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High-risk AI, foundation models, and generative AI are not three levels on one scale. Under the EU AI Act, “high-risk” is a legal classification based on an AI system’s intended purpose and context. “Foundation model” is mainly technical language for a broadly reusable model; the Act’s operative term is general-purpose AI model (GPAI). “Generative AI” describes what a system does—creates text, images, audio, video, or code—and does not automatically make it high-risk.
The same broad model can power applications with different legal classifications. To classify a product, start with its use and deployment context, then check the Act’s prohibited-practice and high-risk routes, GPAI obligations, transparency triggers, responsible parties, and the date each requirement applies.
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What do “high-risk,” “foundation,” and “generative” mean under the EU AI Act?
These labels describe different dimensions of an AI product. Confusing them can lead to the wrong compliance plan.
| Category | What is being described? | What triggers it? | Main obligations or effect |
|---|---|---|---|
| High-risk AI | An AI system used in a legally specified context | Intended purpose, deployment context, a listed high-risk area, or the regulated-product route | Detailed safety, documentation, oversight, quality, security, assessment, registration, and monitoring duties |
| Foundation model | Technical description of a broad model reusable for many tasks | Broad capability and reuse, not a legal risk tier by itself | Usually discussed through the Act’s GPAI category |
| General-purpose AI model (GPAI) | The Act’s legal model-level category | A model supplied for general-purpose use; Commission guidance gives a 1023 FLOP criterion for language-capable models | Technical documentation, copyright policy, and a public summary of training content; extra controls for systemic-risk models |
| Generative AI | A capability that produces content | Generation of text, images, audio, video, or code | Specified transparency and disclosure duties; risk classification still depends on use |
A model can therefore be a GPAI model and generative at the same time, while an application built on it may or may not be high-risk. The model-level and system-level duties must be analyzed separately.
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What does high-risk AI mean under the EU AI Act?
High-risk is a legal classification tied to an AI system’s intended purpose and the circumstances in which it is deployed. Size, sophistication, or the fact that a system generates content does not, by itself, make it high-risk.
How a system enters the high-risk category
The practical test has two routes. First, determine whether the intended use falls within one of the Act’s listed high-risk areas. Second, check whether the AI is a safety component of, or is itself, a product covered by the Act’s regulated-product route. If neither route applies, the system is not classified as high-risk merely because it uses a powerful model.
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The European Commission describes a broad set of provider requirements. A compliant program needs to address:
- Risk assessment and mitigation throughout the system’s operation.
- High-quality, appropriately governed data.
- Automatic activity logging so relevant events can be reconstructed.
- Technical documentation that explains the system and its controls.
- Clear information and instructions for deployers.
- Human-oversight measures that allow people to understand, monitor, intervene in, or stop the system when appropriate.
- Accuracy, robustness, and cybersecurity controls.
- A conformity-assessment process before the system is placed on the market or put into service, where required.
- A quality-management system covering the provider’s compliance processes.
- Public registration where the Act requires it.
- Post-market monitoring and procedures for handling problems after deployment.
These are not interchangeable checkboxes. For example, logs support monitoring and incident investigation, while human oversight addresses how people supervise decisions in operation. The technical file, data controls, security testing, and post-market feedback should fit the system’s actual intended use.
What deployers need to do
High-risk duties do not stop with the company that built the system. Deployers receive information and instructions from the provider and must operate the system within that documented purpose, preserve appropriate human oversight, and support monitoring and incident handling. A company integrating a third-party model into its own workflow should document what it changed, where the system is used, who supervises it, and how performance problems are escalated.
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Is a foundation model the same as a GPAI model?
Not exactly. “Foundation model” is common technical language for a broad model trained for reuse across many tasks. The EU AI Act uses the legal term general-purpose AI model, or GPAI. The Commission describes GPAI models as “the foundation for many different AI systems.”
What GPAI providers must prepare
Every GPAI provider must draw up technical documentation, implement a copyright policy, and publish a summary of the content used for training. These obligations attach to the model provider, rather than automatically to every downstream application developer.
The Commission’s 2025 guidance identifies 1023 floating-point operations (FLOP) as the criterion for language-capable models considered GPAI. That figure is a guidance threshold and should be checked against the current Commission interpretation when a model is close to the boundary.
When does GPAI become systemic risk?
Systemic-risk GPAI models face additional obligations: risk assessment and mitigation, incident reporting, and cybersecurity protections. The Commission gives 1025 FLOP as the presumption threshold for systemic risk and says that threshold is under review. A provider should therefore record the calculation, the model version, and the guidance in force when making its assessment instead of treating the number as permanently fixed.
What is generative AI under the Act?
Generative AI produces content such as text, images, audio, video, or code. “Generative” describes capability, not a standalone legal risk tier. A generative model can be GPAI, and a generative application can be high-risk if its intended use enters a high-risk route, but many generative uses will not meet that classification.
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What transparency rules apply to generated content?
The Commission says AI-generated content must be identifiable. Covered chatbots must tell people when they are interacting with a machine. Deepfakes and certain text published on matters of public interest require visible labelling under the transparency rules. The exact disclosure step depends on the system and content, so teams should map each user-facing output rather than rely on a single “AI-generated” notice everywhere.
Transparency is different from high-risk controls. A chatbot may have to disclose that it is a machine even when it is not a high-risk system; conversely, a high-risk system may need extensive risk management in addition to any applicable disclosure.
Can one model be GPAI, generative, and connected to a high-risk system?
Yes. The categories can overlap because they describe different objects and questions.
- Model layer: Is the supplied model general-purpose, and does it meet the applicable systemic-risk criteria?
- Application layer: What does the integrated system do, for whom, and in what deployment context?
- Output layer: Does it generate content that triggers identification or labelling duties?
A single broad model can support several downstream applications, each with a different risk classification. Changing the prompt, adding retrieval, restricting users, or embedding the model in a product can change the system’s intended purpose and therefore the analysis, even though the underlying model is unchanged.
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When do the EU AI Act rules take effect?
The Act uses a phased timetable. The dates below are the current amended timeline described by the European Commission; amendments and transitional rules can change practical deadlines, so verify the official timeline for the system’s route before relying on a date.
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| Date | Rules applying | Who should pay attention |
|---|---|---|
| 2 February 2025 | Prohibitions, definitions, and AI-literacy provisions | All organizations developing, supplying, deploying, or using AI in scope |
| 2 August 2025 | GPAI obligations and governance provisions | Providers of general-purpose AI models and organizations responsible for governance |
| 2 August 2026 | Most remaining rules, transparency obligations, and applicable enforcement | Providers and deployers preparing system-level controls and disclosures |
| 2 December 2027 | Annex III stand-alone high-risk rules under the current amended timeline | Providers and deployers of systems covered by those stand-alone routes |
| 2 August 2028 | High-risk AI embedded in regulated products | Organizations placing those products on the market or putting them into service |
A date alone does not determine a company’s deadline. First identify whether the system is a stand-alone high-risk system, high-risk AI embedded in a regulated product, a GPAI model, or a system subject to transparency rules. Then apply the transitional provisions and the version of the Commission timeline that is current for that route.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I classify an AI system for compliance?
Use a documented, repeatable workflow. The goal is to classify the model and the deployed system separately, then assign duties to the right party.
- Describe the intended purpose. Record what the system does, the users, the affected people, the deployment environment, the human decisions it supports, and any limits on use. Avoid a vague description such as “general assistant”; state the actual function supplied to users or another system.
- Check prohibited practices first. Determine whether the intended behavior falls within a prohibited practice. If it does, the analysis is not solved by calling the model a foundation model or by adding a disclosure.
- Check both high-risk routes. Compare the purpose and context with the Act’s listed high-risk areas, then examine whether the AI is part of a regulated product or a safety component under the product route.
- Identify the model category. If your organization supplies a broadly reusable model, assess whether it is a GPAI model under the applicable Commission guidance. For language-capable models, record the 1023 FLOP criterion and the evidence used. If the model is at or above the 1025 FLOP systemic-risk presumption, plan for the additional controls while checking the latest guidance.
- Map the responsible party. Separate provider tasks from deployer tasks. The provider may own technical documentation, training-content summaries, risk controls, and instructions; the deployer must operate the system as documented, maintain effective oversight, and feed monitoring information back into the process.
- Map transparency triggers. Check whether users must be told they are interacting with a machine and whether generated outputs are deepfakes or covered public-interest text requiring visible labelling. Document where the notice appears and how it remains attached to the content when shared.
- Build the evidence file. Keep the intended-purpose statement, route analysis, data and risk records, logs, technical documentation, oversight design, conformity-assessment material where required, security controls, and monitoring plan together. Version the file whenever the model, deployment, users, or purpose changes.
- Confirm the date and transition rule. Match the classification to the current application date and any transitional provision. Recheck the Commission timeline before launch and after material amendments.
What are the most common classification mistakes?
Treating model size as the risk test
A large or generative model is not automatically high-risk. Size and compute may matter to GPAI or systemic-risk analysis, but high-risk status still turns on the system’s intended use and context.
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The technical label does not replace the Act’s GPAI analysis. Use the legal category when assigning model-level duties, and retain the technical description as supporting context.
Classifying only the base model
Downstream integration can create a high-risk system even when the base model was supplied for general use. Review the complete deployed workflow, including interfaces, users, human decisions, and regulated-product connections.
Adding a notice instead of managing risk
A disclosure can satisfy a transparency trigger but does not substitute for risk assessment, human oversight, documentation, security, or monitoring where those duties apply.
Relying on an old deadline
The Act’s schedule is phased and subject to amendments and transitional rules. Keep the source date and applicable route beside every implementation milestone.
What should an AI governance owner review each quarter?
A short recurring review catches classification drift before it becomes a launch or enforcement problem. Confirm that the intended purpose, user population, deployment context, model version, compute assessment, training-content summary, transparency notices, human-oversight design, security controls, logs, incidents, and post-market findings still match the documented system. Re-run the prohibited-practice and high-risk-route checks whenever a new feature, customer segment, regulated product, or autonomous action is added.
Frequently Asked Questions
Can the same company have both GPAI and high-risk responsibilities?
Yes. A company may supply a broadly reusable model while also deploying an AI system built with that model. It should maintain separate model-level records for GPAI duties and system-level records for any high-risk use, with clear ownership for each obligation.
Does a fine-tuned model stop being a GPAI model?
Fine-tuning does not answer the legal question by itself. Reassess what the organization supplies, how broadly it can be reused, the applicable Commission guidance, and whether the downstream system’s intended purpose creates a separate high-risk classification.
What should be visible to a person using an AI chatbot?
Where the transparency rule applies, the person must be told that the interaction is with a machine. The notice should be presented at the point of interaction, not hidden only in general terms or a back-end policy.
Why do the systemic-risk FLOP thresholds need monitoring?
The European Commission identifies 1025 FLOP as a presumption threshold for systemic risk and says it is under review. Providers near that level should preserve their calculation and revisit the assessment when guidance changes.
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