MOUSE MOVEMENT, KEYSTROKES DYNAMICS, GAIT-BUILDING AN AI-POWERED CONTINUOUS IDENTITY CHECK

The century-old authentication methods are not sufficient to guarantee the continuous authentication of users in the digital interactions. This paper seeks to discuss how keystroke dynamics, mouse movements and gait, constituting behavioural biometrics, can be utilised using AI to develop a continuous identity verification system. Based on secondary data, such as case studies and quantitative analysis, the study concludes that the accuracy of real-time authentication with deep learning and machine learning models is high. Behaviour characteristics are useful in reducing fraud and promoting the safety of users. The research suggests the use of adaptive and multimodal AI systems to enhance accuracy, scalability, and the ethical requirement of continuous identity validation in a variety of digital contexts.