Direct answer
What instructions for use must we supply with our AI system?
You describe: You supply a high-risk AI system to another organisation. They must be able to use it as you intended, which is only possible if you supply what the system can and cannot do. Likely role: provider (you place the system on the market).
This applies now
- For this situation, the preparation phase matters most right now.
Coming up
- Article 13: transparency towards deployersfrom 2 December 2027
- Article 14: human oversightfrom 2 December 2027
- Article 15: accuracy, robustness and cybersecurityfrom 2 December 2027
The instructions for use are not a manual but a legal document: they define the intended purpose, and therefore when a deployer steps outside your boundaries and becomes a provider themselves. Include the known limitations, the expected level of accuracy and how human oversight is meant to work.
Your first actions
- Provide complete instructions for use. Describe capabilities, limitations, accuracy, oversight measures and expected lifetime in comprehensible form.
- Design and assign effective human oversight. Determine oversight measures per system, appoint competent persons and give them the mandate to intervene or stop.
- Set and test performance and security levels. Determine appropriate accuracy, test robustness against errors and misuse, and take AI-specific security measures.
Record this
- Instructions and interpretation file
- Oversight file per system
- Performance and security file
ISO/IEC 12792: transparency taxonomy of AI systems
ISO/IEC 12792:2025 (Information technology: Artificial intelligence: Transparency taxonomy of AI systems) was published in November 2025 by ISO/IEC JTC 1/SC 42. It specifies a taxonomy of information elements to help stakeholders identify and address transparency needs, and describes the semantics of those elements and their relevance to different stakeholders' objectives. The text was adopted as a European standard as EN ISO/IEC 12792:2025. It is not cited in the Official Journal and therefore confers no presumption of conformity under Article 40. For Article 13 the designated deliverable is prEN 18229-3.
ISO/IEC 24029 series: assessment of the robustness of neural networks
ISO/IEC TR 24029-1:2021 (Assessment of the robustness of neural networks, Part 1: Overview) is a technical report mapping the topic and available assessment methods. ISO/IEC 24029-2:2023 (Part 2: Methodology for the use of formal methods) describes the application of formal methods in assessing robustness. The series is aimed at AI developers and users assessing robustness across the lifecycle. Neither part is cited in the Official Journal, so no presumption of conformity under Article 40 arises. For Article 15 the designated deliverable is prEN 18229-2, which as at June 2026 was still in drafting.
prEN 18229-2: AI trustworthiness framework part 2, accuracy and robustness
prEN 18229-2 (AI trustworthiness framework, Part 2: Accuracy and robustness) is the JTC 21 deliverable under M/613 for Article 15 of the AI Act, which requires high-risk AI systems to achieve an appropriate level of accuracy, robustness and cybersecurity and to declare accuracy metrics in the instructions for use. As at June 2026 the deliverable was at the drafting stage. It has not yet been published as an EN and is not cited in the Official Journal.
prEN 18229-3: AI trustworthiness framework part 3, transparency and human oversight
prEN 18229-3 (AI trustworthiness framework, Part 3: Transparency and human oversight) is the JTC 21 deliverable under M/613 addressing Articles 13 and 14 of the AI Act: transparency and provision of information to deployers, and the design for effective human oversight of high-risk AI systems. As at June 2026 the deliverable was at the drafting stage. It has not yet been published as an EN and is not cited in the Official Journal. This deliverable concerns Article 14 (high-risk) and not the Article 50 transparency obligations, which apply since 2 August 2026.
General interpretation, not legal advice. Checked against Regulation (EU) 2024/1689 and the Digital Omnibus (EU) 2026/1744; the official source remains authoritative.
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