GPAI Code Safety and Security (systemic-risk models only)

A Safety and Security Framework; model evaluations incl. adversarial testing; systemic-risk assessment and mitigation; serious-incident reporting; model and infrastructure security

By Jorge García Aibar Reviewed 2026-09-19

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

Text alternative

Clause: GPAI Code, Safety and Security chapter.
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Eval and red-team suite.
Layers: Layer 03, Layer 04, Layer 05.
Evidence record: AI incident record.
Record schemas: AI incident record .
The same topic in 24 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-GPAICOP-SAFETY
Instrument: GPAI Code of Practice code
Clause: Safety and Security chapter
Applies from: No date Voluntary · published 2025-07-10

The artefact that evidences it

Eval and red-team suite; adversarial testing harness; incident pipeline; weight-security controls.

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.

Risk management

EU AI Act Art. 9 Risk management system (core)
ISO 42001 6.1.2 AI risk assessment (core)
ISO 42001 6.1.3 AI risk treatment (core)
ISO 42001 8.2 AI risk assessment (operation) (core)
ISO 42001 8.3 AI risk treatment (operation) (core)
NIST AI RMF MAP 1 MAP 1: Context is established and understood (core)
NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
NIST AI RMF MANAGE 1 MANAGE 1: AI risks based on assessments and other analytical output are prioritized, responded to, and managed (core)
TC260 Framework 3.0 TC260 2 Classification of AI safety risks (core)
TC260 Framework 3.0 TC260 Summary table Risks × technological × governance measures (core)
ISO 23894 23894 6.4 Risk assessment (core) (clause not verified)
ISO 23894 23894 6.5 Risk treatment (core) (clause not verified)
NIST AI RMF GOVERN 1.3 GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization's risk tolerance (core)
NIST AI RMF MAP 1.5 MAP 1.5: Organizational risk tolerances are determined and documented (core)
NIST AI RMF MANAGE 1.3 MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented (core)
NIST AI RMF MANAGE 1.4 MANAGE 1.4: Negative residual risks to both downstream acquirers of AI systems and end users are documented (core)
NIST AI RMF MEASURE 3 MEASURE 3: Mechanisms for tracking identified AI risks over time are in place (core)
CSA AICM GRC-02 Risk Management Program (core)
Korea AI Act Art. 34(1)(1) Risk management plan for high-impact AI (core)
UK ATRS ATRS 2.5.2 Risks and mitigations (core)
Singapore Agentic Agentic 2.1 Assess and bound the risks upfront (core)
CoE Convention CoE Art. 16 Risk and impact management framework (core)
prEN 18228 prEN 18228 AI risk management (draft; supports Art. 9) (core) (clause not verified)
GAO AI Accountability 1.6 Risk management: implement an AI-specific risk management plan to systematically identify, analyze, and mitigate risks (core)
ISO 42001 A.6 AI system life cycle
NIST AI RMF MEASURE 2 MEASURE 2: AI systems are evaluated for trustworthy characteristics
TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
China GenAI Measures GenAI Art. 17 Security assessment and algorithm filing
China Algo. Rec. AlgoRec Art. 27 Security assessment
EU AI Act Art. 3 Definitions
ISO 42001 6.1.4 AI system impact assessment
ISO 23894 23894 6.6 Monitoring and review (clause not verified)
CSA AICM MDS-12 Open Model Risk Assessment
OECD AI Principles OECD 1.5(c) Systematic risk management at each lifecycle phase

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)
China Deep Synthesis DeepSyn Art. 7 Information-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
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

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)
China Deep Synthesis DeepSyn Art. 10 Input and output review (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
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)
China Deep Synthesis DeepSyn Art. 15 Technology management and algorithm verification (core)
China Deep Synthesis DeepSyn Art. 20 Security assessment of new products (core)
GB/T 45654 GB/T 45654 Security assessment Security-assessment requirements for generative AI services (core) (clause not verified)
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)
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

Incident response and monitoring

EU AI Act Art. 72 Post-market monitoring by providers and post-market monitoring plan (core)
EU AI Act Art. 73 Reporting of serious incidents (core)
ISO 42001 A.8 Information for interested parties (core)
ISO 42001 10.2 Nonconformity and corrective action (core)
NIST AI RMF MANAGE 4 MANAGE 4: Risk treatments, including response and recovery, and communication plans for the identified and measured AI risks are documented and monitored (core)
TC260 Framework 3.0 TC260 5.3.7 Real-time risk monitoring (core)
TC260 Framework 3.0 TC260 5.3.18 Incident reporting (core)
China GenAI Measures GenAI Art. 14 Handle and report unlawful content (core)
China GenAI Measures GenAI Art. 15 Complaint and reporting mechanism (core)
EU AI Act Art. 26(5) Deployer monitoring, informing the provider and suspending use (core)
GDPR Arts. 33–34 Notification and communication of a personal data breach (core)
NIST AI RMF MANAGE 4.3 MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented (core)
CSA AICM SEF-07 Incident Management and Response (core)
CSA AICM SEF-08 Security Breach Notification (core)
Korea AI Act Art. 32(1) Safety duties for AI above the compute threshold (core)
Singapore GenAI GenAI 4 Incident Reporting (core)
Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test (core)
G7 Code G7 Action 2 Identify and mitigate vulnerabilities, incidents and misuse after deployment (core)
G7 Code G7 Action 4 Responsible information sharing and reporting of incidents (core)
GAO AI Accountability 4.1 Planning: develop plans for continuous or routine monitoring of the AI system (core)
GAO AI Accountability 4.2 Drift: establish the range of data and model drift that is acceptable (core)
EU AI Act Art. 55 Obligations for providers of general-purpose AI models with systemic risk
TC260 Framework 3.0 TC260 App. 2 II.6 Emergency plans
China Algo. Rec. AlgoRec Art. 7 Security management and emergency response
EU AI Act Art. 3(49) Definition of serious incident
EU AI Act Art. 20 Corrective actions and duty of information
NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use
NIST AI RMF GOVERN 4.3 GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing

GPAI and foundation models

EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
EU AI Act Art. 55 Obligations of providers of general-purpose AI models with systemic risk (core)
GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
Korea AI Act Art. 32 Safety duties for AI above the compute threshold (core)
EU AI Act Art. 51 Classification of general-purpose AI models as general-purpose AI models with systemic risk
EU AI Act Art. 56 Codes of practice
CSA AICM MDS-12 Open Model Risk Assessment
CSA AICM MDS-03 Model Documentation
Singapore GenAI GenAI 8 Safety and Alignment R&D
G7 Code G7 Action 1 Identify, evaluate and mitigate risks across the lifecycle, including testing
China GenAI Measures GenAI Art. 7 Lawful data and foundation-model sources

Source

Chapter 08, section GPAI Code of Practice, checked against its sources on the review date above.

Machine-readable

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

Cite this obligation

García Aibar, J. (2026). GPAI Code Safety and Security (systemic-risk models only) (AIGE-OBL-GPAICOP-SAFETY). 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-gpaicop-safety. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{GPAI Code Safety and Security (systemic-risk models only) (AIGE-OBL-GPAICOP-SAFETY)}},
  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-gpaicop-safety},
  note         = {Version 0.5.0}
}