Companies using generative AI should begin with a documented inventory of every model, application and workflow, then determine their legal role, screen the intended use for prohibited and high-risk practices, and build evidence that transparency, oversight, training and incident controls work. The EU AI Act applies in stages, so a deployment that appears low risk today may still need new notices or content-marking controls as later obligations apply.
The practical test is not whether a tool is called ChatGPT, an assistant, an API or an internal model. It is how the system is supplied, where it is used, what it is intended to do, who controls it and whether its outputs affect people in the EU. This checklist is an operational guide; the exact duty depends on the company’s role, purpose, risk classification, sector and Member State arrangements.
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What a company must establish first
Regulation (EU) 2024/1689, the EU Artificial Intelligence Act, entered into force on 1 August 2024 and applies progressively to providers, deployers and other supply-chain actors. A company cannot decide its obligations from the model name alone. It needs a defensible record showing:
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- which generative-AI systems, model APIs, open-weight models, fine-tuned models and internal tools it operates;
- whether it is acting as a provider, deployer, importer, distributor or downstream provider for each system;
- where the system is placed on the market, where users are located and where outputs are used;
- what the intended purpose is, including important features, users, sectors and affected people;
- whether the use is prohibited, high-risk, covered by general-purpose-AI rules or subject to Article 50 transparency requirements; and
- what evidence proves that controls, training, monitoring and escalation are functioning.
Keep the classification decision and its reasoning. A short memo signed by an accountable reviewer is more useful than an unannotated spreadsheet because it explains why a use was included or excluded and identifies the facts that would require a reassessment.
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Key EU AI Act dates for generative-AI programs
The timetable below separates an obligation’s application date from later supervisory or enforcement milestones. Dates can be affected by transitional provisions and future guidance, so maintain a regulatory calendar rather than treating this as a one-time project.
| Date | What changes | Operational implication |
|---|---|---|
| 1 August 2024 | Regulation (EU) 2024/1689 entered into force. | Start governance, inventory and role-classification work; later duties apply according to the regulation’s staged timetable. |
| 2 August 2025 | General-purpose-AI (GPAI) provider obligations began to apply. | A company supplying a GPAI model must map documentation, downstream-information, copyright and, where relevant, systemic-risk controls. |
| 2 August 2026 | Major AI Office and Member State enforcement powers begin; Article 50 transparency rules apply. | Covered providers and deployers need operational notices, synthetic-content marking or detectability, and evidence that the controls work. |
| 2 December 2026 | Limited transition for Article 50(2) marking and detection for certain systems placed on the market before 2 August 2026. | Check whether a pre-existing system qualifies and document the transition approach rather than assuming a blanket delay. |
The European Commission describes the Act as entering into force on 1 August 2024. The AI Act Service Desk notes that some AI Office and Member State enforcement powers start on 2 August 2026, while Commission transparency guidance states that Article 50 applies from that date.
Step 1: Build a complete system and use-case inventory
Inventory work should include sanctioned products and unofficial use. Ask procurement, security, legal, data-protection, engineering and business teams to identify every route by which staff or customers can submit prompts or receive generated content.
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- Product or workflow name, business owner and technical owner.
- Underlying model, provider, version or endpoint, hosting location and whether weights are open, modified or fine-tuned.
- Input and output data types, retention settings, connected tools, plug-ins and human review points.
- Intended users, affected people, business purpose, sector and decisions supported or automated.
- Deployment status: pilot, internal production, customer-facing, embedded in another product or withdrawn.
Record geography and supply-chain roles
For each entry, record where the system is placed on the market, where the company is established, where users are located and where outputs are used. Then assign the role or roles that apply to that transaction. A company may be a deployer of a hosted assistant, a downstream provider when it integrates or substantially modifies a model for another product, and a provider for a model it places on the market. Importer and distributor duties can arise in a supply chain even when the company did not train the model.
Using a hosted generative-AI service generally points toward a deployer relationship, but the label is not conclusive. Contract terms, product changes, branding, control of intended purpose and who makes the system available determine the legal analysis. Escalate ambiguous cases instead of forcing every tool into a single permanent category.
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Step 2: Screen prohibited and high-risk intended purposes
Before approving a use, compare its intended purpose with the Act’s prohibited-practice rules and high-risk categories. The assessment should cover the real workflow, not just the marketing description. A general writing assistant can become a materially different system when connected to recruiting, access decisions, customer eligibility, education, healthcare or public services.
Use a documented screening record
- Describe the task in plain language, including what the model sees, generates and recommends.
- Identify whether a person, group or legal interest could be affected by the output.
- Check the prohibited-practice list and record the specific conclusion, reviewer and date.
- Check high-risk categories and any sector-specific obligations; explain why each category is in or out.
- List safeguards, human decision authority, testing requirements and residual risks.
- Set a reassessment trigger for a new model, prompt workflow, connected data source, user group or material change in impact.
Do not treat a “human in the loop” label as a complete safeguard. Document what the human sees, how much time is available, whether the person can override the output, what training they receive and how overrides are recorded. If the reviewer merely accepts a score or summary without meaningful ability to challenge it, the control may not provide the oversight the deployment requires.
Step 3: Determine whether GPAI-provider obligations apply
GPAI rules are directed at companies that provide a general-purpose AI model, including a model that can perform a broad range of tasks and is supplied for integration or downstream use. Merely calling an external model through an interface does not automatically make a customer its GPAI provider. The legal role must be assessed from what the company supplies, modifies and places on the market.
Core GPAI evidence
- Technical documentation: maintain the model information required by the Act and keep it current through significant changes.
- Downstream information: provide information that enables downstream providers to understand capabilities, limitations, intended uses and relevant integration conditions.
- Copyright policy: establish a policy for complying with EU copyright law and a process addressing rights reservations expressed for training data.
- Systemic-risk controls: if the model presents systemic risk, document evaluations, identification of risks, mitigation measures, testing and follow-up.
- Authority interface: prepare records and contacts for cooperation with the EU AI Office and other competent authorities.
The voluntary GPAI Code of Practice can help a provider organize evidence and demonstrate a compliance approach, but it does not replace the regulation or answer the threshold question of which legal role applies. Verify the company’s status and obligations for each model and distribution path.
Step 4: Implement Article 50 transparency controls
From 2 August 2026, Article 50 transparency requirements apply to covered AI systems. The control design should be specific to the interaction and content type, because the relevant paragraph and exceptions differ by use.
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Tell people when they are interacting with AI
Design a clear, timely notice for an AI system that interacts directly with people where the relevant Article 50 rule applies. Place the notice at the point of interaction, not solely in a distant privacy or terms page. Record the wording, display location, language versions, owner and review date.
Mark or make synthetic content detectable
Where required, use appropriate technical means to mark or otherwise make generated audio, image, video and text detectable. Store the method, implementation owner, test results, failure conditions and any exception relied upon. Test the control after model, rendering, compression, export or platform changes; a mark that disappears when a file is edited is not evidence of a reliable process.
Handle deepfakes and public-interest content
Where the applicable Article 50 paragraph covers deepfake material or AI-generated text published to inform the public on matters of public interest, provide the required disclosure or label in a way users can see and understand. Route borderline cases to legal or compliance review, especially where an editorial, artistic, satirical or similar exception may be relevant. Record why an exception was used and who approved it.
Manage the pre-existing-system transition
For a system placed on the market before 2 August 2026, check whether it falls within the limited 2 December 2026 transition for Article 50(2) marking or detection. The transition is not a general exemption from transparency duties. Keep the market-placement date, system version, eligibility analysis and implementation plan in the register.
Step 5: Build the evidence pack
A regulator, customer or internal auditor should be able to follow one record from purpose through deployment and monitoring. At minimum, keep the following linked artifacts:
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| Control area | Evidence to retain | Suggested accountable owner |
|---|---|---|
| System register | Model, version, provider, use case, geography, role and status | AI governance lead |
| Risk classification | Prohibited/high-risk screening, rationale, reviewer and reassessment triggers | Legal and compliance |
| GPAI supply | Technical documentation, downstream information, copyright policy and systemic-risk records where applicable | Model or product owner |
| Transparency | Notices, labels, marking method, detectability tests, exceptions and approvals | Product and engineering |
| Human oversight | Operating procedure, reviewer training, override records and quality checks | Business process owner |
| Incidents | Reports, triage, escalation, corrective action, closure and lessons learned | Security or risk operations |
| AI literacy | Training content, audience, completion or attendance and refresh records | People or learning team |
| Change management | Release approvals, model changes, prompt or data changes and reclassification decisions | Technology change owner |
Use immutable or access-controlled logs where feasible, define retention periods with legal and data-protection teams, and ensure the evidence can be exported in a readable format. A register that cannot show who approved a change or how an incident was resolved leaves a material gap.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 6: Put AI literacy and human oversight into operations
Train staff who select, configure, supervise or rely on generative-AI systems. Training should cover the approved purpose, known limitations, privacy and security handling, prohibited uses, verification of outputs, escalation routes and the meaning of notices or content marks. Keep completion or attendance evidence and refresh it when the system or risk changes.
For workflows requiring human oversight, specify the reviewer’s authority and information. Include sampling or quality monitoring, a route to challenge outputs, controls against automation bias and a process for suspending the system when performance or safety deteriorates. Monitoring should produce records, not just a policy statement.
Step 7: Align procurement and vendor contracts
Before purchasing or renewing a generative-AI service, make the vendor’s role and responsibilities explicit. The contract and technical schedule should address:
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- intended-use restrictions and information needed for downstream classification;
- incident and vulnerability notification times;
- audit access, evidence export and cooperation with authorities;
- data retention, training-use settings, security and subcontractors;
- responsibility for Article 50 notices, content marking and detectability;
- support for changes, version retirement and reassessment; and
- allocation of copyright, complaint-handling and corrective-action responsibilities.
Do not assume a vendor’s compliance claim transfers all duties to the customer. The deployer still needs its own use-case assessment, user controls, training, monitoring and records.
Step 8: Prepare for supervision and incidents
Identify the competent national market-surveillance authority for each deployment and establish the company’s interface with the EU AI Office for GPAI matters. Keep contact details, escalation owners and an evidence index in the system register.
Use an incident workflow
- Capture the prompt, model or system version, output, affected workflow, user and time, subject to applicable data-protection limits.
- Triage severity, reach, potential harm and whether the event indicates a prohibited or misclassified use.
- Contain the issue by disabling a feature, changing access, adding review or suspending the system.
- Notify internal owners and, where required, the relevant provider or authority under the applicable process.
- Record root cause, corrective action, validation and the decision to resume, restrict or retire the system.
Complaints, near misses and repeated low-severity failures belong in the same governance picture as major incidents. They can reveal that a purpose statement, notice, reviewer instruction or vendor control is no longer accurate.
What changes for companies outside the EU?
Being incorporated outside the EU does not by itself remove the Act from consideration. A non-EU company may be in scope when it places an AI system or model on the EU market, supplies it for use in the EU, deploys it for users in the EU, or produces outputs used in the EU, depending on the role and provision involved. The exact territorial trigger must be assessed against the transaction and intended purpose.
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Common implementation failures and fixes
- Only approved software is inventoried: include browser tools, personal accounts, code libraries, internal prototypes and embedded AI features discovered through expense, identity and network reviews.
- The model is classified instead of the use: classify each intended purpose and workflow, then reassess when the purpose changes.
- “Human review” is left undefined: document the reviewer’s authority, information, time, training and override record.
- Transparency is treated as a one-time banner: test notices and content marks after updates, export, translation and integration changes.
- Vendor documentation is accepted without verification: map each supplier statement to a company-owned control and evidence item.
- Training is delivered but not evidenced: retain audience, content, completion and refresh records.
- Transition dates are assumed to be exemptions: record the system’s market-placement date and the precise provision for which a transition is claimed.
- Registers become stale: connect model releases, procurement changes, incidents and complaints to mandatory reassessment.
A practical approval gate
Do not release a generative-AI workflow until the owner can answer these questions in the register:
- What is the intended purpose, and who may be affected?
- Which legal role does the company hold at each point in the supply chain?
- Where is the system placed on the market, deployed and used?
- What is the documented conclusion on prohibited and high-risk practices?
- Are GPAI-provider duties triggered by what the company supplies or modifies?
- Which Article 50 notice, label or marking control applies, and how was it tested?
- Who provides human oversight, what can they override and how is performance monitored?
- Which staff require AI-literacy training, and where is completion recorded?
- How will incidents, complaints, changes and authority requests be handled?
- Can the company export a coherent evidence pack showing decisions, controls and corrective actions?
Use the answers as a release decision, not as a formality. Reopen the gate whenever the model, data, user group, geography, connected tool or intended purpose changes.
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