Risk Classification and Compliance of AI Systems under the EU AI Act

The European Union’s AI Act introduces a framework for assessing and regulating AI systems based on risk levels. While the Act defines risk categories and compliance requirements, the classification of General-Purpose AI (GPAI) systems remains complex. This paper proposes a systematic risk classification enabling operators to categorise AI systems in accordance with the AI Act. We introduce a four-step classification process, distinguishing between high-risk AI systems, GPAI with and without systemic risk. Additionally, we outline key architectural considerations, including LLM-based AI agents, Retrieval-Augmented Generation, and hybrid deployment models. To validate our approach, we apply the framework to three real-world use cases, demonstrating its applicability. Our findings emphasise the challenges and impact of risk-based AI governance, offering practical insights for businesses developing or implementing GPAI under the AI Act.

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Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-15
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Risk Classification and Compliance of AI Systems under the EU AI Act

Journal of the Association for Information Systems
Ethics and Social Impacts of AI
article

Risk Classification and Compliance of AI Systems under the EU AI Act

article en

Abstract

The European Union’s AI Act introduces a framework for assessing and regulating AI systems based on risk levels. While the Act defines risk categories and compliance requirements, the classification of General-Purpose AI (GPAI) systems remains complex. This paper proposes a systematic risk classification enabling operators to categorise AI systems in accordance with the AI Act. We introduce a four-step classification process, distinguishing between high-risk AI systems, GPAI with and without systemic risk. Additionally, we outline key architectural considerations, including LLM-based AI agents, Retrieval-Augmented Generation, and hybrid deployment models. To validate our approach, we apply the framework to three real-world use cases, demonstrating its applicability. Our findings emphasise the challenges and impact of risk-based AI governance, offering practical insights for businesses developing or implementing GPAI under the AI Act.

Journal of the Association for Information Systems
Pforzheim University of Applied Sciences (DE)
Partnerships for the goals
Openalex Percentile: Top 45%
Ethics and Social Impacts of AI
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Risk Classification and Compliance of AI Systems under the EU AI Act · Journal of the Association for Information Systems (2025) | TGRS Research Map | TGRS