[Editor to fill: 200-word domain overview.]
§ DOMAIN · DATA SECURITY
Data Security.
数据安全
Data classification and grading, important data, core data, and the broader data security regime under DSL.
The legal corpus.
15 laws.
- CN-SG Joint Guide China–Singapore Joint Data Compliance Guide: Practical Handbook — China Chapter 中国—新加坡联合数据合规指引：实务手册（中国篇）
- CII Regulations Security Protection Regulations for Critical Information Infrastructure 关键信息基础设施安全保护条例
- RULE Measures for the Security Assessment of Data Export 数据出境安全评估办法
- Data Twenty Opinions Opinions of the CPC Central Committee and the State Council on Building a Basic Data System to Better Play the Role of Data Elements 中共中央 国务院关于构建数据基础制度更好发挥数据要素作用的意见
- RULE Provisions on Promoting and Regulating Cross-border Data Flows 促进和规范数据跨境流动规定
- DRAFT Data Property Rights Registration Work Guide (Trial) — Draft for Public Consultation 数据产权登记工作指引（试行）（公开征求意见稿）
- REGULATION Regulation on Network Data Security Management 网络数据安全管理条例
- Data Terms Batch 1 Explanation of Common Terms in the Field of Data (First Batch) 数据领域常用名词解释（第一批）
- Data Terms Batch 2 Explanation of Common Terms in the Field of Data (Second Batch) 数据领域常用名词解释（第二批）
- DSL Data Security Law of the People's Republic of China 中华人民共和国数据安全法
- CSL Cybersecurity Law of the People's Republic of China (2025 Amendment) 中华人民共和国网络安全法（2025 修正）
- REGULATION Regulations on the Sharing of Government Data 政务数据共享条例
- REGULATION Administrative Measures for Internet Information Services (2024 Revision) 互联网信息服务管理办法（2024 修订）
- RULE Interim Measures for the Registration and Administration of Public Data Resources 公共数据资源登记管理暂行办法
- RULE Implementation Specifications for Authorized Operation of Public Data Resources (Trial) 公共数据资源授权运营实施规范（试行）
In this domain.
19 briefs.
- § 01 · JUDICIAL
Datatang v. Yinmu — China's First Ruling on a Data-IP Registration Certificate, and Why Open-Sourced Data Is Still Protected
A consolidated case study of 数据堂诉隐木科技 (Datatang v. Yinmu) — the Beijing IP Court's June 2024 appeal ruling, widely called China's first case on the evidentiary effect of a data-IP registration certificate. The dispute: Datatang built voice datasets for AI training, open-sourced some under a license; Yinmu took and redistributed them in the same data-services market. DCC synthesizes four commentaries (the case report, a Tsinghua analysis, and two Shenzhen Data Exchange DEXC+ deep-dives) into the four holdings that matter for overseas counsel: (1) a data-IP registration certificate is prima facie evidence of property-type interests and lawful sourcing — but not an absolute property right (property-rights-statutism); (2) open-sourced data, though neither trade secret nor copyrightable compilation, is protectable under the Anti-Unfair Competition Law's general clause; (3) the protection hierarchy (compilation work → trade secret → AUCL Art. 2); and (4) whether the taker honored the open-source license is the hinge for 'improper conduct.'
- § 02 · ANONYMIZATION
Reviving a Zombie Provision — Xu Ke's Concentric-Circle Reconstruction of the Anonymization Regime
Xu Ke (UIBE) calls PIPL Article 4's anonymization carve-out a 'zombie provision' (僵尸法条) — on the books, never used, and one of the biggest blockages in the data-element market. His diagnosis: the zombie state is caused not by the text but by three unaddressed worries (processors fear the standard is unattainable or value-destroying; regulators fear anonymization becomes an evasion tool; users fear it's a hollow promise). His cure is a concentric-circle architecture that maps three risk types (systemic / operational / residual) onto three layers of anonymity (presumptive / determined / trust). This is the most complete academic blueprint yet for making the anonymization clause operational — and it pairs directly with TRIMPS's risk-based, recipient-relative reading.
- § 03 · DATA-PROPERTY-RIGHTS
The 'Rights Block' — Xu Ke's Structural Theory Behind China's Data-Property Framework
Xu Ke's highly-cited (255×) 政法论坛 article on the structure of data rights — the theoretical scaffolding that the Data 20 Articles' three-rights framework rests on. He maps the field's two warring paradigms (formalist 'empowerment' vs substantivist 'conduct regulation'), argues both fail alone, and integrates them via a 'reflexive law' approach. The payoff is a taxonomy of three possible rights structures — rights-ball, rights-bundle, rights-block — and the case that the 'data rights block' (数据权利块) best fits data's 'one principle, many manifestations' character. For overseas counsel, this is the conceptual map that explains why Chinese data rights are structured the way they are — and why Western property and IP analogies keep failing.
- § 04 · ANONYMIZATION
From 'Cannot Be Restored' to 'Difficult to Restore' — TRIMPS on Whether Anonymization Is Absolute, and Whether It's Recipient-Relative
The Third Research Institute of the Ministry of Public Security (TRIMPS) — the body behind China's classified-protection regime and national eID platform — takes on the two questions that determine whether anonymization actually gets data out of PIPL scope. First: does PIPL's 'cannot be restored' standard (Art 73) require re-identification probability of literally zero? The 2025 draft PI Anonymization Guide quietly softened it to 'difficult to restore,' aligning China with the GDPR 'all reasonable means' test and reframing anonymization as a dynamic, continuously-assessed, risk-based process rather than a one-time terminal state. Second: is anonymization recipient-relative — can the same dataset be PI in one party's hands and anonymized in another's? TRIMPS reads the EU SRB v EDPS case and UK ICO guidance toward 'yes,' with major implications for how overseas counsel structure data sharing and cross-border transfer.
- § 05 · AI-GOVERNANCE
Zhu Xiaofeng — Who Pays When GenAI Causation Is Unclear? Applying Civil Code Article 1254 by Analogy
Zhu Xiaofeng (Central University of Finance and Economics Law School) takes on the GenAI causation black hole — when a personal-information harm clearly arises from a GenAI service but specific causation among model designer, model provider, model user, and data provider cannot be established, who pays? Zhu's structural answer: when conventional construction-element-analysis and Article 998 interest-balancing both fail (and they do), apply Civil Code Article 1254's 'unclear-causation' rule by analogy — the same rule used for falling-object-from-building cases. The doctrinal scaffolding: communication-safety theory, gain-and-risk allocation theory, causation proof + harm prevention. Critically: each potential injurer compensates the full damage; among themselves, allocation is proportional, with judges determining specific amounts case-by-case. Highly relevant for multinationals deploying GenAI in China — the proposed framework restructures the operating liability surface.
- § 06 · DATA-ECONOMY
Tang Linyao — Data-Broker Derivative Harms and the 'Data Integration Analysis Framework'
Tang Linyao (Chinese Academy of Social Sciences) maps the regulatory gap for data-broker derivative harms — the harms that arise not from direct PI leakage but from the integration and aggregation activity that data brokers themselves perform. The analytical core: a vertical / horizontal data-relations framework that explains why existing PIPL-style protection (vertical-relationship-focused) systematically fails to address horizontal-relationship harms; and the 'abstract risk substantialization' doctrine borrowed from US precedent and EU GDPR to bring data-broker risk into ex-ante regulatory scope. Operationally, Tang proposes a 'Data Integration Analysis Framework' with concrete tiering (三高 / 双高 / 单高 / 三低) that translates academic doctrine into compliance-program-grade controls. Applied to a real Shenzhen Data Exchange listing as worked example.
- § 07 · ENFORCEMENT
Seven Lessons for Data Compliance Teams from the SAMR 'Ghost Takeout' Series — 3.5 Billion Yuan, 9-Month Suspensions, and the Per-Merchant Aggregation Doctrine
In April 2026, the State Administration for Market Regulation (SAMR) imposed administrative penalties on seven major e-commerce platforms in the 'ghost takeout' series — 3.5 billion yuan in aggregate corporate fines, nearly 20 million yuan in individual fines on legal representatives and food-safety officers, and 3-to-9-month business suspensions. While the cases were ostensibly food-safety enforcement, their analytical structure — pierce-the-paper-compliance, per-merchant aggregation of penalties, identification of licensed-entity liability holders, dual penalties on individual compliance officers — translates directly to data-compliance enforcement. Adapted from a substantive practitioner analysis by 黄春林 (Huang Chunlin), this DCC brief works through seven operational lessons that DSO / PIPO / DPO and compliance counsel should apply *before* the analogous enforcement wave reaches data compliance.
- § 08 · AI-AGENTS
Mapping the AI Agent Risk Surface — A Ten-Category Taxonomy Under China's New 智能体新规
China's Cyberspace Administration jointly issued the Implementation Opinions on Standardized Application and Innovation Development of AI Agents (the '智能体新规' or 'Agent Rules') on May 8, 2026 — the first dedicated regulatory document on AI agents anywhere in the world. This DCC brief works through the ten-category risk taxonomy that practitioners are now using to map the agent attack surface: goal hijacking, tool misuse, identity/permission abuse, supply-chain compromise, unintended code execution, memory and context poisoning, inter-agent communication insecurity, cascade failures, human-machine trust exploitation, and rogue agents. With the agent risk mapped, the brief works the legal-liability vector: how each risk maps to administrative, civil, and criminal exposure under existing PIPL, CSL, Anti-Unfair Competition, and trade-secret regimes. Closes with the Guangzhou Internet Court's recent dual-authorization ruling against an open-source agent that bypassed a chat platform's risk controls — the first Chinese case to articulate the dual-authorization principle for AI agents accessing third-party platforms.
- § 09 · AI-AGENTS
Operationalizing AI Agent Governance — A Ten-Step Internal Control Framework
Part 2 of DCC's brief on the Chinese Agent Rules (《智能体规范应用与创新发展实施意见》, May 2026). After mapping the ten-category risk taxonomy in Part 1, this brief works through the ten-step internal governance framework practitioners are now building to operationalize agent compliance: cross-functional governance organization + agent asset inventory; use-case admission and classification (L1 read-only / L2 limited-write / L3 sensitive-data / L4 high-impact); security assessment and AI red-team testing; identity authorization and permission control (with the under-discussed 'permission inheritance' trap); data protection; tool and protocol security; human-in-the-loop design; supply-chain security; continuous monitoring; and AI-specific incident response. Closes with five operational priorities for teams that need to start now without waiting for the 'big-and-comprehensive' regime build.
- § 10 · AI-GOVERNANCE
Open-Source Does Not Mean Open Data — Zhang Ping on Training-Data Compliance for Open-Source AI
Peking University Law School professor Zhang Ping, writing in 人民论坛 (People's Tribune), takes apart two misconceptions that have dominated the Chinese open-source AI discussion: that 'open source' means training data has no copyright protection, and that 'algorithm open-source' compels 'training data publication.' Both false. Zhang lays out the structural distinction: 'open source is conditional authorization under license' — applied to model weights, not to the training corpus, which is a legally independent object. She then maps the full-chain compliance risk (acquisition / processing / output) and proposes a four-tier differentiated governance framework that finance, healthcare, and government AI deployments can actually use to map their training-data inventory against compliance gates.
- § 11 · DATA-PROPERTY-RIGHTS
NDA Explains the Three-Rights Framework — A Plain-Language Walk-Through from the Regulator Itself
The National Data Administration's official 政策解读 (policy interpretation) on the three-rights framework — the right to hold, the right to use, and the right to operate data — established by the Data 20 Articles. NDA walks through what each right means, illustrative scenarios (group-company data subsidiaries; hospital-pharma research pools; data-broker commission arrangements), how the rights relate to each other (independently severable; non-exclusive across parties for the same data), and why the structural-separation design was chosen over a unitary-ownership model. The clearest available statement of the regulator's own intent on the framework that anchors every downstream rule — data-resource registration, data-property-rights registration, FTZ data-circulation negative lists, on-floor / over-the-counter trading rules.
- § 12 · DATA-PROPERTY-RIGHTS
Who Is the 'Data Processor' Under the Three-Rights Framework — NDA's Farm-Equipment Hypothetical
NDA's official 政策解读 on the threshold question that every three-rights allocation depends on: who is the 'data processor' and who is the 'information subject'? NDA uses a farm-equipment hypothetical — a farm rents tractor, irrigation, and fertilizer equipment from three different vendors; cultivation data is captured in the process — to work through who collects, who decides processing purposes, and how the property-rights regime balances the data-processor's commercial interest against the information-subject's rights to access copies of relevant data. The piece sketches the basic information-subject vs. data-processor dichotomy that anchors the entire downstream data-element regime, and surfaces the access-to-data right (data portability for commercial entities) that overseas counsel often miss.
- § 13 · DATA-PROPERTY-RIGHTS
Cloud, BPO, and Other Entrusted-Processing Arrangements: Why the Processor Doesn't Get the Rights
NDA's official 政策解读 on a tactically critical sub-question of the three-rights framework: when a data processor outsources storage, processing, or analysis to a third-party service provider — typical cloud, BPO, or e-government-system arrangements — does the entrusted party acquire any of the three property rights? NDA's clear answer: no. The entrusted processor (受托人) is not a 'data processor' in the property-rights sense — it merely executes instructions on behalf of the data processor (the principal). It cannot use the data outside the entrusted scope, cannot transfer the data into market circulation, and cannot apply the data to its own debt repayment or bankruptcy distribution. The line is anchored to the Civil Code's contract-of-mandate rules — a long-standing piece of Chinese commercial law extended cleanly into the data-element regime.
- § 14 · IMPORTANT-DATA
'Important Data' Is a Category, Not a Tier
Hong Yanqing argues the mainstream reading of Article 21 of the Data Security Law confuses enterprise asset-inventory language with state-level legal-interest protection — with real consequences for cross-border transfers, enforcement, and how PIPL and DSL stack.
- § 15 · CSL
China's Cybersecurity Law Just Got Teeth — The 2025 Amendment and What Changed
On October 28, 2025, the NPC Standing Committee adopted the first amendment to China's Cybersecurity Law since 2017, effective January 1, 2026. Compliance Talker's global legal policy team walks through what changed across 14 amendments: a new framework provision on AI safety and development, harmonization with PIPL and the Civil Code on personal information, sharply increased penalties (10× cap on top fines), expanded application of the dual-penalty system to individual officers, and broader extraterritorial reach. For overseas teams, the operational takeaway is that cybersecurity compliance is now an executive-level risk, not a documentation exercise.
- § 16 · IMPORTANT-DATA
How to Identify 'Important Data' — A Plain-Language Method from Wang Qinglan
Wang Qinglan, head of compliance at a Chinese data exchange, walks through China's unique 'important data' concept in plain language: where it came from, why no other major jurisdiction has anything quite like it, how the U.S., EU, Japan and Korea solve the same problem differently, and — most useful for compliance teams — three methods to identify whether a dataset is 'important' in practice. Her own 'unorthodox' shortcut: ask whether a hostile foreign actor could use this data to cause trouble. If yes, treat it as important data.
- § 17 · DATA-FUNDAMENTALS
What Is Data, Really? — A Plain-Language Primer on Rules and Compliance
What does it actually mean to call something 'data,' and what turns raw recordings into a data asset? Wang Qinglan uses a toy storage room metaphor to walk through the foundational concept overseas readers often skip: data is not just 'records' — it's records made under rules. Master data, metadata, ontology, the three-tier compliance taxonomy (legal / ethical / promised), and the three-step compliance workflow (select / allocate / execute) — all anchored in a concrete example a non-specialist can follow.
- § 18 · DATA-GOVERNANCE
Data Governance vs. Data Management vs. Data Compliance — A Plain-Language Disambiguation
Wang Qinglan disambiguates three terms that compliance and data teams habitually conflate: data governance, data management, and data compliance. Using a 'data manor' metaphor (the family council vs. the steward team vs. the community monitor), she maps each function to its job — setting direction, executing efficiently, and operating sustainably within external rules and self-imposed commitments. The piece is useful precisely where bilingual confusion is highest: 'data governance' in English carries different connotations than 数据治理 in Chinese practice.
- § 19 · CROSS-BORDER
FTZ Data Export Negative Lists — How 17 Sectors Across Seven Provinces Now Identify Important Data
Article 6 of the 2024 CBDF Provisions authorized Free Trade Zones to publish data-export negative lists. Since then, Tianjin, Beijing, Hainan, Shanghai, Zhejiang and others have published negative lists covering 17 sectors — automotive, pharmaceuticals, retail, civil aviation, reinsurance, deep-sea industry, seed industry, and more. Compliance Talker's analysis walks through the structural convergence of the negative lists, the important-data identification refinements each FTZ has produced, and the operational impact on enterprises both inside and outside the FTZs.