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)　fa⁃　cilitates　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.　©　2025　ZTE　Communications.　All　rights　reserved.