The interplay of data sovereignty and the alienation of algorithmic governance present new challenges for national audit. Based on the governance function of national audit, this study constructs a tripartite collaborative framework of “law-technology-audit”: the law clarifies the sovereignty hierarchy and compliance bottom line, technology enables penetrating verification through federated learning and blockchain, and audit performs supervision functions relying on dynamic authorization and gradient exemption mechanisms. This study innovatively proposes a closed-loop governance logic of “scenario-based identification-elastic response” to adapt to diverse governance needs. In practice, it integrates federated learning with blockchain to form an intelligent audit model characterized by “codified rules, traceable evidence, and distributed verification.” This model promotes the localization of the ISAE 3000 standard and innovates cross-border data flow audits, providing theoretical support and practical pathways for enhancing the adaptability of national audit in digital governance and advancing institutional openness.