To ensure the access security of 6G, physical-layer authentication (PLA) leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for transmitters. Furthermore, the introduction of artificial intelligence (AI) facilitates the learning of the distribution characteristics of channel fingerprints, effectively addressing the uncertainties and unknown dynamic challenges in wireless link modeling. This paper reviews representative AI-enabled PLA schemes and proposes a graph neural network (GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile users. Simulation results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication accuracy. Furthermore, this paper outlines the future development directions of PLA.