Systems and methods for personalizing security incident reports

A system for personalizing security incident reports, the system comprising:

• a generation module, stored in memory, that generates, within a training dataset, a feature vector for each of a group of security incidents, the feature vector including features that describe the security incidents and the features including response codes that a set of clients assigned to the security incidents as labels;
• a training module, stored in memory, that trains a supervised machine learning function on the training dataset using the response codes that the set of clients assigned to the security incidents such that the supervised machine learning function learns how to predict an assignment of future response codes to future security incidents;
• an application module, stored in memory, that applies the trained supervised machine learning function to a feature vector that describes a new security incident, as one of the future security incidents, on the set of clients to predict that the set of clients will ignore the new security incident;
• a personalizing module, stored in memory, that personalizes a list of security incidents that is electronically reported to the set of clients by deprioritizing the new security incident based on applying the trained supervised machine learning function to the new security incident;
• and
• at least one physical processor configured to execute the generation module, the training module, the application module, and the personalizing module.

A non-transitory computer-readable medium comprising one or more computer-readable instructions that, when executed by at least one processor of a computing device, cause the computing device to:

• generate, within a training dataset, a feature vector for each of a group of security incidents, the feature vector including features that describe the security incidents and the features including response codes that a set of clients assigned to the security incidents as labels;
• train a supervised machine learning function on the training dataset using the response codes that the set of clients assigned to the security incidents such that the supervised machine learning function learns how to predict an assignment of future response codes to future security incidents;
• apply the trained supervised machine learning function to a feature vector that describes a new security incident, as one of the future security incidents, on the set of clients to predict that the set of clients will ignore the new security incident;
• and
• personalize a list of security incidents that is electronically reported to the set of clients by deprioritizing the new security incident based on applying the trained supervised machine learning function to the feature vector that describes the new security incident.