With the deep integration of digital technology and the real economy, AI auditing has emerged as a core paradigm that breaks through the pain points of traditional auditing, such as “sampling limitations, post-event lag, and reliance on manual labor”. This paper systematically reviews the theoretical connotations of AI auditing, reveals its current practical status, deeply analyzes four core challenges: data quality, ethical compliance, talent adaptation, and institutional synergy, and proposes feasible development paths from four dimensions: technological optimization, institutional construction, talent cultivation, and industry synergy. The research indicates that AI auditing needs to be “based on data elements, driven by technological innovation, with institutional guarantees as the bottom line, and talent adaptation as the core”, and achieve an upgrade from “tool assistance” to “governance synergy” under the promotion of new productive forces.