Data security governance in universities serves as a crucial guarantee for promoting the digital transformation of higher education and can supports the sustainable high-quality development of universities, yet it is conftonted with severe contradictions and conflicts. Given the dynamic balance between conflicting sides, four pairs of contradictory tensions existed in university data security governance: institutionalization versus professionalization, security versus data sharing, alert-oriented manual supervision versus automated governance, and real-time response versus long-term sustainable management. To thoroughly understand and effectively resolve such contradictions and conflicts, a governance model for university data security can be constructed based on conflict management tools from three aspects of participation of governance stakeholders, identification of contradiction types, and conflict management models, providing theoretical guidance for reconciling tensions and handling conflicts. Finally, flexible response strategies were proposed corresponding to five conflict management modes: classified emergency classification response based on the competitive mode, adjustment of governance norms based on the accommodative mode, streamlined data administration based on the avoidance mode, construction of collaborative governance frameworks based on the cooperative mode, and selection of technical path based on the compromising mode, thereby helping universities cope with complex and evolving data security challenges.

Under the backdrop of the rapid development of generative artificial intelligence (GAI), it is urgent to recognize, prevent, and resolve the educational risks that GAI brings to the higher education ecosystem. Therefore, this paper firstly analyzed the risk representation of integrating GAI into the higher education ecosystem, which mainly manifested as the dissolution of teachers’ and students’ sovereignty, the distortion of knowledge content, the destruction of security inclusion, and the alienation of talent cultivation. Subsequently, the risk warning mechanism that integrating GAI into the higher education ecosystem was constructed, which consisted of four modules of risk warning subject, risk warning content, risk warning guarantee, and risk warning process. Finally, this paper proposed to address the risks of integrating GAI into the higher education ecosystem by stimulating subject awareness, identifying knowledge content, optimizing digital environment and cultivating innovative talents. The research in this paper could provide theoretical guidance for risk governance of GAI education, promote the safe application of GAI in higher education, and safeguard the digital transformation and high-quality development of higher education.