In the field of biosimilar drug development, artificial intelligence provides critical technological support for overcoming complexities, lengthy cycles, and regulatory constraints related to “high similarity.” However, the generation of massive and diverse data has given rise to compliance risks such as ambiguous data ownership, insufficient transparency, and cross-border standard discrepancies. These issues threaten data security and hinder R&D efficiency. To address this, we must first systematically deconstruct the core issues of compliance, transforming abstract risks into quantifiable variables. Drawing on the hierarchical logic of the DIKWP AI model and the interactive logic of its three cognitive spaces, we construct a multidimensional mathematical regulatory model featuring synergistic functions to clarify risk transmission pathways and regulatory targets. Simultaneously, drawing on the full lifecycle regulatory experience of the U.S. FDA and European EMA in pharmaceutical data, this approach integrates “legal norms + intelligent technology” to construct a regulatory framework. This provides a theoretically coherent and practically forward-looking solution for enabling AI-driven data security protection in biosimilar drug development, alleviating the tension between “technological empowerment” and “regulatory constraints,” and facilitating lawful and efficient R&D advancement.