China Provisions on Deep Synthesis (in force 2023-01-10)

Conspicuous labels where synthetic content could mislead the public and non-removable technical marks; training-data management; separate consent for face and voice editing; filing and security assessment for opinion-shaping functions

By Jorge García Aibar Reviewed 2026-09-20

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-CN-DEEPSYN. Drawn from chapter 08.

Text alternative

Clause: China Deep Synthesis, CAC Order No. 12.
Duty holder: Not stated.
Applies from: 2023-01-10, In force.
Artefact: Content-provenance pipeline.
Layers: Layer 02, Layer 03, Layer 04.
Evidence record: Evidence record v1.
Record schema: Evidence record.
The same topic in 22 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-CN-DEEPSYN
Instrument: China Provisions on Deep Synthesis (2023) law
Clause: CAC Order No. 12
Applies from: 2023-01-10 In force · Binding; in force 2023-01-10

The artefact that evidences it

Content-provenance pipeline (visible label plus metadata mark); training-data governance record; consent gate; pre-release security assessment.

Patterns that build it

No pattern in the catalogue names this clause on its "Maps to" line yet; the artefact above is the engineering answer.

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Governance and accountability

EU AI Act Art. 17 Quality management system (core)
ISO 42001 5.1 Leadership and commitment (core)
ISO 42001 5.2 AI policy (core)
ISO 42001 5.3 Roles, responsibilities and authorities (core)
ISO 42001 A.2 Policies related to AI (core)
ISO 42001 A.3 Internal organization (core)
NIST AI RMF GOVERN 1 GOVERN 1: Policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks are in place, transparent, and implemented effectively (core)
NIST AI RMF GOVERN 2 GOVERN 2: Accountability structures are in place so that the appropriate teams and individuals are empowered, responsible, and trained (core)
TC260 Framework 3.0 TC260 4 Comprehensive governance measures (core)
TC260 Framework 3.0 TC260 5.3.12 Traceable chain of responsibility (core)
China GenAI Measures GenAI Art. 9 Provider responsibility as content producer (core)
China Algo. Rec. AlgoRec Art. 7 Algorithm-security responsibility system (core)
GDPR Art. 5(2) Accountability (core)
ISO 42001 9.3 Management review (core) (clause not verified)
CSA AICM GRC-01 Governance Program Policy and Procedures (core)
CSA AICM GRC-06 Governance Responsibility Model (core)
UK ATRS ATRS 2.1 Owner and responsibility (core)
Singapore GenAI GenAI 1 Accountability (core)
Singapore Agentic Agentic 2.2.1 Clear allocation of responsibilities within and outside the organisation (core)
OECD AI Principles OECD 1.5 Accountability (core)
GAO AI Accountability 1.2 Roles and responsibilities: define clear roles, responsibilities, and delegation of authority for the AI system (core)
EU AI Act Art. 4 AI literacy
EU AI Act Art. 87 Reporting of infringements and protection of reporting persons
ISO 42001 7.2 Competence (clause not verified)
ISO 42001 9.2 Internal audit (clause not verified)
ISO 42001 10.1 Continual improvement (clause not verified)
NIST AI RMF GOVERN 4 GOVERN 4: Organizational teams are committed to a culture that considers and communicates AI risk
NIST AI RMF GOVERN 5 GOVERN 5: Processes are in place for robust engagement with relevant AI actors
GPAI Code Safety C8 Commitment 8: Systemic risk responsibility allocation
Korea AI Act Art. 36 Domestic representative
CoE Convention CoE Art. 9 Accountability and responsibility
G7 Code G7 Action 5 Develop, implement and disclose AI governance and risk-management policies
EN 18286 EN 18286 Quality management system for EU AI Act regulatory purposes
GAO AI Accountability 1.1 Clear goals: define clear goals and objectives for the AI system
GAO AI Accountability 1.3 Values: demonstrate a commitment to values and principles established by the entity

Data governance

EU AI Act Art. 10 Data and data governance (core)
ISO 42001 A.7 Data for AI systems (core)
TC260 Framework 3.0 TC260 2.1.3 Data safety risks (core)
China GenAI Measures GenAI Art. 7 Training-data lawful sourcing (core)
China GenAI Measures GenAI Art. 8 Data-annotation standards (core)
China GenAI Measures GenAI Art. 11 Protection of user input and records (core)
GB/T 45654 GB/T 45654 Corpus security Training-corpus (data) security requirements (core) (clause not verified)
EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
GDPR Art. 5(1)(c) Data minimisation (core)
GDPR Art. 25 Data protection by design and by default (core)
ISO 42001 A.7.3 Acquisition of data (core) (clause not verified)
CSA AICM DSP-20 Data Provenance and Transparency (core)
UK ATRS ATRS 2.4.3 Development data specification (core)
Singapore GenAI GenAI 2 Data (core)
GAO AI Accountability 2.1 Sources: document sources and origins of data used to develop the models (core)
GAO AI Accountability 2.2 Reliability: assess reliability of data used to develop the models (core)
EU AI Act Art. 4a Special-category data for bias detection
ISO 42001 A.4 Resources for AI systems
NIST AI RMF MAP 2 MAP 2: Categorization of the AI system is performed
NIST AI RMF MEASURE 2.10 MEASURE 2.10: Privacy risk of the AI system is examined and documented
NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias are evaluated and results are documented
TC260 Framework 3.0 TC260 5.1 Model R&D safety guidelines
EU AI Act Art. 53 Obligations for providers of general-purpose AI models
EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations
EU AI Act Art. 5(1)(e) Prohibited: untargeted scraping of facial images
GPAI Code Copyright 1.1–1.5 Commitment 1: Copyright policy (Measures 1.1 to 1.5)
GDPR Art. 9 Processing of special categories of personal data
CSA AICM DSP-21 Data Poisoning Prevention & Detection
OWASP LLM LLM05:2026 Data and Model Poisoning
GAO AI Accountability 2.4 Variable selection: assess data variables used in the AI component models
GAO AI Accountability 2.5 Enhancement: assess the use of synthetic, imputed, and/or augmented data

Documentation and transparency

EU AI Act Art. 11 Technical documentation (core)
EU AI Act Art. 13 Transparency and provision of information to deployers (core)
EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
ISO 42001 7.5 Documented information (core)
ISO 42001 A.6 AI system life cycle (core)
ISO 42001 A.8 Information for interested parties (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
GDPR Arts. 13–14 Information to be provided to the data subject (core)
CSA AICM MDS-03 Model Documentation (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
UK ATRS ATRS Tier 1 Summary information (core)
Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
OECD AI Principles OECD 1.3 Transparency and explainability (core)
G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
NIST AI RMF MAP 1 MAP 1: Context is established and understood
NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
China GenAI Measures GenAI Art. 19 Disclosure to regulators
China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
EU AI Act Art. 86 Right to explanation of individual decision-making
EU AI Act Art. 18 Documentation keeping
EU AI Act Art. 43 Conformity assessment
EU AI Act Art. 53(1)(d) Public summary of the content used for training
EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
GPAI Code Transparency 1.2 Providing relevant information
GDPR Art. 30 Records of processing activities
NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
CSA AICM MDS-04 Model Documentation Requirements
Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
UK ATRS ATRS 2.2 Description and rationale
CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
GAO AI Accountability 1.7 Specifications: establish and document technical specifications

Inventory and registration

EU AI Act Art. 49 Registration (core)
EU AI Act Art. 71 EU database for high-risk AI systems (core)
ISO 42001 A.4 Resources for AI systems (core)
NIST AI RMF GOVERN 1.6 GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities (core)
China Algo. Rec. AlgoRec Art. 24 Algorithm filing (core)
China GenAI Measures GenAI Art. 17 Algorithm filing (core)
UK ATRS ATRS Tier 1 Summary information (the published record) (core)
EU AI Act Art. 6 Classification rules for high-risk AI systems
TC260 Framework 3.0 TC260 App. 2 II.2 Identity and access management
TC260 Framework 3.0 TC260 4.4.1 CII registration and filing
EU AI Act Art. 3(1) Definition of an AI system
EU AI Act Art. 52 Procedure
NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness
CSA AICM STA-08 Supply Chain Inventory
CSA AICM IAM-03 Identity Inventory
Korea AI Act Art. 33 Confirmation of high-impact AI
GAO AI Accountability 3.1 Documentation: catalog model and non-model components, along with operating specifications and parameters

Runtime guardrails

ISO 42001 A.9 Use of AI systems (core)
NIST AI RMF MANAGE 2 MANAGE 2: Strategies to maximize AI benefits and minimize negative impacts are planned, prepared, implemented, documented, and informed by relevant AI actors (core)
TC260 Framework 3.0 TC260 App. 2 II.5 Dynamic runtime management (core)
TC260 Framework 3.0 TC260 3.2.1 Technological countermeasures for agentic AI (core)
China GenAI Measures GenAI Art. 10 Guided, bounded use (core)
China GenAI Measures GenAI Art. 14 Stop unlawful generation (core)
CSA AICM TVM-13 Guardrails (core)
CSA AICM AIS-09 Input Validation (core)
CSA AICM AIS-10 Output Validation (core)
OWASP LLM LLM01:2026 Prompt Injection (core)
OWASP LLM LLM10:2026 Improper Output Handling (core)
Singapore Agentic Agentic 2.3.1 During design and development, use technical controls (core)
EU AI Act Art. 5 Prohibited AI practices
EU AI Act Art. 15 Accuracy, robustness and cybersecurity
ISO 42001 A.6 AI system life cycle
China Algo. Rec. AlgoRec Art. 8 Periodic algorithm review
China Algo. Rec. AlgoRec Art. 9 Feature database for unlawful content
EU AI Act Art. 5(1)(a)–(b) Manipulative techniques; exploitation of vulnerabilities
GPAI Code Safety C5 Commitment 5: Safety mitigations
OWASP LLM LLM06:2026 Unbounded Consumption

Robustness, security and evaluations

EU AI Act Art. 15 Accuracy, robustness and cybersecurity (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk (core)
ISO 42001 A.6 AI system life cycle (core)
NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics (core)
TC260 Framework 3.0 TC260 3 Technological countermeasures (core)
TC260 Framework 3.0 TC260 App. 2 II.6 Sandbox validation and red teaming (core)
GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
GPAI Code Safety 3.2 Measure 3.2: Model evaluations (core)
NIST AI RMF MEASURE 2.7 MEASURE 2.7: AI system security and resilience as identified in the MAP function are evaluated and documented (core)
CSA AICM MDS-06 Adversarial Attack Analysis (core)
CSA AICM MDS-07 Robustness against Adversarial Attack / Model Hardening (core)
Singapore GenAI GenAI 5 Testing and Assurance (core)
Singapore GenAI GenAI 6 Security (core)
Singapore Agentic Agentic 2.3.2 Before deploying, test agents (core)
CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified (core)
OECD AI Principles OECD 1.4 Robustness, security and safety (core)
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing (core)
GAO AI Accountability 3.7 Assessment: assess performance against defined metrics to ensure the AI system functions as intended and is sufficiently robust (core)
EU AI Act Art. 60 Testing of high-risk AI systems in real-world conditions outside AI regulatory sandboxes
ISO 42001 9.1 Monitoring, measurement, analysis and evaluation
TC260 Framework 3.0 TC260 5.3.14 Resilience
China GenAI Measures GenAI Art. 17 Security assessment
EU AI Act Art. 15(3) Declared accuracy levels and metrics
EU AI Act Art. 9 Risk management system
EU AI Act Art. 42(3) Presumption of conformity for cybersecurity (Cyber Resilience Act)
GPAI Code Safety C6 Commitment 6: Security mitigations
GDPR Art. 32 Security of processing
NIST AI RMF MEASURE 2.1 MEASURE 2.1: Test sets, metrics, and details about the tools used during TEVV are documented
NIST AI RMF MEASURE 1 MEASURE 1: Appropriate methods and metrics are identified and applied
CSA AICM AIS-05 Application Security Testing
OWASP LLM LLM01:2026 Prompt Injection
OWASP Agentic ASI05 Unexpected Code Execution (RCE)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold
GAO AI Accountability 3.2 Metrics: define performance metrics that are precise, consistent, and reproducible

Supply chain and third parties

EU AI Act Art. 25 Responsibilities along the AI value chain (core)
ISO 42001 A.10 Third-party and customer relationships (core)
NIST AI RMF GOVERN 6 GOVERN 6: Policies and procedures are in place to address AI risks and benefits arising from third-party software and data and other supply chain issues (core)
NIST AI RMF MAP 4 MAP 4: Risks and benefits are mapped for all AI system components including third-party software and data (core)
NIST AI RMF MANAGE 3 MANAGE 3: AI risks and benefits from third-party entities are managed (core)
TC260 Framework 3.0 TC260 App. 2 II.4 Supply chain and tool management (core)
EU AI Act Art. 25(4) Written agreement with third-party suppliers (core)
GDPR Art. 28 Processor (core)
NIST AI RMF MANAGE 3.1 MANAGE 3.1: AI risks and benefits from third-party resources are regularly monitored, and risk controls are applied and documented (core)
CSA AICM STA-10 Supply Chain Risk Management (core)
CSA AICM STA-09 Service Bill of Material (BOM) (core)
OWASP LLM LLM04:2026 Supply Chain (core)
OWASP Agentic ASI04 Agentic Supply Chain Vulnerabilities (core)
EU AI Act Art. 26 Obligations of deployers of high-risk AI systems
TC260 Framework 3.0 TC260 4.4.4 Open-source ecosystem
China GenAI Measures GenAI Art. 7 Lawful data and model sources
EU AI Act Art. 22 Authorised representatives of providers of high-risk AI systems
EU AI Act Art. 23 Obligations of importers
EU AI Act Art. 24 Obligations of distributors
EU AI Act Art. 54 Authorised representatives of providers of general-purpose AI models
GPAI Code Transparency 1.2 Providing relevant information
GDPR Arts. 44–46 Transfers to third countries
NIST AI RMF GOVERN 6.2 GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third-party data or AI systems deemed to be high-risk
UK ATRS ATRS 2.1.4 Third party involvement
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property
GAO AI Accountability 2.6 Dependency: assess interconnectivities and dependencies of data streams that operationalize the AI system

Content provenance and deepfakes

EU AI Act Art. 50(2) Machine-readable marking of synthetic content (core)
EU AI Act Art. 50(4) Disclosure of deep fakes (core)
Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
Singapore GenAI GenAI 7 Content Provenance (core)
G7 Code G7 Action 7 Deploy content authentication and provenance mechanisms where feasible (core)
China AI Labelling Label Art. 4 Explicit labels for generated content (core)
China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
EU AI Act Art. 3(60) Definition of deep fake
CSA AICM MDS-09 Model Signing/Ownership Verification
OWASP LLM LLM07:2026 Misinformation
China GenAI Measures GenAI Art. 12 Labelling of generated content

Source

Chapter 08, section China, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-cn-deepsyn.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). China Provisions on Deep Synthesis (in force 2023-01-10) (AIGE-OBL-CN-DEEPSYN). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-cn-deepsyn. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{China Provisions on Deep Synthesis (in force 2023-01-10) (AIGE-OBL-CN-DEEPSYN)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-cn-deepsyn},
  note         = {Version 0.5.0}
}