Foundation models are treated differently because they are general-purpose, adaptable, and can be reused across many downstream contexts. A use-case only approach misses that flexibility and can leave gaps between model design and real-world deployment. The article argues that regulation must follow the model’s breadth, not just the immediate application, to address safety, transparency, and governance risks consistently.
Why the EU AI Act uses a model-level lens for foundation models
Foundation models are regulated differently because their risk profile is created by reuse, not just by a single deployment. Their general-purpose design means one model can become the basis for many products, many operators, and many downstream harms, so the legal question cannot stop at the immediate use case. The act therefore follows the model’s breadth, governance, and foreseeable reuse.
That distinction matters because a narrow AI system is usually evaluated against one bounded function, while a foundation model may be embedded, fine-tuned, wrapped, or repurposed in ways the original provider did not fully control. Regulation at the model layer is meant to capture responsibilities that survive across contexts, especially where transparency, safety testing, documentation, and post-market oversight need to travel with the model.
For the broader governance logic behind that approach, the eu ai act itself is the primary reference point, and the Commission’s EU AI Act regulatory framework explains how obligations differ for general-purpose AI and high-risk systems.
Why narrow AI can be treated more locally
Narrow AI systems usually have a more specific intended purpose, clearer operating boundaries, and a more stable deployment context. That makes it easier to regulate the system through the risk profile of the application, the sector, and the operator’s control environment. In practice, the model and the use case are more tightly coupled, so the law can focus on the concrete service delivered rather than the general capability hidden underneath it.
Foundation models break that assumption. The same base model may be used in customer support, code generation, search augmentation, summarisation, or decision support, and each deployment can introduce different safety and governance issues. A use-case-only approach can miss model behaviour that is latent at build time but material after integration, especially when a downstream developer adds tools, retrieval, or human-in-the-loop workflows that change the effective risk.
That is why current guidance increasingly separates upstream model obligations from downstream deployment obligations. The NIST AI 600-1 Generative AI Profile is useful here because it treats provenance, testing, disclosure, and lifecycle controls as model-governance issues, not just application features.
What this means for governance, transparency, and control design
The practical effect of the EU AI Act’s approach is that providers of foundation models need stronger upstream governance than teams building a single narrow model. Documentation, evaluation, transparency, and oversight have to be good enough for reuse, not just for one release. That shifts attention toward model inventory, training-data and capability understanding, downstream distribution controls, and clear responsibility boundaries between model provider, integrator, and deployer.
This is also why organisations should not treat foundation-model compliance as a one-time approval exercise. Reuse changes the threat surface over time: new prompting patterns, fine-tunes, connectors, and agentic wrappers can turn an acceptable model into a materially different system. Governance therefore has to track configuration drift, update cycles, and the context in which the model is actually operating.
In security terms, the same logic appears in broader control frameworks that emphasise governance and lifecycle management for shared capability. The NIST Cybersecurity Framework 2.0 is helpful when you need to translate that model-level obligation into inventory, oversight, and response responsibilities.
Risk and Threat Considerations
Foundation models concentrate risk because a single defect, omission, or unsafe capability can propagate across many downstream systems. The most common failure mode is not one bad application, but repeated reuse of a model whose behaviour is only partially understood, poorly documented, or later expanded with new tooling and access paths.
Failure mechanism: Providers or integrators assume the risk is fully captured by the immediate use case, then miss model-level issues such as unsafe generalisation, inadequate transparency, or unreviewed downstream repurposing.
Impact: The same weakness can appear in multiple products and business units, increasing regulatory exposure, inconsistent disclosures, and the chance that unsafe model behaviour reaches users at scale.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST AI RMF, NIST AI 600-1 and NIST CSF 2.0 set the technical controls, while EU AI Act define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | GOVERN — Govern | AI governance must track model-level responsibilities across reuse and deployment. |
| Recommendation — Establish governance for model inventory, roles, and lifecycle oversight across reuse. | ||
| NIST AI 600-1 | GOV-1 — Model governance and lifecycle | GenAI profile focuses on provenance, testing, and lifecycle controls for reusable models. |
| Recommendation — Document model provenance, testing, and update controls before downstream release. | ||
| NIST CSF 2.0 | GV.OC-01 — Organizational Context | Foundation-model governance depends on defining roles, scope, and accountability across uses. |
| Recommendation — Define responsibility boundaries for model provider, integrator, and deployer. | ||
| EU AI Act | Article 52 — Transparency obligations | Foundation models require transparency duties that differ from a single narrow use case. |
| Recommendation — Apply transparency and disclosure requirements at the model level, not only the app level. | ||
Practitioner Guidance
What to prioritise: Separate model-provider obligations from application-owner obligations. For foundation models, the key decision is whether your controls follow the model across reuse, fine-tuning, and distribution, not whether one deployment passed a local review.
What to verify: Confirm that the organisation can explain what the model is, where it is reused, what changed after integration, and who owns post-deployment monitoring. If that answer depends on a single product team, the governance model is too narrow.
Common mistake: Treating a foundation model like a normal application component. That shortcut usually underestimates how much the risk profile changes once the same model becomes a platform for many different downstream uses.
Practitioner takeaway: The compliance question is not only what the model does today, but whether the governance model still holds when the same foundation model is reused in contexts its original design did not fully control.
Related resources from NHI Mgmt Group
- How should global enterprises build an EU AI Act readiness program for AI systems and GPAI models?
- How should security teams structure EU AI Act compliance for AI systems?
- How should organisations classify AI systems for EU AI Act compliance?
- Which controls matter most when AI systems are covered by both the EU AI Act and US state laws?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 17, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
