Effective prioritization of vulnerabilities is essential to staying ahead of your attackers. While your threat intelligence might expose a wealth of information about attackers and attack paths, integrating it into decision-making is no easy task. Too often, we make the mistake of taking the data given to us for granted – and this has disastrous consequences.
We'll explain what we miss by trusting CVSS scores, and what should absolutely be taken into consideration to focus on the vulnerabilities posing the greatest risks to our organizations. We'll look at tens of thousands of vulnerabilities, CVSS scores, CVE, NVD, scraping mailing lists, collecting data feeds and ultimately end up with a few dozen data points that helped us understand the probability of a vulnerability being exploited.
Finally, we'll use all that data as well as billions of in-the-wild events collected over 5 years in order to create a machine learning model for predicting the probability of a vulnerability being exploited, a scoring system which outperforms CVSS on every metric: accuracy, efficiency and coverage.