In an era defined by ubiquitous digital interactions and the growing menace of synthetic media, traditional forensics is rapidly evolving into a dynamic, data-driven discipline. One of the most promising developments in this transformation is the integration of behavioral biometrics into multimedia forensics. Behavioral biometrics—patterns in how individuals interact with devices—are inherently difficult to mimic or forge and offer a new dimension of identity validation in cyber investigations. The SU-AIS BB-MAS Dataset, a large-scale collection of behavioral data compiled by Syracuse University and Assured Information Security, exemplifies this new frontier. With over 62 million data points including keystrokes, swipes, and motion data, it serves as a critical resource for forensic researchers, security analysts, and law enforcement agencies. Recognized by IEEE Dataport as one of the three most popular datasets on their portal, SU-AIS BB-MAS has already gained global attention for its scale, quality, and potential impact on AI-based forensic research. This article explores the emerging role of AI-powered behavioral biometrics in digital forensics and their potential to shape the future of cybercrime investigation and justice delivery.