Interactions with various technological systems, such as smartphones, smartwatches, ATMs, and vehicles, are routinely experienced in the ever-advancing technological era. However, it has been acknowledged that traditional authentication methods like PINs and passwords are susceptible to human errors and biases. Consequently, Behavioral Biometrics (BB) has been explored as a potential alternative, offering a more passive form of authentication. Typically, BB is integrated with multifactor authentication (MFA), where multiple verification forms must be provided before access is granted. In this article, gait analysis, which utilizes data from accelerometers and gyroscopes, will be discussed. The efficacy of traditional machine learning methods in interpreting this gait data will be examined, and the viability of gait-based BB in bolstering customer authentication through MFA will be assessed.