世界科技研究与发展 ›› 2024, Vol. 46 ›› Issue (4): 560-574.doi: 10.16507/j.issn.1006-6055.2024.07.006

• 人工智能 • 上一篇    

工业智能安全治理革新与风险防范体系探析

王娟娟1 彭友2 张春飞1   

  1. 1.中国信息通信研究院,北京 100191;2.中国政法大学数据法治研究院,北京 100088
  • 出版日期:2024-08-25 发布日期:2024-09-03

Analysis of Innovation Governance and Risk Prevention System for Industrial Intelligence Security

WANG Juanjuan1   PENG You2   ZHANG Chunfei1   

  1. 1. China Academy of Information and Communications Technology, Beijing 100191, China; 2. The Institute for Data Law of China University of Political Science and Law, Beijing 100088, China
  • Online:2024-08-25 Published:2024-09-03

摘要:

工业数智化是新质生产力发展的关键驱动力。随着人工智能深度嵌入工业流程,工业智能安全治理以可靠性、数据质量与软硬件统筹治理为新重点。目前,与之相应的事后救济制度、数据安全制度、网络安全制度等治理手段在工业智能治理中呈现了显著的不适应性,难以满足安全保障的需求。工业智能作为风险社会的典型表征,实现其善治需要依据风险预防原则对现有制度进行改造。工业智能可靠性保障应在物的瑕疵担保责任和产品责任制度的基础上设置预防性义务,将瑕疵与缺陷的认定难题转化为义务违反的判断;数据质量治理应以建立健全“国家-地方-行业”的多层次标准体系为重点,实现企业间数据质量规范兼容;软硬件统筹治理应通过人工智能赋能风险预防,切实提升软硬件风险评估、监测预警与应对处置的效能。构建预防与应对相互配合的安全风险治理体系,方能有力护航我国工业数智化转型和新型工业化行稳致远。

关键词: 工业数智化, 安全治理, 风险预防, 可靠性, 数据质量, 软硬件统筹治理

Abstract:

Industrial digital intelligence is a key driver for the development of new quality productive forces. With the deep embedding of artificial intelligence into industrial processes, industrial intelligence security governance has taken reliability, data quality and integrated software and hardware governance as its new focus. At present, the corresponding governance means are after-the-fact relief systems, data security systems, and network security systems, which present a significant maladaptation in industrial intelligence governance and are challenging to meet security needs. Industrial intelligence is a typical manifestation of risk society, and realizing its good governance requires the transformation of the existing system based on the principle of a risk prevention. Industrial intelligence reliability assurance should set up preventive obligations based on the warranty liability for defects system and product liability system and transform the difficulty of identifying defects and deficiencies into the judgment of obligation breaches; the data quality governance should focus on the establishment of a sound “national-local-industry” multi-level standard system in order to realize the compatibility of data quality standards among enterprises; integrated software and hardware governance should be empowered by artificial intelligence to prevent risks and effectively improve the effectiveness of hardware and software risk assessment, monitoring and early warning as well as response. Constructing a security risk governance system that is mutually compatible with prevention and response can strongly escort China’s industrial digital intelligence transformation and new industrialization to a stable and far-reaching future.

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