Systems and methods for determining a job score from a job title
Embodiments disclosed herein describe a server, for example a security awareness server or an artificial intelligence machine learning system that establishes a job score for a user based on the user's job title. In embodiments, the vulnerability of a user to malicious cybersecurity attacks, the propensity for the user to engage with a malicious attack, and the severity of a malicious attack likely to be sent to the user and the severity of the harm to the user's organization is the user engages with the malicious attack is represented in a user risk score. The risk score for a user of a security awareness system, or for a group of users of a security awareness system, may be calculated based on one or more of a frequency score for the user, a propensity score for the user, a severity score for the user, and a job score for the user.
1. A method comprising:
identifying, by one or more processors, character strings from a portion of a job title and a job function of each of a plurality of users;
establishing, by the one or more processors, a model trained with input comprising the character strings and the job function of each of the plurality of users, the model configured to output a job score responsive to an input of the job title; and
providing, by the one or more processors, the job score for a user received as output from the model responsive to providing as input to the model at least character strings of the portion of the job title of the user.
2. The method of claim 1 , further comprising providing, by the one or more processors, the job score for a user responsive to providing the job title of the user.
3. The method of claim 1 , further comprising identifying, by the one or more processors, a depth of each of the plurality of users in a hierarchy of an organization.
4. The method of claim 3 , further comprising establishing the model trained with input comprising the character strings and the job function of each of the plurality of users and depth of each of the plurality of users in the hierarchy of the organization.
5. The method of claim 1 , further comprising identifying, by the one or more processors, a height of each of the plurality of users in the hierarchy of the organization.
6. The method of claim 5 , further comprising establishing the model trained with input comprising the character strings and the job function of each of the plurality of users and the height of each of the plurality of users in the hierarchy of the organization.
7. The method of claim 1 , further comprising identifying, by the one or more processors, one of a length of time that each of the plurality of users has been with the organization or an age of the organization.
8. The method of claim 7 , further comprising establishing the model trained with input comprising the character strings and the job function of each of the plurality of users and the length of time that each of the plurality of users has been with an organization or the age of the organization.
9. A system comprising:
one or more processors, coupled to memory, and configured to:
identify character strings from a portion of a job title and a job function of each of a plurality of users;
establish a model trained with input comprising the character strings and the job function of each of the plurality of users, the model configured to output a job score responsive to an input of the job title; and
provide, the job score for a user received as output from the model responsive to providing as input to the model at least character strings of the portion of the job title of the user.
10. The system of claim 9 , wherein the one or more processors are further configured to provide the job score for a user responsive to providing the job title of the user.
11. The system of claim 9 , wherein the one or more processors are further configured to identify a depth of each of the plurality of users in a hierarchy of an organization.
12. The system of claim 11 , wherein the one or more processors are further configured to establish the model trained with input comprising the character strings and the job function of each of the plurality of users and depth of each of the plurality of users in the hierarchy of the organization.
13. The system of claim 9 , wherein the one or more processors are further configured to identify a height of each of the plurality of users in the hierarchy of the organization.
14. The system of claim 13 , wherein the one or more processors are further configured to establish the model trained with input comprising the character strings and the job function of each of the plurality of users and the height of each of the plurality of users in the hierarchy of the organization.
15. The system of claim 9 , wherein the one or more processors are further configured to identify one of a length of time that each of the plurality of users has been with the organization or an age of the organization.
16. The system of claim 15 , wherein the one or more processors are further configured to establish the model trained with input comprising the character strings and the job function of each of the plurality of users and the length of time that each of the plurality of users has been with an organization or the age of the organization.
17. A system comprising:
one or more processors configured to:
identify a job title and a phish-prone percentage for each of a plurality of users;
establish, a model trained with the job title and the phish-prone percentage of each of the plurality of users, the model configured to output a job score responsive to an input of the job title; and
provide the job score for a user received as output from the model responsive to providing the job title as input to the model.
18. The system of claim 17 , wherein the one or more processors are further configured to identify a status of security awareness training of each of the plurality of the users.
19. The system of claim 18 , wherein the one or more processors are further configured to establish the model trained with the job title and the phish-prone percentage and the status of security awareness training of each of the plurality of users.
20. The system of claim 17 , wherein the one or more processors are further configured to establish the model trained with the job title and the phish-prone percentage and breach data identified for each of the plurality of users.