The COMPEL Glossary Graph visualizes relationships between framework terminology, showing how concepts interconnect across domains, stages, and pillars. Term nodes cluster by pillar affiliation while cross-references reveal semantic dependencies — for example, how risk appetite connects to control effectiveness, model governance, and assurance requirements. This network representation helps practitioners navigate the framework vocabulary and understand that COMPEL terminology forms a coherent conceptual system rather than isolated definitions.
COMPEL Glossary / provider
Provider
Under Regulation (EU) 2024/1689, a natural or legal person, public authority, agency, or other body that develops an AI system or a general-purpose AI model — or has it developed — and places it on the market or puts it into service under its own name or trademark, whether for payment or free of charge.
Synonyms
AI system provider , EU AI Act provider
See also
- Deployer — Under Regulation (EU) 2024/1689, a natural or legal person, public authority, agency, or other body using an AI system under its authority — except where the AI system is used in the course of a personal non-professional activity..
- Importer — Under Regulation (EU) 2024/1689, a natural or legal person located or established in the Union that places on the Union market an AI system bearing the name or trademark of a natural or legal person established outside the Union..
- Distributor — Under Regulation (EU) 2024/1689, any natural or legal person in the supply chain — other than the provider or the importer — that makes an AI system available on the Union market..
- High-risk AI system — Under Regulation (EU) 2024/1689, an AI system falling under Article 6(1) because it is a safety component of, or is itself, a product covered by Annex I Union harmonization legislation, or under Article 6(2) because its use case falls within Annex III — unless exempted by the Article 6(3) derogation..
- GPAI (general-purpose AI model) — Under Regulation (EU) 2024/1689, an AI model — including where trained with a large amount of data using self-supervision at scale — that displays significant generality and can competently perform a wide range of distinct tasks, regardless of how it is placed on the market..