Modeling simulated cybersecurity attack difficulty

1. A method comprising:

at a cybersecurity training computing platform comprising at least one processor, memory, and communication interface:

retrieving data associated with a plurality of attack templates for simulating cybersecurity attacks;

using one or more machine learning models to compute, based on one or more organization-specific attributes and parameters defined in the plurality of attack templates, a predicted failure rate for each template of the plurality of attack templates in order to yield a plurality of predicted failure rates for an organization, each of the plurality of predicted failure rates corresponding to a particular one of the plurality of attack templates;

based on the plurality of predicted failure rates corresponding to the plurality of attack templates, generating a recommendation for one or more of the plurality of attack templates to configure a simulated cybersecurity attack on the organization;

sending, via the communication interface, to an administrator user device associated with the organization, information about the simulated cybersecurity attack; and

executing the simulated cybersecurity attack by contacting members of the organization using a communication specified in a first attack template.

2. The method of claim 1 , wherein executing the simulated cybersecurity attack comprises sending simulated phishing messages to the members of the organization.

3. The method of claim 1 , further comprising:

generating the one or more machine learning models to compute the predicted failure rate based on a training data set that correlates organization-specific attributes to historical cybersecurity attack outcome data indicating whether historical cybersecurity attacks were successes or failures.

4. The method of claim 1 , further comprising:

generating a linear regression model to compute a predicted failure rate for a template based on the one or more organization-specific attributes.

5. The method of claim 1 , further comprising, prior to executing the simulated cybersecurity attack using the first attack template:

customizing a training module based on information associated with the first attack template; and

providing the customized training module to the organization.

6. The method of claim 1 , wherein the one or more organization-specific attributes comprise one or more of:

a size of the organization,

an industry in which the organization operates,

a cybersecurity score of the organization, or

member data indicating attributes of members of the organization.

7. The method of claim 6 , wherein the member data indicates one or more of:

a role of each member of the organization,

whether each member of the organization has access to sensitive data,

a cybersecurity score of each member of the organization, or

whether each member of the organization previously failed a cybersecurity attack.

8. The method of claim 7 , further comprising:

updating a cybersecurity score for each member of the organization based on a response to a simulated cybersecurity attack.

9. The method of claim 8 , wherein the updating comprises:

increasing the cybersecurity score in proportion to a difficulty factor associated with the first attack template.

10. The method of claim 8 , wherein the updating comprises:

increasing the cybersecurity score based on a member reporting the simulated cybersecurity attack.

11. The method of claim 1 , further comprising:

generating a cybersecurity score for the organization;

generating one or more cybersecurity scores for one or more departments of the organization; and

generating one or more cybersecurity scores for one or more members of the organization.

12. The method of claim 1 , further comprising:

executing a second simulated cybersecurity attack against a particular department of the organization by contacting members of the department using a communication specified in a second attack template.

13. The method of claim 1 , further comprising:

executing a third simulated cybersecurity attack against a particular member of the organization by contacting the member using a communication specified in a third attack template.

14. The method of claim 1 , further comprising:

based on results of the simulated cybersecurity attack, scheduling follow-up training for one or more members of the organization;

customizing the follow-up training based on a type of the first attack template; and

providing the customized follow-up training to the members.

15. The method of claim 1 , further comprising:

generating a comparison of a simulated attack performance of the organization to performances of other organizations in a same industry as the organization; and

sending the comparison to the administrator user device associated with the organization.

16. A cybersecurity training computing platform comprising:

at least one processor;

a communication interface; and

memory storing instructions that, when executed by the at least one processor, cause the cybersecurity training computing platform to:

retrieve data associated with a plurality of attack templates for simulating cybersecurity attacks;

use one or more machine learning models to compute, based on one or more organization-specific attributes and parameters defined in the plurality of attack templates, a predicted failure rate for each template of the plurality of attack templates in order to yield a plurality of predicted failure rates for an organization, each of the plurality of predicted failure rates corresponding to a particular one of the plurality of attack templates;

based on the plurality of predicted failure rates corresponding to the plurality of attack templates, generate a recommendation for one or more of the plurality of attack templates to configure a simulated cybersecurity attack on the organization;

send, via the communication interface, to an administrator user device associated with the organization, information about the simulated cybersecurity attack; and

execute the simulated cybersecurity attack by contacting members of the organization using a communication specified in a first attack template.

17. The cybersecurity training computing platform of claim 16 , wherein executing the simulated cybersecurity attack comprises sending simulated phishing messages to the members of the organization.

18. One or more non-transitory computer-readable media comprising instructions that, when executed, cause a computing platform to:

retrieve data associated with a plurality of attack templates for simulating cybersecurity attacks;

use one or more machine learning models to compute, based on one or more organization-specific attributes and parameters defined in the plurality of attack templates, a predicted failure rate for each template of the plurality of attack templates in order to yield a plurality of predicted failure rates for an organization, each of the plurality of predicted failure rates corresponding to a particular one of the plurality of attack templates;

based on the plurality of predicted failure rates corresponding to the plurality of attack templates, generate a recommendation for one or more of the plurality of attack templates to configure a simulated cybersecurity attack on the organization;

send, via a communication interface of the computing platform, to an administrator user device associated with the organization, information about the simulated cybersecurity attack; and

execute the simulated cybersecurity attack by contacting members of the organization using a communication specified in a first attack template.

19. The one or more non-transitory computer-readable media of claim 18 , wherein executing the simulated cybersecurity attack comprises sending simulated phishing messages to the members of the organization.

20. The method of claim 1 , wherein the one or more machine learning models are trained using training data derived from real cyber attacks and derived from simulated cyber attacks used for training purposes.