Selectively prioritizing alerts received for an advanced cybersecurity threat prioritization system

A computer program product, comprising a computer readable storage medium having program instructions embodied therewith, the program instructions readable by a processor, executable by the processor, or readable and executable by the processor, to cause the processor to, in response to detecting a current cybersecurity event:

• cause a historical risk score to be generated for the current cybersecurity event, wherein the current cybersecurity event comprises a real-time event impacting availability of information assets belonging to an organization accessed through a network;
• cause a sigma rule detection score to be generated for the current cybersecurity event;
• cause an anomaly risk score to be generated for the current cybersecurity event;
• cause an Indicator of Compromise (IoC) score to be generated for the current cybersecurity event;
• and
• use a machine learning model to:
• evaluate the historical risk score, the sigma rule detection score, the anomaly risk score, and the IoC score to determine a predicted threat of impact on the information assets, and
• create a consolidated risk score corresponding to weighted values for:
• the current cybersecurity event, the consolidated risk score incorporating the historical risk score, the sigma rule detection score, the anomaly risk score, and the IoC score based on the predicted threat of impact.

An advanced threat prioritization system, comprising:

• a processor;
• and
• logic integrated with the processor, executable by the processor, or integrated with and executable by the processor, the logic being configured to, in response to detecting a current cybersecurity event:
• cause a historical risk score to be generated for the current cybersecurity event, wherein the current cybersecurity event comprises a real-time event impacting availability of information assets belonging to an organization accessed through a network;
• cause a sigma rule detection score to be generated for the current cybersecurity event;
• cause an anomaly risk score to be generated for the current cybersecurity event;
• cause an Indicator of Compromise (IoC) score to be generated for the current cybersecurity event;
• and
• use a machine learning model to:
• evaluate the historical risk score, the sigma rule detection score, the anomaly risk score, and the IoC score to determine a predicted threat of impact on the information assets, and
• create a consolidated risk score corresponding to weighted values for:
• the current cybersecurity event, the consolidated risk score incorporating the historical risk score, the sigma rule detection score, the anomaly risk score, and the IoC score based on the predicted threat of impact.