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Prepare for EU AI Act compliance by classifying your model and provider, maintaining the required technical and downstream documentation, operating a Union-copyright policy, publishing a detailed training-content summary, and adding systemic-risk controls when the model qualifies. The general-purpose AI (GPAI) obligations applied on 2 August 2025; enforcement of the GPAI rules starts on 2 August 2026. Models placed on the EU market before 2 August 2025 follow later transition rules in the European Commission’s implementation timeline.
This guide gives a practical evidence-building sequence. It separates baseline duties for every GPAI provider from the additional obligations for systemic-risk models, explains how the voluntary GPAI Code of Practice fits, and identifies the records an AI Office review would expect to see.
What does the EU AI Act require from a GPAI provider?
Every provider of a general-purpose AI model must be able to explain how the model was developed, support downstream users, respect Union copyright law, and disclose the content used for training. These are ongoing provider responsibilities, not one-time paperwork completed at launch.
The four baseline deliverables
- Technical documentation: a current file describing the model, its development and evaluation, capabilities, limitations, and other information needed by the European Commission’s AI Office and downstream AI-system providers.
- Downstream documentation: relevant information that lets organizations integrating the model understand its capabilities, limitations, intended uses, and conditions for safe deployment.
- Union-copyright policy: an operating policy for complying with EU copyright and related-rights law, including handling rights reservations and using state-of-the-art methods to identify and respect them.
- Public training-content summary: a sufficiently detailed public summary of the content used to train the model, structured with the Commission’s template.
The provider must make the technical documentation available to the AI Office when requested and supply relevant information to downstream AI-system providers. Treat each deliverable as controlled evidence linked to a specific model version, not as a generic description of the company.
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When do extra duties apply?
A provider whose GPAI model presents systemic risk must add notification, formal model evaluation, systemic-risk assessment and mitigation, serious-incident tracking and reporting, and cybersecurity protections for the model and the physical infrastructure that operates it. The provider should preserve test plans, findings, mitigation decisions, residual-risk acceptance, incident logs, and governance approvals.
How do I determine whether my model is GPAI or systemic risk?
Start with the European Commission’s scope indicators and record the reasoning in your compliance file. The Commission’s 2025 guidance describes models trained with more than 1023 floating-point operations (FLOP) and able to generate language as falling within the cited GPAI definition. Models above 1025 FLOP are presumed to pose systemic risk, although the Commission says that threshold is under review.
Build a classification record
- Identify the provider: record the legal entity responsible for placing the model on the EU market, its establishment country, affiliates involved in development, and the contact responsible for regulatory requests.
- Describe the model: capture the model name, release and checkpoint versions, modalities, language-generation capability, parameter or architecture information used internally, and intended distribution method.
- Calculate training compute: document how FLOP was estimated, the hardware and training runs included, assumptions about repeated or fine-tuning runs, and the person who approved the calculation.
- Check the systemic-risk indicator: compare the documented compute result with the 1025-FLOP presumption, while noting that the threshold is under review and that other evidence may affect the assessment.
- Record the conclusion: state whether the model is treated as GPAI, whether systemic-risk obligations are triggered, which assumptions remain uncertain, and when the classification will be revisited.
Do not treat the compute threshold as a substitute for governance. A model below a provisional threshold still needs the baseline GPAI documentation if it falls within scope, and a change in training method, capability, or legal guidance can require a new assessment.
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What if the provider is outside the EU?
A provider established outside the EU must appoint an authorised representative in the Union before placing its GPAI model on the EU market. Keep the signed mandate, representative’s contact details, scope of authority, and procedures for forwarding AI Office requests in the technical compliance system.
How should I build the technical documentation?
Create a version-controlled technical file that another qualified reviewer can use to understand what was built, how it was trained and tested, and where it should not be used. The file must be detailed enough for regulatory inspection and practical enough for downstream providers to integrate safely.
Document the model and development process
- Architecture, model family, modalities, context limits, major components, and dependencies.
- Training objectives, data-processing stages, pre-training and fine-tuning methods, reinforcement or preference-optimization methods, and material changes between versions.
- Compute accounting, hardware environment, training duration, significant failures, restarts, and changes to the training run.
- Release channels, access controls, licence terms, update policy, and whether users receive a fixed model, an automatically updated endpoint, or both.
Document data, testing, capabilities, and limitations
- Data categories and collection sources at a level consistent with copyright and privacy obligations.
- Filtering, deduplication, quality checks, safety screening, and known gaps or geographic and linguistic biases.
- Evaluation methodologies, datasets or test suites, prompts and configurations, scoring rules, confidence limits, and results by relevant task or population.
- Observed capabilities, unsupported uses, failure modes, hallucination patterns, robustness limits, and conditions under which performance changes.
- Post-release monitoring, issue triage, corrective-update process, and the person accountable for approving a new version.
Make downstream information usable
Downstream providers need more than a marketing model card. Give them operating information they can apply in a risk assessment: intended and reasonably foreseeable uses, prohibited or unsafe uses, input and output constraints, known security considerations, evaluation results, monitoring expectations, and contact channels for incidents. Tie each statement to a document version so a downstream provider can show which information it relied on.
Use an evidence index
Maintain an index with the model version, document owner, approval date, change history, retention location, and the legal duty or control that each record supports. Mark records that are confidential but available to the AI Office on request, and separate them from the public training-content summary.
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How do I implement a Union-copyright policy?
The policy must operate in practice, not merely state that the provider respects copyright. It should govern data ingestion, rights reservations, technical detection, complaints, and remediation throughout the model lifecycle.
Set ownership and intake controls
- Assign a policy owner with authority over data procurement, engineering controls, and legal escalation.
- Inventory each training-data source, licence or access basis, jurisdictional assumptions, and restrictions on copying, mining, redistribution, or commercial use.
- Define an intake gate that blocks or quarantines material when rights status is unknown, a reservation is detected, or the source terms prohibit the intended use.
- Record approvals, exceptions, compensating controls, and the decision-maker for every material source category.
Respect rights reservations with current technology
Use state-of-the-art technologies and processes to identify and respect reservations of rights. Depending on the source, that can include machine-readable signals, robots or metadata instructions, access-control records, domain and repository exclusions, and a documented process for interpreting ambiguous signals. Record the technology version, scan date, coverage, and known false-positive or false-negative limitations.
Prove that controls continue after training
Keep evidence of filtering and exclusion lists, takedown or opt-out requests, complaints, investigations, retraining or model-update decisions, and corrective actions. A policy that was followed for one release does not automatically cover a later data refresh or fine-tuning run; link each release to the controls actually applied.
What must the public training-content summary contain?
Publish a sufficiently detailed summary of the content used to train the model, using the European Commission’s template as the operating structure. The summary should help the public understand the major sources and categories of training content without exposing protected personal data or confidential security details.
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Prepare the source inventory
- List major data categories and source types, such as publicly available text, licensed material, code, images, audio, synthetic data, and human-generated examples, where applicable.
- Describe collection periods, geographic or language coverage, material filtering, deduplication, and safety processing.
- Explain how licensed, user-provided, or synthetic data were incorporated and how excluded or rights-reserved content was handled.
- Identify the model version covered and material changes from earlier versions.
Apply a publication review
Use a formal approval record linking the published summary to the model version and internal source inventory. Check that the description is detailed enough to be meaningful, consistent with the technical file, and reviewed for privacy, confidential information, security-sensitive details, and copyright concerns. Retain the submitted version, publication date, approvers, and any later correction.
What additional controls does a systemic-risk model need?
Once a model is classified as systemic risk, create a separate workstream with accountable leadership, independent challenge where feasible, and evidence that controls operate continuously.
Notify the Commission
Prepare the information required for Commission notification and keep proof of submission, acknowledgements, and subsequent updates. The notification record should identify the provider, model versions, classification basis, contact points, and material changes.
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Evaluate the model
Use structured evaluations that probe dangerous capabilities, misuse pathways, robustness, autonomy or tool-use behavior where relevant, and the effect of scaling or fine-tuning. Preserve test protocols, evaluator qualifications, environments, prompts, results, limitations, and whether testing was internal or independent.
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Assess and mitigate systemic risk
Maintain a risk register that connects each identified hazard to preventive and detective controls. Document mitigations such as capability restrictions, access tiers, monitoring, abuse detection, deployment limits, rollback plans, and user or downstream-provider safeguards. Record residual risk, the rationale for accepting it, and the executive or board-level owner.
Track serious incidents
Define what counts as a serious incident, a reporting trigger, escalation deadlines, evidence preservation, and communications ownership. Keep a chronological incident log with affected versions, detection source, impact assessment, containment, notification decision, root-cause analysis, corrective action, and closure approval.
Secure the model and its infrastructure
Protect model weights, training systems, evaluation environments, serving endpoints, credentials, and physical infrastructure against theft, tampering, misuse, and unauthorized access. Evidence can include threat models, access reviews, key-management records, vulnerability remediation, segmentation, logging, backup and recovery tests, and red-team findings.
Should I sign the GPAI Code of Practice?
The GPAI Code of Practice is voluntary, but the European Commission and Member States confirmed that it is an adequate tool for providers to demonstrate compliance. It was published on 10 July 2025 after participation by nearly 1,000 stakeholders, Member State representatives, and international observers. Signing can reduce uncertainty and administrative effort when the Code’s commitments match your model and controls; it is not the only lawful route.
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The Code addresses transparency, copyright, and systemic-risk safety and security. Map every applicable commitment to an owner, control, record, review frequency, and model version. Do not sign until the organization can produce the evidence the commitment implies.
When an alternative route may fit
A provider that does not sign may use alternative adequate means. Build a legal-coverage matrix that maps each GPAI duty to policies, technical controls, test results, approvals, and records available to the AI Office. An alternative route needs at least the same specificity on copyright controls, training transparency, downstream information, systemic-risk evaluation, incident response, cybersecurity, governance, and update cadence.
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| Decision area | Code of Practice route | Alternative adequate means |
|---|---|---|
| Legal-coverage mapping | Use the Code commitments as a structured baseline, then map evidence to each commitment. | Build and maintain your own complete map from each legal duty to a control and record. |
| Evidence specificity | Standardized commitments can make gaps easier to identify. | You must define equivalent detail, ownership, approval, and retention yourself. |
| Systemic-risk depth | Follow the Code’s safety and security measures where applicable and document implementation. | Show an equally credible evaluation, mitigation, incident, and cybersecurity framework. |
| Copyright and training transparency | Use the Code’s transparency and copyright measures alongside the Commission template. | Demonstrate operational rights controls and a complete public training-content summary independently. |
| Regulatory review | The Commission has stated that the Code is an adequate demonstration tool. | Be prepared to explain why your alternative means are adequate and produce supporting records. |
| Change management | Monitor Code updates and revise controls when commitments or guidance change. | Maintain your own legal-change monitoring and reassess every model release. |
What should the compliance project timeline look like?
Use the dates as gates in a release calendar rather than as reminders added after deployment.
- Now—classify and assign ownership: complete the provider, model, compute, GPAI, and systemic-risk assessment; appoint an EU authorised representative if required.
- Before each market release—freeze the evidence set: approve the technical file, downstream documentation, copyright controls, training-content summary, and version-specific change log.
- For systemic-risk models—run the extra controls: complete notification, evaluations, mitigation review, incident readiness, and cybersecurity checks before release or material capability changes.
- By 2 August 2026—be enforcement-ready: GPAI enforcement begins on this date, so ensure records can be produced promptly and that responsible staff know the escalation path.
- For legacy models—verify transition treatment: models placed on the market before 2 August 2025 have a later transition date shown in the Commission timeline; confirm the applicable rule for each model rather than applying a single date to all releases.
- After every legal or technical change—reassess: maintain a legal-change calendar and check transition rules before a new model, major fine-tune, data refresh, or deployment expansion.
What records should an AI Office review be able to find?
Organize the evidence so a reviewer can move from the provider and model identity to the control, result, and approval without relying on personal inboxes or undocumented explanations.
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- Model inventory, version history, modalities, distribution channels, compute methodology, and classification decisions.
- Technical documentation, evaluation protocols and results, capability and limitation statements, and downstream information packages.
- Copyright policy, source inventory, rights-reservation detection, filtering logs, complaints, corrective actions, and policy approvals.
- Published training-content summary, Commission-template submission, publication record, and version linkage.
- For systemic-risk models: notification evidence, risk register, mitigation decisions, serious-incident log, reporting decisions, security assessments, and infrastructure controls.
- Code-of-Practice signature and implementation evidence, or the alternative-means legal-coverage matrix.
- Change calendar, review dates, retention rules, and records showing that updates were assessed.
What are the most common preparation failures?
Treating a model card as the entire technical file
A short public model card rarely contains development history, test conditions, limitations, downstream instructions, or the evidence needed for an AI Office request. Keep the public explanation concise but maintain a substantially richer controlled file.
Using a policy without operational proof
A copyright statement is not evidence that rights reservations were detected or honored. Retain source decisions, technical scans, exclusions, complaints, and corrective actions tied to each training run.
Publishing a vague training summary
Broad labels such as “internet data” do not explain categories, collection periods, filtering, licensing, or version coverage. Use the Commission template and reconcile the public summary to the internal inventory before approval.
Assuming the compute threshold settles systemic risk forever
The 1025-FLOP presumption is under review, and model capabilities and deployment context can change. Recalculate after major training or scaling changes and retain the assumptions behind every result.
Signing the Code without closing evidence gaps
Voluntary status does not make commitments optional after signing. Perform a control-by-control readiness review and assign owners before representing the Code as your compliance route.
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Waiting until enforcement begins
Enforcement starts on 2 August 2026, but documentation, data controls, and evaluation programs take time to build. Use each release gate to create evidence rather than attempting a retrospective reconstruction.
How can a provider run a final readiness review?
Run a cross-functional review involving legal, data governance, engineering, security, safety, product, and executive ownership. Sample a released model version and ask reviewers to retrieve every required record without relying on the original project team.
- Confirm the model and provider classification, compute calculation, EU representative status, and transition date.
- Trace one training-data source from intake through copyright screening, model release, public summary, and any complaint handling.
- Trace one evaluation finding to its test protocol, risk decision, mitigation, deployment control, and monitoring result.
- Ask a downstream-provider representative to use the supplied documentation to identify limitations and safe integration conditions.
- Test an incident escalation, security response, document retrieval, and version rollback using a recorded exercise.
- Close gaps with named owners and deadlines, then obtain executive approval of residual risk and the release decision.
A provider is ready when it can explain its classification, show the required documents for the exact model version, demonstrate operating controls, and respond consistently to a regulator and to downstream users.
Frequently Asked Questions
Do small companies have to publish a training-data summary?
The GPAI duties described here apply based on the model and provider’s role, not simply on company headcount. A small provider should use the same version-linked inventory and approval process, scaling the governance team rather than omitting required content.
Can a downstream company rely on the model provider’s compliance documents?
It can use the provider’s information as an input to its own AI-system obligations, but it remains responsible for assessing its integrated system and intended use. Request the exact model version, limitations, evaluation context, update policy, and incident contact before deployment.
What happens if the systemic-risk threshold changes?
A changed threshold can alter classification for an existing model. Keep the compute method and assumptions reproducible, subscribe to legal-change ownership internally, and trigger a reassessment whenever the Commission updates its guidance.
Is the public training summary the same as disclosing the full dataset?
No. The requirement is a sufficiently detailed public summary, not publication of every record or protected item in the training corpus. The internal inventory should be more granular and should support the public description while protecting personal, confidential, and security-sensitive information.
How often should GPAI documentation be updated?
Update it whenever a new model version, major fine-tune, data refresh, capability change, deployment channel, evaluation result, incident, or legal development could affect the documented obligations. Set a scheduled review as a backstop, but do not wait for that date after a material change.
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