Dynamic Risk Scoring

A Dynamic Risk Scoring system is a security mechanism that continuously evaluates and updates risk assessments based on real-time user behavior and contextual factors.

Unlike static risk models that rely on predetermined rules or historical data, dynamic risk scoring adapts moment-by-moment as new information becomes available about a user's actions, location, device characteristics, and environmental conditions.

These systems typically analyze multiple data points simultaneously, including login patterns, typing rhythms, mouse movements, network locations, device fingerprints, and access patterns. Machine learning algorithms process this information to generate risk scores that fluctuate throughout a user session, allowing security systems to respond appropriately to changing threat levels.

Dynamic risk scoring is particularly valuable for preventing account takeover attacks, insider threats, and unauthorized access attempts. When risk scores exceed predetermined thresholds, the system can trigger additional authentication challenges, restrict access to sensitive resources, or alert security teams. Conversely, low-risk activities can proceed with minimal friction, maintaining user productivity while ensuring security.

This approach enables organizations to implement adaptive security measures that balance protection with usability, making security decisions based on current context rather than static policies alone.