China TC260 | AI Safety Framework 2.0 | September 2025

The Complete Guide to China TC260

China's comprehensive AI Safety Governance Framework. Understand content labeling requirements, emergency response guidelines, incident classification, and how it compares to EU and US frameworks. Essential knowledge for global AI operations.

v2.0
Current Version
Sept 2025
Latest Update
4 Levels
Incident Classification
Global
Cooperation Framework
Understanding the Framework

AI Safety Governance Framework

The TC260 AI Safety Governance Framework is China's comprehensive regulatory approach to artificial intelligence. Developed by the National Information Security Standardization Technical Committee (TC260), it establishes binding requirements for organizations deploying AI systems in China.

Version 2.0, released in September 2025, significantly expanded the framework with enhanced content labeling requirements, detailed emergency response guidelines, and mechanisms for international cooperation on AI safety.

The framework applies to all organizations operating AI systems that serve Chinese users, regardless of where the organization is headquartered, making it essential knowledge for global AI governance strategies.

Version 1.0
September 2023

Initial Framework

First comprehensive AI safety governance framework published by TC260

Basic risk classification system
Initial content moderation requirements
Foundational governance principles
Version 2.0
September 2025

Enhanced Framework

Major update with expanded scope and detailed implementation guidance

Enhanced content labeling requirements
Emergency response guidelines
Incident classification system
Global cooperation mechanisms
Core Requirement

Content Labeling Requirements

TC260 mandates comprehensive labeling for all AI-generated content to ensure transparency and traceability.

AI-Generated Content

All content generated by AI systems must be clearly labeled

Visible watermarks on AI-generated images and videos
Text indicators for AI-generated written content
Audio watermarks for AI-generated speech and music
Metadata embedding for traceability
Real-time labeling for streaming content

Implementation: Labels must be persistent, tamper-resistant, and machine-readable

Deepfake Detection

Systems must detect and flag synthetic media content

Automated deepfake detection capabilities
Confidence scoring for synthetic content
User notification mechanisms
Reporting channels for synthetic media
Archive of detected synthetic content

Implementation: Detection systems must achieve minimum accuracy thresholds

Source Attribution

AI-assisted content must disclose AI involvement

Disclosure of AI assistance level
Training data source attribution (where applicable)
Model version and provider identification
Modification history tracking
Human oversight disclosure

Implementation: Attribution must be accessible to end users and regulators

Platform Responsibilities

Platforms distributing AI content have specific obligations

Content verification before distribution
Label preservation across platform transfers
User reporting mechanisms
Content takedown procedures
Regular compliance audits

Implementation: Platforms must implement technical and organizational measures

Incident Management

Emergency Response Guidelines

TC260 Framework 2.0 introduces a comprehensive four-level emergency response system for AI safety incidents.

Level 1
Critical
Response: Immediate (within 1 hour)

Critical AI Safety Incident

Incidents causing or likely to cause widespread harm to public safety, national security, or social stability

Example Incidents:

AI system causing physical harm at scale
Critical infrastructure compromise via AI
Widespread dissemination of harmful AI-generated content
AI system manipulation affecting public order

Required Actions:

1
Immediate system shutdown
2
Notify national authorities
3
Public warning issuance
4
Evidence preservation
5
Inter-agency coordination
Level 2
High
Response: Within 4 hours

Significant AI Safety Incident

Incidents with significant impact on individuals, organizations, or specific sectors

Example Incidents:

Large-scale data breach via AI system
AI-enabled fraud affecting multiple victims
Significant bias incidents in public services
AI system failures in critical applications

Required Actions:

1
System isolation
2
Sector regulator notification
3
Affected party notification
4
Root cause investigation
5
Mitigation plan development
Level 3
Medium
Response: Within 24 hours

Moderate AI Safety Incident

Incidents with limited scope but requiring formal response and reporting

Example Incidents:

Localized AI system malfunction
Individual privacy violations
Contained bias incidents
Minor security vulnerabilities

Required Actions:

1
Incident documentation
2
Internal investigation
3
Corrective measures
4
Regulatory reporting (if required)
5
Lessons learned capture
Level 4
Low
Response: Within 72 hours

Minor AI Safety Incident

Incidents with minimal impact, handled through normal operational procedures

Example Incidents:

Minor accuracy issues
User experience problems
Documentation gaps
Minor compliance deviations

Required Actions:

1
Standard incident logging
2
Operational review
3
Process adjustment
4
Internal reporting
5
Preventive measures
Incident Taxonomy

Incident Classification System

TC260 categorizes AI safety incidents into four main categories for appropriate handling.

Safety Incidents

Incidents affecting physical or psychological safety

Physical harm caused by AI systems
Psychological harm from AI content
Safety-critical system failures
Autonomous system malfunctions

Security Incidents

Incidents affecting system or data security

AI model theft or leakage
Adversarial attacks on AI systems
Data poisoning incidents
Unauthorized AI system access

Ethics Incidents

Incidents involving ethical violations

Discrimination and bias manifestation
Privacy violations
Lack of transparency
Accountability gaps

Compliance Incidents

Incidents involving regulatory non-compliance

Content labeling violations
Registration requirement violations
Reporting failures
Cross-border data violations
Global Context

How It Compares to EU/US Frameworks

Understanding the similarities and differences between TC260, EU AI Act, and NIST AI RMF.

Aspect
TC260 Framework
EU AI Act
NIST AI RMF
Legal StatusBinding regulatory framework with enforcement mechanismsBinding regulation with significant penaltiesVoluntary guidance framework
Risk ApproachRisk classification with content-specific requirementsFour-tier risk classification systemFlexible, continuous risk assessment
Content LabelingMandatory labeling for all AI-generated contentRequired for specific AI interactions and deepfakesRecommended as transparency measure
RegistrationMandatory algorithm registration with authoritiesEU database registration for high-risk AINo registration requirement
Real-time OversightActive monitoring by regulatorsMarket surveillance by authoritiesSelf-assessment and voluntary disclosure
Content ModerationDetailed content moderation requirementsGeneral transparency obligationsAddressed through trustworthy characteristics
Cross-borderData localization and cross-border transfer controlsGDPR-aligned data transfer mechanismsNo specific cross-border provisions
Emergency ResponseDetailed incident classification and responseSerious incident reporting requirementsGeneral incident response guidance
Mandatory Requirement

Algorithm Registration

China requires registration of AI algorithms that influence public opinion or have significant social impact.

Registration Scope

Recommendation algorithms
Content generation algorithms
Synthesis/deepfake algorithms
Decision-making algorithms affecting rights

Required Information

Algorithm name and type
Application scenarios
Technical characteristics
Security assessment results
Service provider information

Ongoing Obligations

Regular security assessments
Algorithm updates notification
Annual compliance reports
User complaint handling
International Engagement

Global Cooperation Mechanisms

TC260 Framework 2.0 introduces mechanisms for international cooperation on AI safety governance.

Bilateral Agreements

Framework for AI safety cooperation with individual countries

Information sharing protocols
Joint incident investigation procedures
Mutual recognition discussions
Technical standards harmonization

Multilateral Forums

Participation in international AI governance bodies

UN AI governance discussions
G20 AI principles alignment
ISO/IEC standards development
Regional cooperation frameworks

Industry Cooperation

Engagement with international technology companies

Compliance guidance for foreign companies
Technical exchange programs
Joint research initiatives
Best practice sharing

Academic Exchange

International research collaboration on AI safety

Joint research programs
Academic conference participation
Publication and knowledge sharing
Talent exchange programs
Global AI Governance

Master Multi-Framework Compliance

Our comprehensive training program covers TC260 alongside EU AI Act, NIST AI RMF, and ISO 42001. Build expertise in global AI governance and lead your organization's compliance efforts.

TC260 + EU + US Coverage
Practical Implementation
Global Best Practices
Got Questions?

Frequently Asked Questions

Common questions about China's TC260 AI Safety Governance Framework.

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