Every brief.
The full run, most recent first.
- § 01 · AI-AGENTS
Fault for Services, Strict Liability Only for Machines: Wang Liming on Tort Liability When AI Agents Cause Harm
In Tsinghua University Law Journal (《清华法学》) 2026 No. 5, Wang Liming (王利明) of Renmin University sets out how Chinese tort law should handle harm done by AI agents. His answers: an agent is never a liable party, so liability falls on the people along its value chain; AI that provides a service, such as generative AI, is judged by fault, while strict product liability applies only to agents that enter the physical world as tangible things and threaten life, body, health or tangible property; where fault applies, operators and deployers should prove they took reasonable care; multiple actors are not jointly and severally liable by default, but answer in proportion to their causal contribution, with the party closest to the end user carrying the heaviest risk-control duty; open-source and free providers get lighter treatment; causation should be presumed when the evidence sits with the defendant; and the 'mass micro-torts' agents produce, such as leaks of many people's personal information, call for representative and public-interest litigation rather than individual suits. DCC summarizes the article section by section and maps each proposal against the Supreme People's Court's AI Disputes Opinions (法发〔2026〕10号), the PIPL and the Civil Code. Most of Wang's framework is already in the Opinions; the reversal of the burden of proof for service agents, the presumption of causation, and substitute measures for data-subject rights go further.
- § 02 · SPP
Five Years of PIPL: The Supreme People's Procuratorate's Eight Typical Cases on Personal Information
On November 2, 2026, the day after the fifth anniversary of the Personal Information Protection Law taking effect, the Supreme People's Procuratorate (SPP) issued eight typical cases on protecting citizens' personal information. Seven are prosecutions for the crime of infringing upon citizens' personal information (Criminal Law Article 253a); the eighth is a civil public-interest suit. DCC translates the SPP's release in full and maps the eight cases for counsel. Four points stand out. Information that is already public is 'not a public resource': compiling rental listings and selling them abroad, beyond the purpose for which they were posted, is the crime (Case 5). A passenger's upcoming flight is 'whereabouts and tracks' information, the most tightly protected criminal category (Case 6). Three of the eight leaks ran through people with legitimate access: insiders at source units, an airline's outsourced call-center agent, and bribed HR staff (Cases 1, 6, 7). And procuratorates now attach public-interest damages to PI crimes, set by sensitivity, scale, public value and cross-border risk rather than by what the offender earned: RMB 4 million against one recruitment-data seller (Case 7) and RMB 1.5 million against two people who scraped 3.86 million cancer-hospital records and moved them overseas, when their proven profit was RMB 10,800 (Case 8). The full notice and all eight cases are on the DCC law page.
- § 03 · AI-AGENTS
Who Lets a Personal AI Agent In? Meta Muse, US Banks and Retailers, and the Pre-Research Behind China's Agent Data-Processing Standard
A full translation. 网安寻路人 opens a series, written with the core drafting group of the national standard Security Requirements for Data Processing by AI Agents (智能体数据处理安全要求), now being drafted, which shares the standard's preliminary research. The first installment is a case study, not a Chinese rule. It follows how US banks and retailers responded after Meta launched its personal agent Muse on September 8, 2026 and passed 10 million users in about six weeks. Amazon, JPMorgan, Schwab, and Morgan Stanley blocked or restricted outside agents, while Walmart, Shopify, and Best Buy opened controlled gateways. The author traces two fault lines. The first is identity, overreach, and liability: agents logging in with customers' credentials leave platforms unable to tell the customer, a mistaken agent, and an impostor apart. The second is control of shopping entry points and profit pools, from Amazon's roughly $70 billion ad business to banks' low-cost deposits. The industry's answer is moving from 'silent agents' toward verifiable agent identity (KYA), scoped credentials, read-only interfaces, and human approval for high-risk acts. With the original's seven charts, its timeline, its ten-row comparison of institutional policies, and its 26 notes.
- § 04 · ENFORCEMENT
MPS Testing Center Names 35 Apps for Personal-Information Violations: A Finance-Heavy Batch, and 15 Takedowns From the Last One
On October 30, 2026, the National Cybersecurity Notification Center published a Ministry of Public Security notice: the MPS Computer Information System Security Product Quality Supervision and Testing Center found 35 mobile apps collecting or using personal information unlawfully. The apps were tested between August 17 and September 11, 2026. This is the first notice in the MPS app-testing series that DCC has covered, and DCC translates it in full with a consolidated table of all 35 apps. Collection and use without the user's consent accounts for 30 of the 46 findings. The other seven categories, each cited against one to seven apps, cover incomplete or inaccurate notice, unlisted third-party sharing, permissions requested without stating their purpose, forced consent to non-necessary data, collection beyond the necessary scope, no effective account cancellation, and no convenient channel to exercise rights. The batch leans heavily toward finance: by their names alone, at least ten are wealth-management, fund, brokerage, or stock-picking apps. Every app was sampled from a mainstream app store, with Apple's the largest source. The notice also reports that 15 of the previous batch's 42 apps failed retest and were taken down by distribution platforms.
- § 05 · AI-AGENTS
Keeping an AI Agent Inside Its Personal-Information Boundaries: What Meta's PrivacyLib Shows About Making PIPL Purpose Limits Work at Runtime
On October 27, 2026, Meta described PrivacyLib, a layer of its privacy infrastructure that enforces privacy rules where backend services actually read and write data. AgentLib ties each agent tool call to its session, so the check happens even when an agent strings tools together in ways no developer anticipated. 网安寻路人 uses the disclosure to answer a question the PIPL leaves open for agents: how do you hold a system whose execution path unfolds at runtime to Article 6's purpose and minimum-necessity limits and Article 13's legal bases? The author's answer has five parts. Turn the user's task into concrete processing boundaries, set by trusted business systems rather than the model. Check every operation where data actually flows, not at the final answer. Carry provenance and use conditions onto derived content (summaries, vectors, long-term memory), and re-classify when inference yields sensitive personal information. In agent-to-agent delegation, grant only the intersection of the upstream's delegable scope, the sub-agent's own permissions, and the subtask's needs; keep control instructions apart from business data; and map each tool to its legal role under Article 21 (entrusted processing) or Article 23 (provision to another handler). And make the rules themselves current, revocable, and auditable. Five diagrams from the original are included.
- § 06 · AI-ETHICS-REVIEW
Are Companies Ready for AI Ethics Review? A Practitioner's Guide to Committees, Expert Re-Review, and the Registration Platform
AI ethics review has become a live issue: most algorithm- and AI-heavy companies applying to list in Hong Kong have recently received targeted regulatory inquiries on it. This practitioner's guide from 网安寻路人 walks through the regime built on MOST's 2023 Sci-Tech Ethics Review Measures and MIIT's 2026 AI Ethics Review Measures, starting with an 11-question FAQ. Its sharpest reading concerns Article 26 of the AI Measures, which waives expert re-review only where filing or registration already made ethics compliance a condition. The authors say they have seen almost no algorithm or generative AI filing that did, so algorithmic recommendation and deep synthesis providers, and generative AI filers and registrants, almost all still need full ethics review including expert re-review. On the committee, which is not mandatory because a Service Center can stand in, the guide calls for at least 7 members including an outsider, and advises against seating independent directors. It also covers registration on the national platform (within 30 days of setup, annual reports by March 31), the 30-day decision and re-review clocks, a 72-hour emergency track, the pilot Service Centers in ten provinces, and consequences that cross-refer to the Law on Progress of Science and Technology, the PIPL, the DSL, and the CSL.