network operation and used to further determine the set of security vulnerabilities.

Assignments (1)

Continuity (4)

References Cited (225)

Cited By (1)

US 12,712,909