数据合规六大原则CCRC-DCO，企业运营必修课

Core Viewpoint - The article emphasizes the importance of data compliance as a systematic project that balances the free flow of data with the protection of personal rights and public safety, highlighting six core principles that serve as the foundation for compliant data processing activities [1]. Group 1: Fundamental Principles - The principle of legality, legitimacy, necessity, and integrity serves as the fundamental baseline for data processing, requiring all activities to be legally compliant and ethically sound [3][6]. - Legality mandates that data processing must adhere to laws such as the Personal Information Protection Law and the Data Security Law, ensuring activities are conducted within legal frameworks [3]. - Legitimacy requires that the purpose of data processing must be justifiable and not achieved through deceitful or coercive means [6]. - Necessity dictates that the scope and method of data processing must be directly related to the stated purpose and should minimize the impact on personal rights [6]. Group 2: Key Defensive Principles - The principle of clear purpose and minimum necessity aims to prevent data abuse by eliminating excessive collection and arbitrary use of data [7]. - Transparency is crucial for ensuring user awareness, requiring data processors to clearly inform users about the specific purposes of data collection in an understandable manner [10][11]. - The minimum necessity principle mandates that only the essential types and amounts of personal information should be collected to achieve the stated purpose [11]. Group 3: Quality and Security Principles - The accuracy principle emphasizes the need for data processors to maintain high-quality information, as inaccurate or outdated data can lead to significant harm [14]. - The security principle requires data processors to implement appropriate technical and management measures to prevent data breaches, alterations, and losses [16][20]. - The responsibility traceability principle enforces accountability by ensuring that data processors are responsible for their actions, with mechanisms in place to trace compliance responsibilities [18][23]. Group 4: Comprehensive Integration - The six principles are interconnected and collectively form a complete logical chain for data compliance, from establishing baselines to ensuring transparency, quality, security, and accountability [26].