金融数据安全治理白皮书

Industry Overview - The financial industry, as a data-intensive sector, faces increasing data security challenges due to frequent cyberattacks, data breaches, and stringent regulatory requirements [7] - Financial data security governance is crucial for protecting customer privacy, ensuring financial asset security, and maintaining the stability of the financial system [7] - The industry is shifting its focus from operational-level security to production environment security management, emphasizing real-time monitoring, rapid response, and dynamic protection [9] Core Perspectives - Data security governance is the cornerstone of digital transformation in the financial industry, ensuring data integrity, availability, and privacy through systematic management [8] - Financial institutions should implement "data security left-shift" by integrating security requirements into the entire business system development lifecycle, including design, coding, testing, deployment, and maintenance [10] - Emerging technologies such as large models, data synthesis, privacy computing, and homomorphic encryption are double-edged swords, offering enhanced risk identification and decision-making capabilities while introducing new security risks [11] Data Security Governance Framework - The financial data security governance framework includes management specifications, technical protections, security monitoring, data access control, and professional talent development [14] - The framework emphasizes the importance of compliance, business innovation, and maintaining financial system stability [15] - The development of financial data security governance has evolved through four stages: financial informatization, internet finance, deep integration of finance and technology, and digital finance [17] Regulatory and Compliance Requirements - Key data security laws and regulations in China include the Cybersecurity Law, Data Security Law, and Personal Information Protection Law, which set strict requirements for financial institutions [19][20] - Financial regulatory bodies such as the National Financial Regulatory Administration, the People's Bank of China, and the China Securities Regulatory Commission play crucial roles in enforcing data security regulations [21][22][23] - Financial institutions face challenges in managing internal and external data interactions, as well as integrating data security governance with diverse business scenarios [27][28] Implementation and Best Practices - Financial institutions should adopt a phased and iterative approach to data security governance, focusing on business data assets and integrating management, technology, and operations [108][109] - The implementation of data security governance can be divided into three stages: foundational, optimization, and operational, each with specific goals and tasks [110][112][113][114] - Best practices include establishing a comprehensive data security management system, implementing advanced technical measures, and fostering a data security culture within the organization [36][37][116][117] Emerging Technologies and Trends - Traditional security technologies such as data encryption, access control, and data masking remain essential, but face challenges in key management, performance, and adaptability to new threats [40][41][42][43] - Emerging technologies like data security platforms, secure service edge (SSE), and data synthesis offer new capabilities but also introduce complexity and potential risks [49][50][51][52] - Large language models and AI-driven solutions are increasingly being used for threat detection, fraud prevention, and data classification, though they require significant computational resources and pose ethical and legal challenges [53][54]