近年来，AI深度嵌入图书馆业务场景，使数据安全面临传统防护体系难以覆盖的新型风险。基于此，本研究关注了制度规范、技术部署与运营管理的三类管理现状，具体归纳了数据融合、算法黑箱及外部依赖三类核心风险。并针对性提出了隐私计算部署、算法安全审计、供应链管理的应对建议。研究结论可为图书馆构建主动防御型数据安全体系提供实践指引，以数据安全治理能力助力智慧图书馆高质量发展。

In recent years, artificial intelligence has been deeply integrated into various business scenarios of libraries, bringing new data security risks that traditional protection systems are difficult to cope with. Focusing on the current situation from three aspects including institutional norms, technical deployment and operational management, this paper summarizes three major core risks in libraries: data fusion risks, algorithm black box risks and external dependency risks. Corresponding countermeasures are put forward, such as deploying privacy computing technology, implementing algorithm security auditing, and optimizing supply chain management. The research findings can provide practical references for libraries to establish a proactive defense-oriented data security system, and boost the high-quality development of smart libraries through the improvement of data security governance capability.