How to Legally Train AI on Copyrighted Works in Greece (2026): Practical Steps for AI Firms, Museums and Publishers

Greece’s copyright landscape has shifted dramatically for anyone building or deploying AI systems that ingest protected content. Law 5179/2025 strengthened collective-management-organisation (CMO) presumptions, introduced tiered administrative fines for online piracy, and clarified how text-and-data-mining (TDM) exceptions interact with cultural-heritage materials, making AI training copyright Greece compliance an immediate priority. The reforms build on Greece’s foundational copyright statute, Law 2121/1993, and complete the country’s transposition of the EU Digital Single Market (DSM) Directive 2019/790. This guide delivers the step-by-step licensing workflows, contract templates and risk-mitigation checklists that AI developers, museum curators and publishers need to operate lawfully under the new regime.

Who must act now:

• AI/ML development teams scraping or licensing Greek-language text, image or audio datasets.
• Museums, archives and cultural institutions granting or considering access to digitised collections for model training.
• Publishers and rights-holders negotiating dataset licences or enforcing opt-out rights.

The Primary Compliance Decision: Do You Need Permission?

Yes, in almost every commercial scenario you need a licence or confirmed exception before using copyrighted works to train an AI in Greece. The narrow TDM research exception (transposed from Article 3 of the DSM Directive) applies only to lawfully accessed works used by research organisations and cultural-heritage institutions for scientific-research purposes. Commercial AI training falls outside that safe harbour unless the rights-holder has not exercised an opt-out under Article 4 of the DSM Directive, as transposed into Greek law. Even where a TDM exception might technically apply, CMO presumptions Greece now allows collecting societies to presume they represent rights-holders for licensing purposes, shifting the practical burden onto the data user.

Quick Primer: The Greek and EU Legal Framework for AI Training Copyright Greece

Greece’s copyright framework rests on Law 2121/1993, which has been progressively amended to transpose EU directives, including the DSM Directive (2019/790), into national law. The 2025–2026 reform cycle, anchored by Law 5179/2025 and subsequent ministerial decisions, represents the most significant update for anyone concerned with permissions for AI training on Greek soil.

Key Statutes and Dates

What Law 5179/2025 Changed

• CMO presumptions reinforced. Collecting societies can now presume authority to license on behalf of rights-holders, even absent an explicit mandate, unless the rights-holder formally opts out.
• Tiered administrative fines. The Hellenic Copyright Organization (OPI) gained broader powers to impose graduated fines for online piracy and unauthorised commercial exploitation.
• Cultural-heritage clarification. The statute explicitly addresses how museums and archives may permit TDM for non-commercial research, while preserving rights-holder opt-out for any commercial application.
• Alignment with EU AI Act transparency obligations. Industry observers expect the interplay between the DSM Directive’s TDM provisions and the EU AI Act’s dataset-disclosure requirements to drive further regulatory guidance throughout 2026.

CMOs and the New Presumptions: What They Mean for AI Training Datasets

Under the strengthened CMO presumptions Greece regime, collecting societies such as AEPI (music), OSDEL (literary works) and OSDEETE (audiovisual) can approach AI developers and assert licensing authority over datasets that include works within their repertoire, without needing to prove an individual mandate from each creator. The likely practical effect is that AI firms will receive licence demands from CMOs first, before any individual rights-holder contacts them directly.

This matters because the burden of rebuttal now rests on the data user. If a CMO claims you have ingested protected works, you must demonstrate either that the works are not in the CMO’s repertoire, that a valid TDM exception applies, or that you already hold a bilateral licence directly from the rights-holder who has opted out of CMO representation.

Step-by-Step: How to Handle a CMO Claim

1. Acknowledge promptly. Respond within the timeframe stated in the CMO’s notice (typically 15–30 days) to preserve negotiation options.
2. Request a repertoire list. Ask the CMO to identify the specific works it claims to represent within your dataset.
3. Cross-reference your data manifest. Compare the CMO’s list against your internal dataset inventory and provenance logs.
4. Produce evidence of exception or licence. If you rely on a TDM research exception, provide documentation proving lawful access and non-commercial purpose. If you hold a bilateral licence, supply a copy.
5. Negotiate terms or remove works. Where no exception or existing licence applies, negotiate a dataset licence with the CMO or remove the disputed works from your training corpus.
6. Document everything. Maintain a written record of all communications and decisions, these records are critical evidence if the matter escalates to enforcement or litigation.

Licensing Datasets for AI: Practical Checklist and Model Clauses

Successfully licensing datasets for AI training in Greece requires a structured approach that begins well before any contract is signed. The checklist below covers the full lifecycle from data inventory through to termination and audit.

1. Compile a data inventory. Catalogue every work in the proposed training dataset, noting title, author/creator, source, date and current rights status (in-copyright, public domain or unknown).
2. Classify rights needed. For each work, determine whether you require reproduction rights, adaptation rights, the right to create derivative training data and the right to distribute model outputs.
3. Identify the correct licensor. Determine whether rights sit with a CMO, a publisher or the individual creator. Cross-reference CMO repertoire databases where available.
4. Negotiate and execute the licence. Use the model clauses in the appendix below as a starting point. Ensure the licence explicitly covers machine-learning training as a permitted use.
5. Document provenance. Maintain hash-level logs linking each ingested work to its licence, exception or public-domain justification.
6. Build in audit rights. Grant the licensor reasonable audit rights so that compliance can be verified without disrupting operations.
7. Include an opt-out/withdrawal mechanism. Allow rights-holders to withdraw works from future training runs, with a defined notice period and technical process.
8. Set liability caps and indemnities. Allocate risk clearly: the licensee typically indemnifies for unauthorised use beyond the licence scope, while the licensor warrants title and authority to license.

Example Redlines for Publishers and Museums

When negotiating with publishers, early indications suggest the most contested clauses involve sublicensing (publishers resist broad sublicensing to third-party model developers) and output liability (who is responsible if a trained model reproduces substantial portions of a licensed work). Museums, by contrast, tend to focus on attribution obligations and restrictions on commercial exploitation of cultural-heritage imagery. Both categories of licensor benefit from clear withdrawal mechanisms that allow them to remove works from future training cycles without unwinding earlier model weights.

Negotiation Tips for High-Value or Heritage Materials

• Tiered pricing. Propose a fee structure that scales with the commercial value of the AI output, rather than a flat per-work rate, to make deals viable for both sides.
• Sandboxed access. Offer to provide model-training access within a controlled environment (the licensor’s servers or a neutral third-party facility) to address concerns about data leakage.
• Sunset and renewal clauses. Heritage institutions often prefer shorter licence terms with renewal options, so they can reassess as the regulatory landscape evolves.
• Moral-rights acknowledgements. Under Greek law, moral rights are inalienable. Include express attribution commitments and a covenant not to distort the integrity of the licensed works during training.

Museums and Cultural Heritage: Special Considerations for AI Training Copyright Greece

Cultural heritage copyright Greece rules create a layered rights landscape that museums must navigate carefully before granting any AI developer access to their digitised collections. The core distinction is between works in the public domain, where the underlying copyright has expired, and works that remain in copyright. Even where the artwork itself is in the public domain, photographs and digital scans of that artwork may attract separate copyright protection for the photographer or digitisation technician under Greek law.

Museums should adopt a three-step policy before permitting any AI training on their holdings:

1. Rights audit of the collection. Classify every item as public-domain, in-copyright (known rights-holder) or orphan work (rights-holder unknown or unlocatable).
2. Photograph/scan rights check. Confirm whether digitisation was performed in-house (rights likely held by the institution) or by an external contractor (separate licence needed).
3. Third-party rights clearance. Identify any third-party interests, for example, an artist’s estate, a donor restriction or a loan agreement that limits reproduction.

Case Example: Training on a Digitised Photographic Collection

Consider a museum holding 10,000 digitised photographs of Byzantine artefacts. The artefacts themselves are centuries old and clearly in the public domain. However, the photographs were taken by a contracted photographer in 2018 and remain in copyright. Before licensing these images for AI training, the museum must either (a) confirm that the photographer’s contract assigned reproduction and adaptation rights to the museum, or (b) negotiate a separate licence with the photographer. Where the contract is silent on machine-learning use, as most pre-2020 contracts will be, a fresh agreement is essential. The risk matrix below helps institutions prioritise clearance efforts.

Technical and Operational Compliance: Data Provenance, TDM Logs and Access Controls

Robust technical controls are not merely best practice, they are the evidence base that will determine whether an AI firm can successfully defend its dataset against a CMO claim or an enforcement action. Operational compliance for AI training copyright Greece encompasses four pillars.

• Dataset manifests. Maintain a machine-readable manifest for every training corpus. Each entry should include the work’s unique identifier (hash), title, source URL, rights status, licence reference and date of ingestion.
• TDM logs. If relying on a TDM exception, log the lawful-access pathway (subscription, open-access licence or institutional agreement), the date of access and whether the rights-holder has exercised an opt-out under Article 4 of the DSM Directive.
• Access controls and sandboxing. Restrict raw-data access to authorised personnel. Where licences require it, train models within sandboxed environments that prevent bulk export of the underlying dataset.
• Output monitoring. Implement filters or classifiers that flag when a model’s output is substantially similar to a specific training-data input, this reduces the risk of generating content that directly reproduces copyrighted material.

Minimal Technical Evidence to Rebut “Access” Claims

In any enforcement scenario, the copyright holder must generally show that the AI system had access to their work and produced substantially similar output. To rebut an access claim, an AI firm should be prepared to produce: (a) the complete dataset manifest showing provenance for all ingested works, (b) licence or exception documentation for each work, and (c) technical logs demonstrating that any works removed following a withdrawal request were purged from active training data. Maintaining these records in an auditable, timestamped format is essential.

Enforcement, Fines and Dispute Scenarios Under the 2025–2026 Reforms

The 2025–2026 reforms significantly escalated the consequences of non-compliance. The Hellenic Copyright Organization (OPI) can now impose tiered administrative fines for online piracy fines Greece without requiring a court order, and CMOs have strengthened standing to pursue civil claims for unlicensed commercial use of copyrighted datasets.

Enforcement actors include OPI (administrative fines and injunctions), public prosecutors (criminal proceedings for wilful large-scale infringement) and CMOs themselves (civil claims for damages and licence fees). Rights-holders may also seek interim injunctions to halt ongoing AI training that uses disputed works.

Mitigation strategies include maintaining comprehensive dataset documentation (as outlined above), responding promptly to CMO notices, carrying professional-indemnity insurance that covers intellectual-property disputes, and implementing a notice-and-takedown protocol for disputed works.

Quick Response Checklist After a CMO Notice or Takedown Demand

1. Log the notice with date, CMO identity and claimed works.
2. Suspend ingestion of disputed works pending review.
3. Cross-reference against your dataset manifest and licence records.
4. Prepare a written response within the stated deadline.
5. Engage local IP counsel if the claim involves substantial volumes or damages.
6. Negotiate a licence, remove the works or assert a documented exception.

Practical Next Steps: A 10-Point Action Plan for AI Copyright Compliance Greece

Whether you are an AI developer, a museum curator or a publisher, the following roadmap translates the legal requirements above into concrete operational steps.

1. Audit all existing training datasets for rights status.
2. Classify each work as public-domain, in-copyright (licensed), in-copyright (unlicensed) or orphan.
3. Obtain licences for all in-copyright, unlicensed works, via CMO blanket licence, bilateral publisher agreement or direct creator licence.
4. Log provenance in a machine-readable manifest with hash, source, rights status and licence reference.
5. Negotiate with CMOs proactively, do not wait for a demand letter.
6. Implement technical controls, sandboxed training, access restrictions and output monitoring.
7. Build a compliance playbook covering ingestion, licensing, withdrawal and dispute response.
8. Update procurement and contract templates to include AI-training-specific clauses (see appendix).
9. Check insurance coverage for IP infringement and related claims.
10. Retain specialist IP counsel with experience in Greek copyright and dataset licensing. Find a Greek copyright lawyer through the Global Law Experts directory, or explore the broader international intellectual property practice area.

Appendix: Sample Licence Clauses and Templates

The following plain-text clause snippets are starting points for negotiation. They should be adapted by qualified counsel to reflect each transaction’s specific circumstances.

• Licence grant (training). “Licensor grants Licensee a non-exclusive, non-transferable licence to reproduce, adapt and process the Licensed Works solely for the purpose of training Licensee’s machine-learning models, subject to the terms herein.”
• Attribution and moral rights. “Licensee shall credit Licensor as the source of the Licensed Works in all dataset documentation. Licensee shall not distort, mutilate or otherwise modify the Licensed Works in a manner prejudicial to the author’s honour or reputation.”
• Audit and provenance. “Licensee shall maintain a machine-readable manifest of all Licensed Works ingested, including hash identifier, date of ingestion and licence reference. Licensor may audit this manifest upon 30 days’ written notice, no more than once per calendar year.”
• Opt-out / withdrawal. “Licensor may withdraw specific Licensed Works from the scope of this licence by written notice. Licensee shall remove the withdrawn works from all active training corpora within 60 days of receipt of notice.”
• Indemnity and limits. “Licensee shall indemnify Licensor against third-party claims arising from Licensee’s use of the Licensed Works beyond the scope of this licence. Licensor warrants that it holds sufficient rights to grant this licence. Aggregate liability of either party shall not exceed [amount] per calendar year.”

Sources

1. Hellenic Copyright Organization (ΟΠΙ), News and Guidance
2. Global Law Experts, Greece Copyright Law Changes 2026
3. Lexology, AI Training and Copyright: Towards a Remuneration Right for Creators
4. Legal 500, Greece: Artificial Intelligence Country Comparative Guide
5. Zepos & Yannopoulos, AI Regulation in Greece
6. EUR-Lex, Directive (EU) 2019/790 (DSM Directive)
7. Greece Government Gazette (FEK), Law 5179/2025
8. Hellenic Copyright Organization (ΟΠΙ), Official Site
9. Tsamichas Law, The Legal Architecture of Digital Art: NFT, Tokenisation & AI