China data law, read carefully.
Built for overseas counsel and compliance teams reading China's data-compliance laws in depth. DCC translates primary regulatory sources carefully and adds editorial context on how the rules actually operate.
The case files.
- § 01 · DATA-ECONOMY
China Halts Data-Asset ABS: Exchanges Pull the Handbrake on a ¥200 Billion Pipeline
According to reporting by Caixin (财新) and 财联社 circulated on 3–5 June 2026, the Shanghai and Shenzhen stock exchanges issued window guidance bringing the entire data-asset ABS (数据资产ABS) business chain to a stop — new filings turned away, approved-but-unissued deals told to pause, even issuance-approved deals told to delay. This halts a category that exploded from roughly 11 issuances raising ~¥4.6bn in 2025 to 21 issuances and ¥15.4bn in the first five months of 2026, with a declared pipeline approaching ¥200bn. The stated trigger is mission drift: pure-data-asset deals are under 2% of the market, while local-government financing vehicles (城投/LGFV) used the loose, fast 'data-asset' label to repackage existing non-standard debt as standardised bonds — data as window-dressing, with no real data cash flow behind it. DCC reads the event, the structural reasons, the three審查 gates the exchanges are expected to harden, and what it means for anyone underwriting, rating, or investing in China data-asset financing.
- § 02 · DATA-ECONOMY
What a 'Data-Asset ABS' Actually Securitises — The Collateral Is Data, the Cash Flow Is Not
The name misleads. A Chinese 'data-asset ABS' (数据资产证券化) is labelled as such when data-pledged collateral exceeds 50% of the asset pool — but the underlying assets that actually generate the repayment cash flow are conventional financial claims: supply-chain receivables, trust-loan beneficiary rights, or finance-lease claims. Data is the collateral, the credit-enhancement, or the pricing-and-monitoring tool — not the cash-flow source. This brief, the second in DCC's data-asset-ABS series, unpacks the mechanism overseas counsel need to price the risk: the four live deal structures (trust-loan, receivables, finance-lease, data-empowerment); the difference between accounting recognition (入表) and legal right-confirmation (确权); and the four legal infirmities that make these deals fragile — unsettled data property rights, the true-sale problem created by data's non-exclusivity, the limits of bankruptcy isolation when asset value depends on the originator's continued operation, and the PIPL/DSL eligibility gates. It reads the flagship deals (平安-如皋, 华鑫-鑫欣, 青岛, 杭州高新金投) for what each actually did.
- § 03 · DATA-PROPERTY-RIGHTS
Two Paths for the 'Right to Hold Data' — and Why the Narrow One May Add Little
Hong Yanqing (洪延青, 网安寻路人) works through the most unstable concept in China's 'separation of three rights' data-property framework — the Right to Hold Data (数据持有权). He pushes two readings to their logical ends. Path 1, the official 'complete separation' (三权完全切割): if the rights to hold, use, and operate data are truly independent, the holding right shrinks to a bare 'lawful-control state' whose only content is defensive — and that defense is already provided, against the world, by PIPL Article 10, DSL Article 32, the Network Data Security Regulation, and Article 13 of the Anti-Unfair Competition Law, so its incremental value as a standalone property right is thin. Path 2, the 'mother-right' reconstruction (持有权母权化): redefine 'holding' from factual control to a normative control that contains utilization potential, so the rights to use and operate are carved out from within it. DCC's read for overseas counsel: in Chinese data deals the tradeable substance sits in the rights to use and operate plus contract, registration, and compliance — not in 'who holds the data' — and China's data-property theory is still genuinely unsettled.
- § 04 · AI-GOVERNANCE
China's First 'AI Hallucination' Tort Judgment — GenAI Is a Service, Not a Product, and the Chatbot's '¥100,000 Promise' Binds No One
The Hangzhou Internet Court has decided China's first 'AI hallucination' (AI幻觉) tort case — written into the Supreme People's Court's 2026 work report to the NPC. A user asking a chatbot about college applications was told, across seven rounds, that a non-existent campus existed; when finally shown the official website, the model 'apologised' and 'promised' to pay ¥100,000, even generating a fake lawsuit template telling him to sue. He did. The court dismissed every claim and, in doing so, laid down the first judicial articulation of China's generative-AI liability framework: (1) an AI model is not a civil subject, so its 'promise' is no declaration of intent — and is not attributable to the provider either; (2) generative AI is a service, not a product, so fault liability under Civil Code Article 1165 applies, not product liability's no-fault rule under Article 1202; (3) there is no result-based duty to guarantee accuracy for ordinary inaccurate output — only a process duty of care (conspicuous AI-content labelling plus industry-standard accuracy measures), which the provider had discharged; and (4) no proven damage, no causation. For any company deploying GenAI to the Chinese public, this is the operating liability surface and the evidentiary playbook.
- § 05 · HEALTH-DATA
China's Hospitals Get Their Own Data Rulebook: Reading the 2026 Healthcare Data Security & PI Measures
On 12 February 2026 five agencies — the National Health Commission, the Ministry of Public Security, the Cyberspace Administration of China, the National Administration of Traditional Chinese Medicine, and the National Disease Control and Prevention Administration — jointly issued the Measures for the Administration of Data Security and Personal Information Protection of Healthcare Institutions (Trial). It is the first operational, sector-specific rulebook that turns the Data Security Law, PIPL, and the Network Data Security Regulation into concrete hospital obligations: a three-tier core/important/general data classification keyed to MLPS levels and commercial cryptography; a five-pillar full-lifecycle security system; a ten-item data prohibition list and an eight-item personal-information prohibition list; heightened protection for special groups; limits on facial recognition and AI; and a real enforcement chain running from named-person accountability through regulatory interviews, administrative penalties, civil tort liability, and criminal referral. DCC reads it for overseas pharma, medtech, and hospital-JV counsel — with the cross-border choke point and its academic-cooperation carve-out as the parts that most affect global clinical-data flows.
- § 06 · AI-GOVERNANCE
Prompt Stacks and Prompt Governance — Why System-Level Prompts Are Emerging as a Regulatory Lever (and Where They Fall Short)
A Chinese AI-law reading of Neumann, Sargeant and Singh's FAccT 2026 paper Prompt Governance? — and what it means for how China, the EU, and the US treat 'system prompts' as a regulatory object. Li Wenlong (科技利维坦) walks through the four-layer 'prompt stack' (system instructions → system guidelines → developer instructions → user prompts), five properties practitioners need to understand (layered, hidden, natural-language, malleable, loosely coupled to behaviour), and the comparative regulatory landscape: the EU GPAI Code of Practice requires signatories to disclose system prompts to regulators in model reports; the Trump EO 14319 / OMB M-26-04 stops at model / system / data cards and leaves system-prompt disclosure voluntary; the UK's AI Cybersecurity Code says effectively nothing. China's current GenAI safety regime (TC260-003 plus the GenAI Interim Measures) is output-evaluation-based — filing and pre-launch scoring, with no architectural hook into system prompts. Li predicts a Brussels Effect: system-prompt disclosure to regulators will become a global compliance baseline, analogous to the DPIA in data law. For overseas counsel: this is what is coming, what to start archiving now, and why 'what you write' in a system prompt is not 'what the model executes.'
- § 07 · 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.'
- § 08 · 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.
- § 09 · 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.
- § 10 · 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.
- § 11 · 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.
- § 12 · DATA-PROPERTY-RIGHTS
Wang Nian — Data Source's Rights as a 'Fair Use' Right Alongside the Three Rights
Wang Nian (Tsinghua Law) takes on the unresolved fourth-right question in the Data 20 Articles framework: what is the data source's right (数据来源者权), and how does it relate to the three rights (hold/use/operate)? Drawing on the 'data symbiosis' (数据共生) framework from the ALI-ELI Data Economy Principles and the EU Data Act, Wang argues that pre-existing legal entitlements — privacy, PI rights, IP, trade secrets — cover only part of the source's interest, leaving a residual that needs an independent legal protection. He frames the data-source right as a 'fair use right' (公平使用权): a contractual-relationship right against the specific data processor, distinct from the property-style three rights, that captures the value contribution of the source's participation in data co-creation. The corporate-data-portability analog DCC flagged in our NDA brief gets its doctrinal foundation here.
- § 13 · 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.
- § 14 · 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.
- § 15 · 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.
- § 16 · 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.
- § 17 · ENFORCEMENT
MIIT Public-Naming Bulletin 2026 Batch 3 (Total Batch 56): 31 Apps and SDKs Cited for PI Violations and Window-Redirect Abuse
MIIT's Information & Communications Administration Bureau published its 2026 Batch 3 public-naming bulletin (total Batch 56) on May 21, 2026, citing 31 apps and SDKs for violations of personal-information collection rules and window-redirect abuse. DCC frames this as the first entry in our enforcement tracker — explaining the joint CAC + MIIT + MPS 2026 Special Campaign that authorizes the batches, the four-statute legal architecture invoked, the rectification-then-enforcement pathway each named entity faces, the cadence of the bulletin series (roughly monthly, 56 batches since inception), and the operational picture this gives overseas counsel of which PI-protection violations actually attract enforcement in the Chinese mobile-app channel.
- § 18 · 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.
- § 19 · 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.
- § 20 · FOREIGN-INVESTMENT-SECURITY-REVIEW
Why China Used Foreign Investment Security Review on Manus — Not Tech or Data Export
Hong Yanqing on Beijing's banning of Meta's Manus acquisition. The regulator's choice of pathway — Foreign Investment Security Review, not Technology or Data Export — signals a shift from 'transaction-level' to 'capability-level' oversight of frontier AI projects, with implications for any overseas tech investment touching China.
- § 21 · TOKENS
Cold Water on 'Token Trading' — Wang Qinglan on the NDA's High-Quality Data Set Initiative
In March 2026, the National Data Administration released the *Implementation Plan for Promoting High-Quality Industry Data Set Construction (Draft for Public Consultation)*, which explores a 'token (词元) based value system' and 'token trading as a new transaction mode' for high-quality data sets. The Chinese AI policy community immediately heralded the move as 'revolutionizing data trading.' Wang Qinglan pours cold water: token is a measuring unit, not a magic transformer. AI tokens are not crypto tokens. The bottleneck in China's data-element market isn't measurement — it's supply, rights clarity, compliance cost, and data silos.
- § 22 · CRIMINAL-LIABILITY
When PIPL Violation Becomes a Crime — Hong Yanqing on China's Personal Information Criminal Threshold
Hong Yanqing on the criminal-side analog to PIPL — when does mishandling personal information cross from administrative violation into the crime of 'infringing on citizens' personal information'? His critique: the two key elements ('relevant State provisions' and 'serious circumstances') are too loose, and courts have stretched them in ways that should worry compliance teams.
- § 23 · FACIAL-RECOGNITION
When Is Facial Recognition in a Public Place 'Necessary for Public Security'? Hong Yanqing's Four-Element Framework
Hong Yanqing on how to operationalize PIPL Article 26's 'necessary for public security' principle for public-place video surveillance and facial recognition. His framework: a four-step necessity test, tiered risk regime with a published prohibited list, three-fold technical controls, and a lifecycle closure mechanism — drawing on EU AI Act and US state-level practice.
- § 24 · 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.
- § 25 · CROSS-BORDER
Cross-Border Data Discovery — How the U.S., EU, and China Each Play Offense and Defense
When a foreign authority wants data stored in China — or vice versa — three doctrines compete. The U.S. uses a 'data controller standard' (CLOUD Act) that reaches globally on offense and shields domestically through ECPA blocking on defense. The EU uses 'market access' leverage (GDPR Article 3 jurisdictional reach plus Article 48 blocking). China uses a 'data location standard' (territorial sovereignty plus the MLA Law, DSL, and PIPL blocking clauses). Wang Qinglan maps the four discovery paths, the three jurisdictional doctrines, and what compliance teams should build to survive the squeeze.
- § 26 · DATA-PROPERTY-RIGHTS
Will Judicial Review 'Reset' the Data Registration Rush? — Reading Wang Qinglan on the SPC's New Data Disputes Case Category
Wang Qinglan, head of compliance at a Chinese data exchange, asks what the Supreme People's Court's new 'data disputes' case category — effective January 1, 2026 — does to the data property rights registration certificates that institutions across the country have been issuing. Her argument: certificates issued through formal-only review will not survive substantive judicial scrutiny, and a single rejected certificate could erode trust in the entire registration regime. The path forward is a three-tiered protection model and aligned standards across regulators, registration institutions, and courts.
- § 27 · PERSONAL-INFORMATION
PIPO vs. DPO — How China's Personal Information Protection Officer Differs from the GDPR Data Protection Officer
The Cyberspace Administration of China announced in July 2025 that personal-information processors handling data on 1 million or more individuals must submit Personal Information Protection Officer (PIPO) information to CAC. Compliance Talker's global legal policy research team contrasts China's PIPO regime under PIPL Article 52 with the GDPR's Data Protection Officer (DPO) framework under Articles 37–39. The most consequential difference: PIPO carries individual administrative liability — up to RMB 1 million in personal fines and industry bans — where DPO does not.
- § 28 · CROSS-BORDER
Mutual Trust Mechanisms for Cross-Border Data Flow — China's 'Trusted Data Space' Bet
Compliance Talker's global legal policy team analyzes three competing models for cross-border data mutual trust: the EU's 'rule trust' (adequacy + SCC), the US's 'market trust' (CLOUD Act + DPF), and China's 'technology trust' bet on Trusted Data Spaces (TDS). The NDA's November 2024 *TDS Development Action Plan 2024-2028* makes confidential computing, federated learning, and blockchain the technical layer through which China seeks to demonstrate cross-border data flow can be 'usable but invisible.' For overseas teams, this is the most concrete view of where Chinese cross-border data infrastructure is heading.
- § 29 · FACIAL-RECOGNITION
Reading the FRT Application Measures — What the 100k-Record Filing Threshold Actually Triggers
The Administrative Measures for the Application Security of Facial Recognition Technology took effect June 1, 2025. The May 2025 announcement on FRT filing implementation followed. Compliance Talker's global legal policy team walks through the seven specific compliance obligations the Measures impose — the non-exclusive-use rule, end-side storage default, 100k-individual filing threshold, separate-consent reinforcement, PIA mandate, and more — with practical implementation guidance on each. For overseas firms with any China-facing FRT deployment, this is the operational walkthrough.
- § 30 · 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.
- § 31 · 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.
- § 32 · 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.
- § 33 · 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.
- § 34 · DATA-PROPERTY-RIGHTS
What Does Data Registration Actually Confirm? — A Doctrinal Reading
Long before the SPC's January 2026 'data disputes' case category started squeezing data registration certificates against judicial review, Wang Qinglan had already written the foundational critique: data registration does not 'confirm rights' because there are no legal data rights to confirm. The Data 20 Articles created data property rights, not data legal rights, and Chinese property rights are not Article-conferred civil rights. Registration certificates are 'trust credentials,' not 'rights certificates.' This is the doctrinal essay overseas counsel should read before the SPC sequel.
- § 35 · DATA-EXCHANGES
On-Exchange vs. Off-Exchange Data Trading — A Uniquely Chinese Market Structure
Why does China have data exchanges? Wang Qinglan's piece opens with an observation overseas readers will recognize: 'When you tell foreigners about China's on-exchange data trading market, you get blank stares — because exchange-organized data trading is uniquely Chinese.' The analogy she offers — Shenzhen Data Exchange is to data what the Shenzhen Stock Exchange is to securities — unlocks the architecture. Five tiers of trading venues by public-risk level. Three waves of Chinese data-exchange evolution. And the operational meaning of why on-exchange and off-exchange trading coexist.
- § 36 · DATA-ECONOMY
What Is Actually Traded on China's Data Exchanges — A Bakery Metaphor
Per the Shenzhen Provisional Measures for Data Trading Administration, four categories of object can be traded on a Chinese data exchange: data products, data services, data tools, and other regulator-approved objects. Wang Qinglan walks through what each means in plain language with a bakery metaphor — wheat (raw data) becomes flour (data resources) becomes cakes (data products); a baker is a data service; the oven is a data tool. The piece is useful precisely because it answers a question overseas teams rarely think to ask: what are the data exchanges actually selling?
- § 37 · PUBLIC-DATA
Case Study — A Public-Data Operator Hands Personal Data to a Bank. Two Compliance Failures.
A real-case analysis from Wang Qinglan. A state-affiliated auction company holds the public-data operating right for vehicle license-plate auction data. A bank persuades it to hand over the personal data of winning bidders. The bank builds a targeted credit product and pays the auction company RMB 12 million a year in revenue share. Two compliance failures: (1) no individual consent under PIPL; (2) no credit reference business license under the Credit Reference Industry Regulation and Credit Reference Business Measures. Public-data authorized operation does not displace the credit reference licensing regime.
From the desk.
- § 01 · DATA-ECONOMY
From Collateral to Cash Flow: The 'Secondary Licensing' Model That Would Make Data-Asset ABS Real
If today's data-asset ABS is '1.0' — data as collateral behind a conventional debt claim — then '2.0' is the version where the data's own cash flow (licensing fees, data-service subscriptions) directly repays the securities, upgrading data from credit-enhancement tool to genuine underlying asset. This third brief in DCC's data-asset-ABS series examines the structure most likely to get there: the 'secondary licensing' (二次许可) model borrowed from intellectual-property ABS, in which a holder exclusively licenses data to an originator for an upfront lump sum, then takes a reverse exclusive licence back and pays periodic fees that become the ABS cash flow — ownership never moving. It maps the obstacles (data's non-exclusivity defeats 'exclusive licence' and 'exclusive possession'; PIPL/DSL cap what can be licensed; valuation is immature), the finance-lease-of-data variant, and the early policy encouragement (Anhui's March 2026 measures endorsing reverse-licensing). The irony the June 2026 halt exposed: regulators want real data cash flow — which is exactly what 2.0 promises but cannot yet deliver at scale.
- § 02 · DATA-PLEDGE-FINANCING
Data Pledge Financing in China: What Is Actually Being Pledged, and Where the Law Gets Stuck
As Chinese banks and data exchanges experiment with data pledge financing (数据质押融资), a threshold question remains unresolved: what, legally, is being pledged? Chen Yiqian of Shenzhen Data Exchange walks through the two available routes under the Civil Code — chattel pledge (动产质权) and rights pledge (权利质权) — and the three operational problems that make chattel pledge difficult and the two doctrinal barriers that make rights pledge harder still. The analysis converges on a practical conclusion: chattel pledge via a third-party data custodian is the most workable path today, while data property rights and data intellectual-property rights both remain insufficiently legalised to support a reliable pledge. For overseas counsel advising on China data-asset financing, the gap between policy ambition and legal infrastructure is the central risk to price. Connects to the broader data property-rights registration project and the unresolved question of how data enters corporate balance sheets.
- § 03 · CRITICAL-INFORMATION-INFRASTRUCTURE
Are You a CII Operator or an Important-Data Handler? A Practitioner's Assessment Framework Under China's New Rules
China's Cybersecurity Law, Data Security Law, and Network Data Security Management Regulations impose materially heavier compliance obligations on critical information infrastructure (CII) operators (关键信息基础设施运营者) and important-data handlers (重要数据处理者) than on ordinary data processors. This brief, drawing on a DEXC+ practitioner analysis by Gu Qingzhuo (古青卓) of the Shenzhen Data Exchange compliance team, explains how the two statuses are determined under the current framework, why neither is self-evident from a company's own assessment alone, how recent rules — including the Regulations on Promoting and Regulating Cross-Border Data Flows and the national standard GB/T 43697-2024 — have clarified but not fully resolved the important-data identification problem, and what overseas counsel should do when advising clients that operate in China's critical sectors.
- § 04 · 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.