A quantitative method for assessing and ranking the likelihood and potential impact of security threats, typically assigning numerical values to help prioritize response efforts.
Risk scoring for insider threats combines multiple factors including user behavior patterns, access privileges, data sensitivity, and contextual information to calculate dynamic risk scores. Modern systems use machine learning to continuously adjust scores based on real-time activities and environmental factors. Ponemon Institute's 2025 research indicates that organizations using dynamic risk scoring respond to high-risk insider activities 65% faster than those relying on static risk assessments.
The process of identifying, analyzing, and evaluating risks to determine their potential impact and likelihood of occurrence.
The use of statistical analysis and machine learning to identify patterns in user behavior and detect anomalies that may indicate security threats or policy violations.
A structured approach to identifying, analyzing, and mitigating potential threats to systems, applications, or business processes.