EU AI Act Art. 53: GPAI provider obligations
GPAI provider obligations, incl. a public summary of training content on an AI Office template
AIGE-OBL-EUAIA-ART53. Drawn from chapter 08.
Text alternative
- Clause: EU AI Act, Art. 53.
- Duty holder: GPAI provider.
- Applies from: 2025-08-02, In force.
- Artefact: Model cards.
- Layer: Layer 02 Inventory & Transparency.
- Evidence record: Dataset card, +2 more.
- Record schemas: Dataset card , Model card , Vendor due-diligence response .
- The same topic in 21 other frameworks; the crosswalk section below links each clause.
Data table: the dates
| Date | Step | Detail |
|---|---|---|
| 2025-08-02 | In force | Obligations from 2025-08-02; enforcement from 2026-08-02 |
| 2026-08-02 | Later step | Commission enforcement powers apply |
| 2027-08-02 | Later step | GPAI models placed on the market before 2025-08-02 comply by this date (Art. 111(3)) |
- Id
AIGE-OBL-EUAIA-ART53- Instrument
- EU AI Act (post-Omnibus) law
- Compared side by side
- ISO 42001 vs EU AI Act · NIST AI RMF vs EU AI Act
- Clause
- Art. 53
- Duty holder
- GPAI provider
- Authority
- AI Office
- Applies from
- In force · Obligations from 2025-08-02; enforcement from 2026-08-02
- Later dates
-
- Commission enforcement powers apply
- GPAI models placed on the market before 2025-08-02 comply by this date (Art. 111(3))
- System class
- GPAI model
The artefact that evidences it
Model cards; training-content summary; AIBOM and dataset provenance.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Patterns | AIBOM | Direct |
| Patterns | Training-Data Rights Ledger | Direct |
| Patterns | Vendor / Model Due-Diligence Gate | Direct |
| Open controls | AIGE-CTL-ASSURE-012 AI Bill of Materials per Build | Direct |
| Cases | The Garante's ChatGPT order: launch before a lawful basis | Cites the article |
| Same crosswalk topic | AI Verify 1.1.6, 1.7.2, 1.1.1, 8.1.1, 8.2.1, 4.1.1, 4.8.1, 8.5.1, 11.1.1 | Core clause |
| Same crosswalk topic | CSA AICM MDS-03, MDS-04, DSP-20, DSP-21, MDS-12 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 16, DeepSyn Art. 17, DeepSyn Art. 14 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 12, GenAI Art. 19, GenAI Art. 7, GenAI Art. 8, GenAI Art. 11, GenAI Art. 7(2), GenAI Art. 4(3) | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 14(2), CoE Art. 15(2) | Core clause |
| Same crosswalk topic | EU AI Act Art. 11, Art. 13, Art. 50, Art. 86, Art. 18, Art. 43, Art. 53(1)(d), Art. 50(2), 50(4), Art. 10, Art. 10(2)(f)–(g), Art. 4a, Art. 53(1)(c), Art. 5(1)(e), Art. 55, Art. 51, Art. 56, Art. 40(2), Art. 95(2)(b) | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3, G7 Action 1, G7 Action 11 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.9, 3.5, 1.7, 2.1, 2.2, 2.4, 2.5 | Core clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Content labelling, GB/T 45654 Corpus security | Core clause |
| Same crosswalk topic | GDPR Arts. 13–14, Art. 30, Art. 5(1)(c), Art. 25, Art. 9 | Core clause |
| Same crosswalk topic | GPAI Code Transparency 1.1, Transparency 1.2, Copyright 1.1–1.5, Safety C1, Safety 3.2, Safety C7, Copyright 1.1, Copyright 1.2, Copyright 1.3, Copyright 1.4, Copyright 1.5 | Core clause |
| Same crosswalk topic | ISO 42001 7.5, A.6, A.8, A.7, A.7.3, A.4 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 31, Art. 34(1)(2), Art. 32 | Core clause |
| Same crosswalk topic | NIST AI RMF MAP 1, MEASURE 2.8, MAP 1.6, MEASURE 2.9, MAP 2, MEASURE 2.10, MEASURE 2.11, GOVERN 6.1, MAP 4.1, MEASURE 2.12 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.3, OECD 1.1 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 3, GenAI 2, GenAI 8, GenAI 9 | Core clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 2.1.3, TC260 5.1 | Core clause |
| Same crosswalk topic | UK ATRS ATRS Tier 1, ATRS 2.2, ATRS 2.4.3 | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 16 | Related clause |
| Same crosswalk topic | OWASP LLM LLM05:2026 | Related clause |
Patterns that build it
- AIBOM (layer 2)
- Vendor / Model Due-Diligence Gate (layer 2 and 5)
- Training-Data Rights Ledger (layer 2)
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.
Documentation and transparency
- EU AI Act Art. 11 Technical documentation (core)
- EU AI Act Art. 13 Transparency and provision of information to deployers (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)
- China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable 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)
- AI Verify 1.1.6 Transparency: publicly report accuracy, intended use cases, limitations, risk assessment and mitigations to stakeholders (core)
- AI Verify 1.7.2 Transparency: make model or system cards publicly available where warranted by public interest (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
- AI Verify 1.1.1 Transparency: design an in-house policy on communication to consumers that articulates the principles for transparency
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(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
GPAI and foundation models
- 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)
- GPAI Code Safety C1 Commitment 1: Safety and Security Framework (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
- GPAI Code Safety 3.2 Measure 3.2: Model evaluations
- GPAI Code Safety C7 Commitment 7: Safety and Security Model Reports
- 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
- AI Verify 4.1.1 Safety (technical test): run benchmarks and red-teaming to evaluate a generative model for safety and possible harms
- AI Verify 4.8.1 Safety: identify and prevent content that violates laws or is harmful, including CBRN information and offensive cyber capabilities
IP and copyright
- GPAI Code Copyright 1.1 Draw up, keep up-to-date and implement a copyright policy (core)
- GPAI Code Copyright 1.2 Reproduce and extract only lawfully accessible copyright-protected content (core)
- GPAI Code Copyright 1.3 Identify and comply with rights reservations when crawling the World Wide Web (core)
- GPAI Code Copyright 1.4 Mitigate the risk of copyright-infringing outputs (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
- GPAI Code Copyright 1.5 Designate a point of contact and enable the lodging of complaints
- 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
Environmental impact
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
- NIST AI RMF MEASURE 2.12 MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities as identified in the MAP function are assessed and documented (core)
- OECD AI Principles OECD 1.1 Inclusive growth, sustainable development and well-being (core)
- AI Verify 11.1.1 Inclusive growth, societal and environmental well-being: determine that the AI system benefits the environment (e.g. energy and water use) (core)
- EU AI Act Art. 40(2) Standardisation deliverables on energy and resource performance
- EU AI Act Art. 95(2)(b) Codes of conduct: environmental sustainability
- Singapore GenAI GenAI 9 AI for Public Good
Cases that cite this article
Open controls that evidence it
Draft controls in the open control profiles that map to this row: each states a requirement and the evidence it must leave behind.
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AIGE-CTL-ASSURE-012AI Bill of Materials per Build (Assurance and evidence profile)
Source
Chapter 08, section EU AI Act, post-Omnibus, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-euaia-art53.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). EU AI Act Art. 53: GPAI provider obligations (AIGE-OBL-EUAIA-ART53). 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-euaia-art53. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{EU AI Act Art. 53: GPAI provider obligations (AIGE-OBL-EUAIA-ART53)}},
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-euaia-art53},
note = {Version 0.5.0}
}