Compliance Made Practical: Translating the EU AI Act into Implementable Security Actions

The EU AI Act, along with emerging regulations in other countries, mandates that AI systems meet security requirements to prevent risks associated with AI misuse and vulnerabilities. However, for practitioners, defining and achieving a secure AI system is complex and context-dependent, posing challenges in understanding what actions they need to take and when they are sufficient. ISO/IEC TR 24028/29 and ENISA Securing Machine Learning Algorithms offer a comprehensive framework for AI security, aligning with the EU AI Act's requirements by addressing risks, threats, and mitigation strategies. However, for practical implementation, these reports lack hands-on guidance. Industry resources like the OWASP AI Exchange and OWASP LLM Top 10 fill this gap by providing accessible, actionable insights for securing AI systems effectively. This paper addresses the question of responsibility in AI risk mitigation, especially for companies utilizing pretrained or off-the-shelf models. We want to clarify how companies can practically comply with the upcoming ISO 27090 and ensure compliance with the EU AI Act through actionable security strategies tailored to this prevalent use case.

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Publication Details

Published
2025-04-29
DOI
https://doi.org/10.1109/raie66699.2025.00016
Citations
2
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
4.96
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article

Compliance Made Practical: Translating the EU AI Act into Implementable Security Actions

Niklas Bunzel
2 citations
Privacy-Preserving Technologies in Data
4.96
article

Compliance Made Practical: Translating the EU AI Act into Implementable Security Actions

Niklas Bunzel
article en
2 citations

Abstract

The EU AI Act, along with emerging regulations in other countries, mandates that AI systems meet security requirements to prevent risks associated with AI misuse and vulnerabilities. However, for practitioners, defining and achieving a secure AI system is complex and context-dependent, posing challenges in understanding what actions they need to take and when they are sufficient. ISO/IEC TR 24028/29 and ENISA Securing Machine Learning Algorithms offer a comprehensive framework for AI security, aligning with the EU AI Act's requirements by addressing risks, threats, and mitigation strategies. However, for practical implementation, these reports lack hands-on guidance. Industry resources like the OWASP AI Exchange and OWASP LLM Top 10 fill this gap by providing accessible, actionable insights for securing AI systems effectively. This paper addresses the question of responsibility in AI risk mitigation, especially for companies utilizing pretrained or off-the-shelf models. We want to clarify how companies can practically comply with the upcoming ISO 27090 and ensure compliance with the EU AI Act through actionable security strategies tailored to this prevalent use case.

Technische Universität Darmstadt (DE), Fraunhofer Institute for Secure Information Technology (DE)
Openalex Percentile: Top 5%
Privacy-Preserving Technologies in Data
4.96
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