Group 1: Digital Economy Development - In 2024, the core industry value added of China's digital economy is expected to account for about 10% of GDP, with data production reaching 41.06 ZB, a year-on-year growth of 25% [1] - The total computing power scale is projected to reach 280 EFLOPS, with 5G base stations totaling 4.251 million and mobile IoT terminal users reaching 2.656 billion [1] - The eight major hub regions account for over 60% of the newly added computing power nationwide [1] Group 2: Artificial Intelligence Integration - The "Artificial Intelligence +" initiative aims to better integrate digital technology with manufacturing and market advantages, supporting the widespread application of large models [2] - In 2024, the number of enterprises developing or applying artificial intelligence is expected to increase by 36%, with high-quality data sets growing by 27.4% [3] - The number of enterprises utilizing large model data technology and data application enterprises is projected to grow by 57.21% and 7.14%, respectively [3] Group 3: Data Element Reform - The national data market transaction scale is expected to exceed 160 billion yuan in 2024, with a year-on-year growth of over 30% [5] - The national public data resource registration platform has approved 700 items of registered data since its launch [5] - The "data element" theory proposed by China Electronics aims to optimize data flow and enhance data utilization efficiency [6] Group 4: Data Infrastructure and Security - The construction of data infrastructure is crucial for high-quality supply, credible circulation, and high-level application of data [8] - The "密态可信数据空间" product supports secure data integration and processing, addressing challenges in data value release [8] - A multi-dimensional defense system is being developed to mitigate risks associated with data leakage and misuse in large model applications [9]

来源：中国新闻网 中新网福州5月2日电 (记者 蔡敏婕)随着人工智能进入大模型时代，高质量数据集成为核心要素。第八 届数字中国建设峰会近日在福建省福州市举行。在数据要素安全与流通基础设施分论坛上，人工智能与 数据流通的融合发展成为核心议题。 北京大学信息科学与技术学部主任梅宏认为，发展数字经济的关键是数据要素市场的培育与形成。我国 数据要素化尚处起步探索阶段，在资产地位、权属确权、流通交易、利益分配和安全隐私等方面存在诸 多障碍。迫切需要构建安全可信的流通基础设施，从技术、制度、生态多维度发力，保障数据安全，打 破流通壁垒，推动数据要素市场化，释放数据潜在价值。 工业和信息化部电子第五研究所所长、党委副书记杨建军指出，当前人工智能治理存在数据质量、制度 供给、责任界定等问题，各国治理理念存在差异。中国需在数据治理层面突破技术瓶颈，在制度供给层 面构建风险分类分级管理等机制，在场景落地层面加强政企协同、产学研合作，同时加强国际合作，构 建安全、可信、可持续的人工智能发展环境。(完) 王桂荣称，传统的基于边界防护的安全方法已经无法满足复杂网络环境下的防护需求，需要基于内生安 全理念构建纵深防御防御体系，该集团推出 ...

Group 1 - The application of large AI models is experiencing a significant explosion across various industries, bringing both immense productivity and creativity, as well as substantial security risks [2] - "Small data" has become a core concern in the digital economy, where data is likened to "oil" and AI to an "engine." Traditional enterprises are now processing their accumulated data into refined "small data," which, if compromised, can lead to a collapse of their core competitiveness [2][3] - There is a high dependency on AI by some enterprises, which can lead to a "butterfly effect" if large model decisions go awry. External attacks can exploit vulnerabilities, while internal errors can contaminate the model's learning environment [3] Group 2 - The majority of active servers for large models are operating without proper security measures, allowing unauthorized access and potentially leading to a chain reaction of business disruptions [3] - The importance of network security is emphasized as a foundational element for the development of the digital economy, with the need for continuous innovation in cybersecurity to create a secure environment for new technologies [4] - The global technological competition is intensifying, with AI at the center, necessitating advancements in cybersecurity to ensure technological autonomy and create a protective barrier for the digital economy [4]

Core Viewpoint - The rapid application of artificial intelligence, particularly large models, in vertical fields presents new challenges and opportunities for data security, with the market expected to experience significant growth [1][6]. Group 1: Challenges in Data Security - The first challenge is the increased importance of "small data," which refers to the core assets accumulated by companies over time, making them vulnerable to theft or loss [2][5]. - The second challenge is that many large models are deployed without adequate security measures, leading to potential systemic risks where an attack could disrupt multiple interconnected systems [3][5]. - The third challenge is the over-reliance of users on AI for decision-making, which can lead to vulnerabilities if the models are manipulated or if incorrect information is introduced [5][7]. Group 2: Opportunities in Data Security Market - The data security market is predicted to "ignite" soon, with growth expected to outpace other industries due to the increasing density and value of data [6][7]. - The rise of "car-road-cloud integration" in the intelligent connected vehicle industry presents a significant market opportunity, with the potential for a trillion-dollar market as data security becomes crucial for safe operations [8]. - Companies are increasingly focusing on building data security "moats" through investments in data infrastructure, computing power platforms, and dynamic data management [1][7].