Systems and methods for determining individual and group risk scores
Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a risk score or vulnerable for a user of a security awareness system, or for a group of users of a security awareness system. The server may create a frequency score for a user, which predicts the frequency at which the user is to be hit with a malicious attack. The frequency score may be based on at least a job score, which may be represented by a value that is based on the type of job the user has, and a breach score that may be represented by a value that is based on the user's level of exposure to email.
1. A method comprising:
identifying, by one or more servers, results of at least one or more user assessment surveys for a user and whether the user clicks on one or more links of one or more simulated phishing tests;
receiving, by the one or more servers, data from a security services provider;
determining, by the one or more servers, a risk score of the user as a function of the data from the security services provider, whether the user clicks on one or more links of one or more simulated phishing tests and the results of the at least one or more user assessment surveys for the user; and
causing, by the one or more servers, display of the risk score of the user.
2. The method of claim 1 , further comprising identifying, by the one or more servers, a training history of the user.
3. The method of claim 2 , further determining, by the one or more servers, the risk score of the user as the function of the data from the security services provider, whether the user clicks on one or more links of the one or more simulated phishing tests, and the training history of the user.
4. The method of claim 1 , wherein the security service provider comprises one of a security information management (SIM) system, security event management (SEM) system, or security information and event management (SIEM) system.
5. The method of claim 1 , wherein the function is a weighted function or a logarithmic function.
6. The method of claim 1 , further comprising associating, by the one or more servers, the risk score of the user with data identifying activity of the user from the security services provider.
7. The method of claim 1 , further comprising interacting, by the one or more servers, with the security services provider.
8. The method of claim 1 , wherein the data from the security services provider comprises one of data from real-time monitoring, security records or external threat data.
9. A system comprising:
one more processors, coupled to memory and configured to:
identify results of at least one or more user assessment surveys for a user and whether the user clicks on one or more links of one or more simulated phishing tests;
receive data from a security services provider;
determine a risk score of the user as a function of the data from the security services provider, whether the user clicks on one or more links of one or more simulated phishing tests and the results of the at least one or more user assessment surveys for the user; and
cause display of the risk score of the user.
10. The system of claim 9 , wherein the one or more processors are further configured to identify a training history of the user.
11. The system of claim 10 , wherein the one or more processors are further configured to determine the risk score of the user as the function of the data from the security services provider, whether the user clicks on one or more links of the one or more simulated phishing tests, and the training history of the user.
12. The system of claim 9 , wherein the security service provider comprises one of a security information management (SIM) system, security event management (SEM) system, or security information and event management (SIEM) system.
13. The system of claim 9 , wherein the function is a weighted function or a logarithmic function.
14. The system of claim 9 , wherein the one or more processors are further configured to associate the risk score of the user with data identifying activity of the user from the security services provider.
15. The system of claim 9 , wherein the one or more processors are further configured to interact with the security services provider.
16. The system of claim 9 , wherein the data from the security services provider comprises one of data from real-time monitoring, security records or external threat data.
17. A method comprising:
identifying, by one or more servers, a training history of a user and whether the user clicks on one or more links of one or more simulated phishing tests;
receiving, by the one or more servers, data from a security services provider;
determining, by the one or more servers, a risk score of the user as a function of the data from the security services provider, whether the user clicks on one or more links of one or more simulated phishing tests and the training history of the user; and
causing, by the one or more servers, display of the risk score of the user.
18. The method of claim 17 , further comprising determining, by the one or more servers, the risk score as the function of results of at least one or more user assessment surveys for the user.
19. A system comprising:
one more processors, coupled to memory and configured to:
identify a training history of a user and whether the user clicks on one or more links of one or more simulated phishing tests;
receive data from a security services provider;
determine a risk score of the user as a function of the data from the security services provider, whether the user clicks on one or more links of one or more simulated phishing tests and the training history of the user; and
cause display of the risk score of the user.
20. The system of claim 19 , wherein the one or more processors are further configured to determine the risk score of the user as the function of the training history of the user.