Building Predictive Identity Threat Models Using Behavioral Biometrics and Zero Trust Cybersecurity Principles

Source abstract

This review explores the integration of behavioral biometrics and Zero Trust cybersecurity principles in building predictive identity threat models for advanced cyber defense. As traditional perimeter-based security becomes increasingly ineffective against sophisticated identity-based attacks, the fusion of behavioral biometrics—such as keystroke dynamics, mouse movement, touchscreen behavior, and gait recognition—with Zero Trust frameworks offers a more dynamic and continuous method of user authentication and threat prediction. By leveraging machine learning algorithms to analyze behavioral patterns in real time, organizations can identify anomalies indicative of account compromise, insider threats, or unauthorized access. Zero Trust, which operates on the principle of “never trust, always verify,” ensures that every user and device is continuously authenticated, authorized, and validated based on risk context and behavioral insights. This paper synthesizes the state-of-the-art technologies, architectures, and analytical techniques underpinning predictive identity threat modeling. It also examines use cases across sectors, implementation challenges, and future directions for securing digital identities in an era of pervasive cyber threats.