Applying Machine Learning to Optimize the Correlation of SecurityScorecard Scores with Relative Likelihood of Breach
SecurityScorecard ratings provide a means for objectively monitoring the cybersecurity hygiene of organizations (including their vendors) and gauging whether their security posture is improving or deteriorating over time. The ratings are valuable for vendor risk management programs, determining risk premiums for cyber insurance, executive-level and board reporting, enterprise cyber risk management (self-monitoring), and for assessing compliance with cybersecurity risk frameworks.
We conducted a study, investigating the use of Machine Learning (ML) to tune the weighting of each of the risk factors so that the total score is optimally correlated with the relative likelihood of incurring a data breach. Download the white paper to learn more.