AI-Driven Identity Verification and Authentication in Networks: Enhancing Accuracy, Speed, and Security through Biometrics and Behavioral Analytics

Abstract

The rising complexity and magnitude of the digital networks have escalated the need to handle challenging, reliable and collective systems of identity verification and authentication processes. Historical methods, including passwords, and one-time multi-factor authentication, are failing to meet the recent complexity of cyber threats and the usability demands. The following paper will discuss the changes that AI and ML technologies bring to identity verification and authentication of users of network environments. Being able to use biometric modalities (e.g., facial recognition, user scanning, and voice identification) and behavioral analytics (e.g., keystroke dynamics and user activity patterns), AI systems can carry out real-time, adaptive, and continuous authentication with a greater degree of exactitude and decreased policies. The work investigates state-of-the-art frameworks and algorithms, presents real-world examples of their usage in enterprise security and digital onboarding and focuses on the problems of bias, privacy issues, adversarial weaknesses as well as model drift. The paper will end by discussing the future research directions involving privacy preserving machine learning, explainable authentication systems, as well as the combination of decentralized identity models. These trends place the AI as one of the enabling factors supportive of secure and seamless user-friendly management of identities within next-generation network infrastructures.