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Short answer: A company deploying generative AI should first inventory every model and use case, identify its legal role, and document whether the purpose is prohibited, high-risk, general-purpose AI (GPAI), or outside those categories. It then needs proportionate controls for transparency, human oversight, staff AI literacy, monitoring, incidents, procurement and supervisory evidence.
Regulation (EU) 2024/1689 entered into force on 1 August 2024, but obligations start on different dates. GPAI-provider obligations apply from 2 August 2025. The main AI Office enforcement powers and Article 50 transparency rules apply from 2 August 2026, with a limited 2 December 2026 transition for marking or detection under Article 50(2) for certain systems already placed on the market. Treat this checklist as an implementation plan, not a substitute for advice on your role, sector and Member State.
What the EU AI Act means for a company using generative AI
The Act regulates the role a company performs and the purpose of an AI system, not merely the fact that an employee opened a chatbot. The same model can create different obligations when it is used for internal drafting, embedded in a customer product, fine-tuned for a business service or supplied to other organizations.
For each system, record where it is placed on the market, where users are located and where outputs are used. A headquarters outside the EU is not, by itself, a reliable reason to dismiss the Act. Those geographic facts, together with the company’s role and intended purpose, determine which provisions need legal review.
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Identify the role before choosing controls
| Role to assess | Typical activity | Evidence to retain |
|---|---|---|
| Deployer | Uses an AI system under its authority, such as an employee assistant, customer support bot or content-generation workflow. | Purpose statement, operating instructions, human-oversight design, notices, monitoring and incident records. |
| Provider | Develops an AI system or GPAI model and places it on the market or puts it into service under its name. | Technical documentation, instructions, conformity and risk records, downstream information and required policies. |
| Downstream provider | Integrates an existing model into a product or materially changes it for a new intended purpose. | Integration design, changed-purpose analysis, supplier information, testing and updated user documentation. |
| Importer or distributor | Places a system from another jurisdiction on the EU market or makes it available through a distribution chain. | Supplier identity, market-placement checks, documentation and corrective-action trail. |
Do not assume that buying access to an API always makes the buyer only a deployer. Fine-tuning, branding, embedding or changing the intended purpose can create downstream-provider responsibilities. Have a reviewer sign the role decision and record the facts that support it.
Key dates and transition points
| Date | What it means | Planning implication |
|---|---|---|
| 1 August 2024 | Regulation (EU) 2024/1689 entered into force. | Start a maintained inventory and governance file rather than waiting for an enforcement deadline. |
| 2 August 2025 | GPAI-provider obligations began to apply. | A company supplying a general-purpose model should already have documentation, copyright-policy processes and downstream information in place. |
| 2 August 2026 | Major AI Office and Member State enforcement powers begin to apply, and Article 50 transparency rules apply. | Deployments need operational notices, synthetic-content marking or detection, interaction disclosures and evidence before this date. |
| 2 December 2026 | Limited transition for marking or detection under Article 50(2) for certain systems placed on the market before 2 August 2026. | Check whether a pre-existing system qualifies; do not treat this date as a general extension for every Article 50 duty. |
Commission guidance, the AI Act Service Desk and national implementation updates can change how these dates are applied in practice. Assign an owner to monitor updates and record the version and date of each internal decision.
Step 1: Build a complete AI-system and use-case inventory
Start with discovery, not paperwork. Ask procurement, security, engineering, marketing, HR, legal, customer support and every business unit to list tools that generate, transform, classify or summarize content.
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- Capture the model or system name and version, supplier, account owner, deployment environment, data categories, connected systems and business process.
- Write the intended purpose in one sentence that states who uses the system, what it produces and what decision or action follows.
- Record whether outputs are shown to the public, used by staff only, sent to customers, used to rank or select people, or fed into another automated process.
- Record market-placement country, user location, output-use location and the legal entity responsible for operation.
Give each entry a unique identifier and change history. A spreadsheet can be a starting point, but an uncontrolled list becomes unreliable when models, prompts, vendors or purposes change.
Step 2: Screen prohibited and high-risk purposes
For every inventory entry, document a two-stage decision: whether the intended purpose falls within a prohibited practice, and whether it is a high-risk use requiring the additional controls applicable to that category. The reviewer should cite the facts considered, the legal provisions checked, the date and the approving function.
Use a repeatable screening record
- Describe the users and affected people. Include employees, customers, applicants, students, patients, citizens or other groups exposed to the output.
- Describe the decision path. State whether the output is advice, a draft for human review, a recommendation, a ranking, an access decision or an automated action.
- Check the intended purpose against Article 5 prohibitions and the applicable high-risk categories. Do not rely on a vendor’s marketing label; assess your configured use.
- Record safeguards and residual risk. Note human review, override authority, quality tests, data controls, escalation routes and known limitations.
- Obtain sign-off and set a review trigger. Reassess when the model, data, users, geography or business purpose changes.
A general-purpose model may be low risk for one drafting workflow and become high risk when connected to a consequential business process. Preserve the original screening and every later revision so an auditor can see why the classification changed.
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Step 3: Determine whether GPAI obligations attach to your company
Most organizations that simply use a hosted model will be deployers. A company that supplies a general-purpose model, places one on the market under its name, or materially integrates and re-purposes one may have provider or downstream-provider duties.
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If you provide a GPAI model
- Maintain the technical documentation and information required for downstream providers and authorities.
- Adopt a copyright-compliance policy and a process for honoring rights reservations in training data.
- Document model evaluations, systemic-risk identification and mitigation where the model has systemic risk.
- Prepare an evidence trail for interaction with the European AI Office, including responsible owners and response procedures.
These provider obligations apply from 2 August 2025. Full AI Office enforcement powers begin on 2 August 2026, so unresolved documentation is not a reason to delay remediation.
If you integrate or fine-tune someone else’s model
Obtain supplier documentation, usage restrictions, model limitations, incident contacts, update notices and information about content-marking capabilities. Record what you changed: weights, prompts, retrieval data, safety filters, output routing, user interface and intended purpose. Have legal and technical reviewers decide whether the change creates downstream-provider responsibilities.
Step 4: Implement Article 50 transparency controls
Article 50 applies from 2 August 2026. Design controls around the actual interaction and content flow rather than adding a generic disclaimer to a privacy page.
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Where a person interacts directly with an AI system covered by the relevant Article 50 paragraph, provide a clear, timely notice. Place it at the point of interaction, keep it understandable to the intended audience and record the wording, owner, release date and languages used.
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For covered synthetic audio, image, video and text, use appropriate technical means to mark or otherwise make the content detectable. Document the method, metadata or watermark standard, where it is applied in the pipeline, how it survives export or editing, and the tests showing that it works.
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Label deepfakes and publicly informing content when required
When the relevant Article 50 paragraph applies to a deepfake or AI-generated content intended to inform the public, place the disclosure where viewers will see it before relying on the content. Keep an exception record when a legal exception or technical limitation is invoked.
Check the 2 December 2026 transition only for qualifying systems placed on the market before 2 August 2026 and only for the marking or detection obligation described in Article 50(2). Continue preparing other applicable notices and controls on the main 2 August 2026 timetable.
Step 5: Create the governance evidence pack
Compliance is demonstrated through contemporaneous evidence. Keep one controlled repository linked to the system identifier.
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- System file: provider, model version, architecture or service description, intended purpose, users, data flows and dependencies.
- Role and risk file: provider/deployer/downstream decision, prohibited and high-risk screening, reviewer, date and change triggers.
- Human-oversight file: who can review, override, pause or shut down the workflow; response times; and segregation of duties.
- Quality and monitoring file: test plans, error samples, drift indicators, acceptance thresholds and review cadence.
- Transparency file: notice text, placement screenshots or interface specifications, marking method, test results and exceptions.
- Incident file: reports, severity, affected users, containment, authority notifications where required, root cause and corrective action.
- Training file: AI-literacy curriculum, affected roles, completion dates, attendance or assessment evidence and refresher schedule.
- Change file: model updates, prompt or policy changes, new data sources, new markets and the resulting reclassification decision.
Limit access to personal and confidential data, but retain enough context to reproduce the decision. Set retention periods with legal, privacy and sector requirements rather than deleting evidence immediately after a ticket closes.
Step 6: Train people and define operating procedures
AI literacy is a control, not a one-time presentation. Train people who select tools, configure prompts, review outputs, approve publication, handle incidents or make decisions based on AI assistance.
- Explain the system’s intended purpose, known limitations, prohibited uses and escalation route.
- Teach staff how to recognize fabricated facts, unsafe recommendations, personal-data leakage and prompt-injection attempts.
- Require human review for outputs that can affect a person’s rights, access, safety, employment or reputation.
- Show how to give a user the required notice and how to preserve or verify content markings.
- Record completion and test understanding with a practical exercise relevant to the employee’s role.
Publish a simple operating rule: stop using the system, preserve the input and output, notify the named owner and do not conceal an error. That rule makes incident records more complete and reduces improvised responses.
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Step 7: Align procurement and vendor contracts
Before buying or renewing a generative-AI service, require information that supports your classification and evidence files. Contract terms should address:
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- technical documentation, usage instructions, limitations and update notices;
- incident notification deadlines, cooperation with investigations and evidence preservation;
- audit or assurance access proportionate to the risk;
- data-use, retention, confidentiality and deletion controls;
- responsibility for Article 50 marking, detection, notices and failures after export;
- subprocessors, geographic processing locations and support for authority requests; and
- termination, model rollback and safe migration if the service changes materially.
Do not accept a contract that says only “the service is AI Act compliant.” Compliance depends on your configured purpose and role, so the agreement must allocate the information and cooperation needed to prove your own controls.
Step 8: Prepare for supervision and cross-border questions
Identify the competent national market-surveillance authority for each deployment and the European AI Office interface for GPAI matters. Keep a contact map showing the responsible legal entity, deployment country, authority, internal owner and escalation backup.
Maintain an exportable evidence pack containing the inventory record, role decision, risk screening, notices, marking tests, training records, monitoring results, incidents, complaints and corrective actions. Test an export before an authority asks for it; missing timestamps, owners or version links are common reasons an otherwise sound control cannot be demonstrated.
For a company outside the EU, document the same geography questions instead of making a headquarters-based assumption. If users are in the EU, the system is placed on the EU market, or outputs are used in the EU, obtain a role-and-scope analysis for the specific facts. Member State implementation and guidance can affect the practical contact point.
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No single software category is required. Select an approach that can produce the evidence your risk profile demands.
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| Approach | Role and risk coverage | Transparency and AI-literacy records | Audit trail, authority mapping and effort |
|---|---|---|---|
| Controlled spreadsheet plus document repository | Can cover inventory and screening if every field, owner and review date is mandatory; weak against duplicate or stale entries. | Stores notices, marking tests and training files, but relies on manual linking. | Low setup cost; high ongoing evidence burden and manual export. |
| Workflow or GRC platform | Supports approval gates, prohibited/high-risk questionnaires, change triggers and reassessment workflows. | Can assign training, version notices and retain completion evidence. | Stronger audit trail and authority mapping; requires configuration, access governance and maintenance. |
| Vendor-integrated controls | May expose model metadata, usage logs and content-marking functions, but does not replace your purpose or role decision. | Useful for automated notices and marking tests when supported by the service. | Lower manual effort for connected systems; creates dependency on vendor exports, update notices and contract rights. |
Whichever approach you choose, test ten representative systems from discovery through evidence export. If a reviewer cannot reconstruct the classification, notice, training and incident history from the record, the process is not ready for scale.
Common failure modes and fixes
“The vendor says it is compliant, so we did nothing.”
Fix: Keep the vendor statement as an input, then complete your own role, purpose, geography and risk assessment. Add missing documentation and incident obligations to the contract.
“We cannot decide whether we are a deployer or provider.”
Fix: Draw the data and control flow, list every change made to the supplied model and identify whose name and intended purpose appear when the system reaches users. Have legal and technical owners sign one documented decision.
“Our disclosure is buried in terms and conditions.”
Fix: Move the notice to the interaction or content surface, make it timely and readable, version it and test it with real users.
“Marking disappears when content is edited or exported.”
Fix: Test the complete publishing pipeline, including compression, cropping, format conversion and third-party platforms. Record failure conditions, add a fallback disclosure and assign an owner for retesting after model or workflow changes.
“A model update changed the risk profile.”
Fix: Treat provider updates, fine-tuning, new data sources, new users and new countries as review triggers. Freeze the previous evidence, reassess the purpose and approve the release before production use.
Operational checklist
- Assign an accountable executive and a technical owner for every AI-system record.
- Inventory models, APIs, open-weight deployments, fine-tunes, agents and embedded features.
- Record provider, deployer, downstream-provider, importer and distributor roles.
- Document market placement, user location and output-use geography.
- Screen Article 5 prohibitions and high-risk categories; obtain dated sign-off.
- Map GPAI duties if your company supplies or materially integrates a general-purpose model.
- Implement Article 50 notices, content marking or detection, and exception handling before 2 August 2026.
- Check the narrow 2 December 2026 transition for qualifying pre-existing systems.
- Maintain system, risk, human-oversight, monitoring, incident, complaint and change records.
- Train affected staff and retain AI-literacy completion evidence.
- Update vendor contracts for documentation, incidents, audit access, data handling and marking responsibilities.
- Map national authorities and the European AI Office interface, then test an evidence export.
The practical test is simple: for any generative-AI workflow, can you show what it is, who is responsible, why its use is permitted, what users are told, how people supervise it and what happened when something went wrong? If not, stop expanding that workflow until the missing evidence and control owner are assigned.
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