As intelligent audit becomes increasingly integrated into audit practices, data security concerns have gradually come to the forefront. Current data security governance focuses primarily on protecting data entities, lacking dynamic assessment and continuous optimization mechanisms for the entire data lifecycle and all its elements. This approach struggles to meet security requirements in intelligent audit scenarios. Therefore, leveraging a data security maturity model, we propose a data security governance model tailored for intelligent audit. This framework encompasses three dimensions: data security processes, security capabilities, and maturity levels. This model integrates security capability elements including organizational, institutional, technological, and personnel aspects across five stages of audit data: collection, preprocessing, analysis, lead verification, and report generation. It establishes a five-level maturity to guide governance pathways. While the model demonstrates strong structural versatility, challenges in adaptability persist for complex scenarios such as industry-specific customization, cross-organizational collaboration, and cross-border data auditing. Further development of adaptation mechanisms and evaluation methodologies is required.