GPAI Code Copyright chapter

A policy to comply with Union copyright law, incl. respecting reservations of rights

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-COPYRIGHT. Drawn from chapter 08.

Text alternative

Clause: GPAI Code, Copyright chapter.
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Training-data provenance and licence….
Layers: Layer 01, Layer 02.
Evidence record: Evidence record v1.
Record schema: Evidence record.
The same topic in 15 other frameworks; the crosswalk section below links each clause.

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

The artefact that evidences it

Training-data provenance and licence records; policy-as-code for source filtering.

L1 Govern-as-Code
L2 Inventory & Transparency

What this obligation connects to Solid edges are direct relations, dashed ones looser (a case that cites the same article, a framework whose clauses only touch the topic); patterns and controls take the colour of their stack layer. Select a node to open it; the lists below give every relation in full.

Data table: every relation

Relations of AIGE-OBL-GPAICOP-COPYRIGHT
Family	Item	Relation
Same crosswalk topic	AI Verify 8.1.1, 8.2.1, 8.5.1	Core clause
Same crosswalk topic	CSA AICM DSP-20, DSP-21	Core clause
Same crosswalk topic	China Deep Synthesis DeepSyn Art. 14	Core clause
Same crosswalk topic	China GenAI Measures GenAI Art. 7, GenAI Art. 8, GenAI Art. 11, GenAI Art. 7(2), GenAI Art. 4(3)	Core clause
Same crosswalk topic	EU AI Act Art. 10, Art. 10(2)(f)–(g), Art. 4a, Art. 53, Art. 53(1)(c), Art. 5(1)(e), Art. 53(1)(d)	Core clause
Same crosswalk topic	G7 Code G7 Action 11	Core clause
Same crosswalk topic	GAO AI Accountability 2.1, 2.2, 2.4, 2.5	Core clause
Same crosswalk topic	GB/T 45654 GB/T 45654 Corpus security	Core clause
Same crosswalk topic	GDPR Art. 5(1)(c), Art. 25, Art. 9	Core clause
Same crosswalk topic	ISO 42001 A.7, A.7.3, A.4	Core clause
Same crosswalk topic	NIST AI RMF MAP 2, MEASURE 2.10, MEASURE 2.11, GOVERN 6.1, MAP 4.1	Core clause
Same crosswalk topic	Singapore GenAI GenAI 2	Core clause
Same crosswalk topic	TC260 Framework 3.0 TC260 2.1.3, TC260 5.1	Core clause
Same crosswalk topic	UK ATRS ATRS 2.4.3	Core clause
Same crosswalk topic	OWASP LLM LLM05:2026	Related clause

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.

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)
China Deep Synthesis DeepSyn Art. 14 Training-data management (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)
AI Verify 8.1.1 Data Governance: put in place measures to ensure data quality over time (core)
AI Verify 8.2.1 Data Governance: understand the lineage of data (origin, collection, curation, movement) through a data provenance record (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
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

IP and copyright

EU AI Act Art. 53(1)(c) Copyright policy, including rights reservations (core)
NIST AI RMF GOVERN 6.1 GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party's intellectual property or other rights (core)
G7 Code G7 Action 11 Implement data input measures and protect personal data and intellectual property (core)
China GenAI Measures GenAI Art. 7(2) No infringement of IP rights in training data (core)
AI Verify 8.5.1 Data Governance: address risks from third parties, including infringement of their rights (copyright, intellectual property) (core)
EU AI Act Art. 53(1)(d) Public summary of the content used for training
NIST AI RMF MAP 4.1 MAP 4.1: Approaches for mapping AI technology and legal risks of its components, including the use of third-party data or software, are in place, followed, and documented, as are risks of infringement of a third party's intellectual property or other rights
CSA AICM DSP-20 Data Provenance and Transparency
Singapore GenAI GenAI 2 Data
China GenAI Measures GenAI Art. 4(3) Respect IP rights and business ethics
AI Verify 8.1.1 Data Governance: verify data quality, including intellectual property and privacy risks

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-copyright.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 Copyright chapter (AIGE-OBL-GPAICOP-COPYRIGHT). 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-copyright. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{GPAI Code Copyright chapter (AIGE-OBL-GPAICOP-COPYRIGHT)}},
  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-copyright},
  note         = {Version 0.5.0}
}