Every brief.
The full run, most recent first.
- § 49 · 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.
- § 50 · 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.
- § 51 · 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.
- § 52 · 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.
- § 53 · 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.
- § 54 · 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.'