In this report, we analyze real-world end-user vulnerability assessment (VA) behaviour using a machine learning (ML) algorithm to identify four distinct strategies, or “styles.” These are based on five VA key performance indicators (KPIs) which correlate to VA maturity characteristics.
This study specifically focuses on key performance indicators associated with the Discover and Assess stages of the five-phase Cyber Exposure Lifecycle. During the first phase – Discover – assets are identified and mapped for visibility across any computing environment. The second phase – Assess – involves understanding the state of all assets,
including vulnerabilities, misconfigurations, and other health indicators. While these are only two phases of a longer process, together they decisively determine the scope and pace of subsequent phases, such as prioritization and remediation.
The actual behaviour of each individual enterprise in the data set, in reality, exhibits a mixture of all VA Styles. For the purposes of this work, enterprises are assigned to the specific style group with which they most closely align. We provide the global distribution of VA Styles, as well as a distribution across major industry verticals.