数据智能产业规模持续扩大 面临三大安全挑战

Core Insights - The data intelligence industry is experiencing rapid growth and is becoming a core engine for driving innovation and growth in the digital economy [1][3] - The integration of data and artificial intelligence (AI) is deepening, with AI serving as a crucial means for releasing the value of data, while data fuels AI innovation [1][2] - The industry is facing three major security challenges: unresolved technical bottlenecks, increased complexity in governance of new business models, and insufficient practical implementation of security governance [6] Industry Development - The global data intelligence industry is expanding, with approximately 30,000 data intelligence companies worldwide as of December 2024, of which 4,696 are in China, accounting for 15% of the total [3] - The number of high-level papers published in the data intelligence field is increasing, with over 250,000 papers expected in 2024, including 1,941 high-level papers globally and 1,100 from China [3] - China is the largest exporter of top-tier data intelligence talent, with 47% of the world's top 20% data intelligence talents receiving their undergraduate education in China [3] Policy and Support - Recent policies such as the "Interim Measures for the Management of Generative AI Services" and the "Three-Year Action Plan for Data Elements (2024-2026)" have provided strong momentum for the development of the data intelligence industry [4] - Various provinces in China, including Beijing, Shanghai, Jiangsu, and Shandong, are implementing policies to promote AI and digital economy applications through the establishment of intelligent computing centers and innovation development pilot zones [4] Security Challenges - The data intelligence industry is currently facing three main security challenges: the inability to overcome technical bottlenecks, the rising complexity of governance in new business models, and the lack of mature governance frameworks for practical implementation [6] - There is a need for a comprehensive governance framework that includes data security, model algorithm security, application security, and service security to support the entire lifecycle of large models [6] - Companies should undergo structural changes in governance models, ensuring that security responsibilities permeate all levels of the organization, from decision-making to supervision [6]