The accelerating digitization of financial services, mobile computing, and interconnected platforms has fundamentally transformed how identity is established, verified, and protected. Traditional authentication mechanisms, largely dependent on static credentials such as passwords, personal identification numbers, or one-time tokens, have proven increasingly insufficient in the face of sophisticated cyber threats, insider misuse, and usability constraints. Against this backdrop, behavioral biometrics has emerged as a critical paradigm for continuous, implicit, and user-centric authentication. Behavioral biometrics leverages unique patterns embedded in human interaction with digital systems, including keystroke dynamics, mouse movements, touchscreen gestures, gait, and contextual device usage. Unlike physiological biometrics, behavioral traits evolve over time, offering both opportunities and challenges for adaptive security architectures.

Recent advances in artificial intelligence and machine learning have catalyzed a new generation of behavioral biometric systems capable of learning from streaming data, accommodating behavioral drift, and operating unobtrusively across heterogeneous platforms. These developments are particularly salient in high-stakes domains such as retirement account management, mobile identity, and Internet of Things environments, where persistent authentication is essential yet user tolerance for friction is minimal. The integration of AI-driven behavioral biometrics into financial systems, exemplified by emerging research on retirement account security, underscores the strategic importance of continuous authentication as both a technical and socio-ethical endeavor (Valiveti, 2025).

This article presents an extensive, theory-driven research analysis of AI-driven behavioral biometric authentication systems, synthesizing foundational biometric theory, contemporary machine learning methodologies, and applied security research. Drawing exclusively on established scholarly literature, the study develops a comprehensive conceptual framework for continuous authentication, critically evaluates methodological approaches, and interprets findings through comparative and interdisciplinary lenses. Particular emphasis is placed on privacy preservation, incremental learning, adversarial robustness, and regulatory implications. By articulating both the promise and limitations of behavioral biometrics, this work contributes a publication-ready, integrative perspective aimed at researchers, system architects, and policymakers navigating the evolving landscape of digital identity security.