Co-authored with my friend Steve, this article interprets the “Data Classification and Grading Practical Guide 2.0”, with special thanks to Mr. Ai Long, Director of the Strategic Consulting Center at Tianrongxin, for his careful review and guidance.
This article is submitted to: CSA Release | Data Classification and Grading Practical Guide 2.0
Data, as a core element of the digital economy era, is a fundamental strategic resource for the country, characterized by its vast scale, diverse types, and variable states. When faced with the complexity and abstraction of data, deeply understanding different data attributes, conducting typological analysis, and effectively classifying and grading data is essential for achieving refined data security governance and is a necessary path to balance data security protection and circulation utilization.
The CSA Greater China region released the “Data Classification and Grading Practical Guide 2.0,” which discusses and analyzes the current status of data classification and grading management both domestically and internationally, an overview of data classification and grading, capacity building for data classification and grading, methods for data classification and grading, implementation plans for data classification and grading, and applications of data classification and grading. It also provides introductions to typical domestic data classification and grading products, templates and tools for data classification and grading, reference materials for data classification and grading, key technologies and methods for data classification and grading, interpretations of typical industry standards, examples of data classification and grading dictionaries, and examples of document recognition rules as reference appendices.
In the writing and practical verification of the guide, several enterprises contributed their technical experiences and product support, which provided valuable references for promoting the implementation of data classification and grading. In an era where digitalization and intelligence complement each other, we hope to provide effective references and practical assistance to practitioners and researchers in the fields of data governance and data security.
A New Upgrade of Guide V2.0: Deep Integration of Theory and Practice#
Compared to version 1.0, the “Practical Guide V2.0” has seen significant improvements, specifically in the expansion of knowledge coverage, in-depth technical support, and a rich variety of application templates. The guide incorporates more industry standards and policy interpretations, helping data processors across various industries and fields find applicable solutions in diverse business scenarios. The guide also enhances theoretical methods, assisting enterprises in flexibly responding to different business needs through various approaches such as "line classification," "surface classification," and "hybrid classification." In technical analysis, the guide discusses in detail the application value of core technologies such as natural language processing (NLP), machine learning, and metadata analysis. Additionally, the guide reduces implementation difficulty by providing a wealth of practical templates and reference materials.
Main Content Overview#
Data classification and grading are foundational tasks for data security governance, requiring multi-role collaboration and being a continuous, complex, and systematic engineering project. Establishing a mature data classification and grading capability system is a prerequisite for ensuring that data classification and grading and security grading control can operate effectively and routinely. This capability building includes four main aspects: the functional structure of data classification and grading, management systems and processes, technical tool construction, and continuous operation mechanisms.
Data classification is the process of establishing a classification system based on data attributes and characteristics for management and use; data grading involves dividing security levels based on the importance of data and the potential harm it may cause, ensuring targeted protection. Classification and grading are not only the foundation of data governance and protection but also core requirements of laws and regulations.
The implementation of data classification and grading includes five stages: business activity identification, data asset discovery, data asset identification, rule formulation, and labeling.
Currently commonly used identification technologies include NLP, OCR, video file processing, and audio file processing.
The results of data classification and grading are widely applied in compliance supervision, data asset management, and data security protection. First, enterprises can meet legal and regulatory requirements through classification and grading, completing compliance tasks such as submitting important data directories and conducting cross-border data assessments. Second, by sorting data assets, they can display categories, levels, and distribution, supporting data value development and security management. In data processing activities, the results of classification and grading can be used in areas such as domain-specific storage, sensitive data monitoring, and differentiated approval process settings, achieving precise security control. Third, classification and grading are the basis for data security risk assessment and incident management, enhancing capabilities for pre-warning, in-process response, and post-rectification. Combined with security measures such as data encryption, desensitization, and anti-leakage, the results of classification and grading can also optimize enterprise security protection strategies, reduce protection costs, and comprehensively improve data governance levels.
In the future, the technical application of classification and grading will pay more attention to real-time and diversity, with the rapid development of generative artificial intelligence and multimodal learning bringing new possibilities to this field. Generative AI can predict future data classification needs of enterprises by analyzing historical data and behavior patterns, thereby optimizing grading rules in advance. The advantage of this technology lies in its foresight and flexibility, enabling enterprises to respond quickly to sudden data flows or policy changes. Multimodal learning, on the other hand, provides more comprehensive technical support for classification and grading by integrating various data forms such as text, images, video, and audio.