Urban critical infrastructure such as electric grids, water networks and transportation systems are prime targets for cyberattacks. These systems are composed of connected devices which we call the Industrial Internet of Things (IIoT). An attack on urban critical infrastructure IIoT would cause considerable disruption to society. Supervisory Control and Data Acquisition (SCADA) systems are typically used to control IIoT for urban critical infrastructure. Despite the clear need to understand the cyber risk to urban critical infrastructure, there is no data-driven model for evaluating SCADA software risk for IIoT devices. In this paper, we compare non-SCADA and SCADA systems and establish, using cosine similarity tests, that SCADA as a software subclass holds unique risk attributes for IIoT. We then disprove the commonly accepted notion that the Common Vulnerability Scoring System (CVSS) risk metrics of Exploitability and Impact are not correlated with attack for the SCADA subclass of software. A series of statistical models are developed to identify SCADA risk metrics that can be used to evaluate the risk that a SCADA-related vulnerability is exploited. Based on our findings, we build a customizable SCADA risk prioritization schema that can be used by the security community to better understand SCADA-specific risk. Considering the distinct properties of SCADA systems, a data-driven prioritization schema will help researchers identify security gaps specific to this software subclass that is essential to our society’s operations.