Abstract: Generating a cybersecurity risk model using sparse data is disclosed, including: obtaining signals associated with a cybersecurity risk, wherein the obtained signals include technographic signals and query derived signals obtained from queries; generating pseudo signals based at least in part on a priori factors relating to the cybersecurity risk; and combining the pseudo signals and the obtained signals into a Bayesian model indicating the cybersecurity risk.

Abstract: A technique for assessing and quantifying a cyber security risk is provided that includes a cyber resilience platform for obtaining reliable data from a vast set of available metrics, standardizing the data, and extracting meaning from the data to inform cyber security decisions. The disclosed technique provides visibility of a dynamic cyberspace landscape by identifying key factors that impact types of cyber security risk. These key factors can be used to distinguish between users of different risk types for various cyber threats and to identify actions affecting loss probabilities associated with a cyber threat. Strengths and weaknesses in a customer's cyber security profile are identified, including configuration settings that impact the effectiveness of cyber security protection measures and a cyber security score indicative of the customer's risk exposure.