Adaptive Remediation of Multivariate Risk

• US 20190036971A1
• Filed: 07/25/2018
• Published: 01/31/2019
• Est. Priority Date: 07/26/2017
• Status: Active Grant

• Current Assignee: Forcepoint LLC
• Original Assignee: Forcepoint LLC
• Inventors: Ford, Richard A., Irvine, Ann, Reeve, Adam
• Granted Patent: US 10,642,996 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 11/3072 where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
• CPC Class Codes: G06F 11/3438 monitoring of user actions tracking the activity of the user H04L67/535
• CPC Class Codes: G06F 21/552 involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/577 Assessing vulnerabilities and evaluating computer system security
• CPC Class Codes: G06F 21/602 Providing cryptographic facilities or services
• CPC Class Codes: G06F 21/6245 Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/6254 by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 21/84 output devices, e.g. displays or monitors
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06F 2221/031 Protect user input by software means
• CPC Class Codes: G06F 2221/032 Protect output to user by software means
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: H04L 2209/16 Obfuscation or hiding, e.g. involving white box
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection
• CPC Class Codes: H04L 63/1433 Vulnerability analysis
• CPC Class Codes: H04L 63/1441 Countermeasures against malicious traffic countermeasures against attacks on cryptographic mechanisms H04L9/002
• CPC Class Codes: H04L 63/20 for managing network security; network security policies in general filtering policies H04L63/0227
• CPC Class Codes: H04L 67/025 for remote control or remote monitoring of applications
• CPC Class Codes: H04L 67/141 Setup of application sessions admission control or resource allocation in data switching networks H04L47/70
• CPC Class Codes: H04L 67/146 : Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
• CPC Class Codes: H04L 67/289 : Intermediate processing functionally located close to the data consumer application, e.g. in same machine, in same home or in same sub-network
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A method, system and computer-usable medium for adaptively remediating multivariate risk, comprising: detecting a violation of a multivariate security policy, the multivariate security policy comprising a plurality of variables; identifying a variable from the plurality of variables associated with a cause of the violation; associating an entity with the variable associated with the cause of the violation; and, adaptively remediating a risk associated with the entity.

Claims

1. A computer-implementable method for adaptively remediating multivariate risk, comprising:

detecting a violation of a multivariate security policy, the multivariate security policy comprising a plurality of variables;

identifying a variable from the plurality of variables associated with a cause of the violation;

associating an entity with the variable associated with the cause of the violation; and

,

adaptively remediating a risk associated with the entity.

2. The method of claim 1, further comprising:

configuring the multivariate security policy by setting parameters of the plurality of variables.

3. The method of claim 1, wherein:

the multivariate security policy comprises a rule and an action associated with a particular user; and

,

each of the plurality of variables is associated with the rule and the action of the multivariate security policy for the particular user.

4. The method of claim 3, wherein:

at least some of the plurality of variables comprise risk-adaptive user behavior factors.

5. The method of claim 1, wherein:

detecting the violation of a multivariate security policy comprises analyzing a plurality of combinations of variables and events.

6. The method of claim 5, wherein:

analyzing the plurality of combinations of variables and events comprise at least one of

analyzing two sets of variables associated with a single entity;

analyzing two sets of variables associated with two entities; and

,

analyzing one set of variables associated with two entities.

7. A system comprising:

a processor;

a data bus coupled to the processor; and

a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for;

detecting a violation of a multivariate security policy, the multivariate security policy comprising a plurality of variables;

identifying a variable from the plurality of variables associated with a cause of the violation;

associating an entity with the variable associated with the cause of the violation; and

,

adaptively remediating a risk associated with the entity.

8. The system of claim 7, wherein the instructions executable by the processor are further configured for:

configuring the multivariate security policy by setting parameters of the plurality of variables.

9. The system of claim 7, wherein:

the multivariate security policy comprises a rule and an action associated with a particular user; and

,

each of the plurality of variables is associated with the rule and the action of the multivariate security policy for the particular user.

10. The system of claim 9, wherein:

at least some of the plurality of variables comprise risk-adaptive user behavior factors.

11. The system of claim 7, wherein:

detecting the violation of a multivariate security policy comprises analyzing a plurality of combinations of variables and events.

12. The system of claim 11, wherein:

analyzing the plurality of combinations of variables and events comprise at least one of

analyzing two sets of variables associated with a single entity;

analyzing two sets of variables associated with two entities; and

,

analyzing one set of variables associated with two entities.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

detecting a violation of a multivariate security policy, the multivariate security policy comprising a plurality of variables;

identifying a variable from the plurality of variables associated with a cause of the violation;

associating an entity with the variable associated with the cause of the violation; and

,

adaptively remediating a risk associated with the entity.

14. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are further configured for:

configuring the multivariate security policy by setting parameters of the plurality of variables.

15. The non-transitory, computer-readable storage medium of claim 13, wherein:

the multivariate security policy comprises a rule and an action associated with a particular user; and

,

each of the plurality of variables is associated with the rule and the action of the multivariate security policy for the particular user.

16. The non-transitory, computer-readable storage medium of claim 15, wherein:

at least some of the plurality of variables comprise risk-adaptive user behavior factors.

17. The non-transitory, computer-readable storage medium of claim 13, wherein:

detecting the violation of a multivariate security policy comprises analyzing a plurality of combinations of variables and events.

18. The non-transitory, computer-readable storage medium of claim 17, wherein:

analyzing the plurality of combinations of variables and events comprise at least one of

analyzing two sets of variables associated with a single entity;

analyzing two sets of variables associated with two entities; and

,

analyzing one set of variables associated with two entities.

19. The non-transitory, computer-readable storage medium of claim 13, wherein:

the computer executable instructions are deployable to a client system from a server system at a remote location.

20. The non-transitory, computer-readable storage medium of claim 13, wherein:

the computer executable instructions are provided by a service provider to a user on an on-demand basis.