Direct answer
What does "reasonably foreseeable misuse" mean in the AI Act?
This falls under Article 18: documentation keeping. That obligation applies from 2 December 2027.
First step: Set up the ten year retention of the system documentation.
You describe: You encounter the term in requirements and documentation and want to know what to do with it concretely in design and management. Likely role: provider (design); deployer (use).
This applies now
- Article 4: AI literacyApplicable
Coming up
- Article 18: documentation keepingfrom 2 December 2027
- Article 11: technical documentationfrom 2 December 2027
- Article 9: risk management systemfrom 2 December 2027
- Article 17: quality management systemfrom 2 December 2027
Depends on your situation
- Article 61: informed consent of test subjects for testing in real world conditionsArticle 60(4), point (i), with Article 61(1)
These provisions only apply once the stated fact is established. The locator says which provision settles it.
Reasonably foreseeable misuse is use that deviates from the intended purpose but results from reasonably predictable human behaviour or interaction with other systems. Providers must account for it in risk management and instructions; deployers recognise it in practice, for example when teams use a tool for a different purpose than intended.
Your first actions
- Set up the ten year retention of the system documentation. Bring the five components of Article 18(1) together per high-risk system in an identifiable place, record both the date of placing on the market and the date of putting into service, calculate the end date from the later moment, and assign the upkeep to a role rather than to a person.
- Take role- and context-specific AI literacy measures. Determine for each role, system and context which combination of instruction, guidance, practice or training is appropriate.
- Build the technical file per Annex IV. Document system description, development process, data, oversight measures, performance and risk management before market placement.
Record this
- Retention file per high-risk system
- AI literacy measures record
- Technical file (Annex IV)
biometrics and identification
Facial recognition at access control: the guard behind the camera counts too
An organisation secures the entrances to its buildings with facial recognition and uses that biometric access control to register visitors as well. When the system returns no match, a security officer reviews the camera images and decides personally whether someone may enter. The question is whose measures have to reach that officer: those of the supplier of the model, those of the department that deploys the system, or both.
Provenance: Article 4(1) provides that providers and deployers of AI systems shall take measures to support the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf. In doing so they must take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, and consider the persons or groups of persons on whom the AI systems are to be used. The same provision states that this obligation does not require providers or deployers to guarantee any specific level of AI literacy of any individual.
We read the phrase about the persons on whom the system is used as the centre of gravity for biometrics: whoever stands in front of the camera is subject to the outcome and has little to set against it. That argues for equipping the officer who decides for himself when no match comes back more substantively than the colleague who merely switches the system on and off. The article itself names no sufficient level and expressly states that you need not guarantee one, so where the floor lies for each role stays open. In our assessment a record kept per role, stating the choice made and the reason for it, is easier to defend than one organisation-wide session backed only by an attendance list.
Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.
Article 4(1)
law enforcement
Police using AI in investigations: context sets how deep the training goes
A police force uses AI to search large volumes of investigation files and surface connections a detective would otherwise miss. The outputs feed into the choice of which suspect is pursued further and end up in documents that enter the criminal process. The question is whether one and the same basic instruction is enough for the analyst operating the model and for the detective who acts on its output.
Provenance: Article 4(1) requires providers and deployers of AI systems to take measures supporting the development of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf. The provision prescribes that they take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, as well as the persons or groups of persons on whom the AI systems are to be used. It also states that this obligation does not require any specific level of AI literacy of any individual to be guaranteed.
Article 4 requires you to weigh the context of use and the people the system is applied to, and in law enforcement both factors run high on our reading. Whether a general introduction to what AI can do is then enough for someone carrying an output into a file that affects a person's position as a suspect, we doubt, but the provision expressly names no level you must guarantee, so that floor is yours to justify. We would record for each role what someone must be able to recognise, for instance that a discovered connection is not yet evidence, and revisit that choice periodically.
Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.
Article 4(1)
media and content
Newsroom with generative AI: do freelancers count within your measures?
A newsroom uses generative AI to prepare summaries, headlines and imagery, after which an editor finishes the piece and the desk decides to publish. Part of that work sits with freelancers, and an outside agency produces marketing content with the same tools. The question is whether your AI literacy measures must reach those freelancers and that agency, or only the people on the payroll.
Provenance: Article 4(1) is addressed to providers and deployers of AI systems and requires them to take measures supporting the development of AI literacy of their staff and of other persons dealing with the operation and use of AI systems on their behalf. The provision requires them to take into account technical knowledge, experience, education and training and the context the AI systems are to be used in, as well as the persons or groups of persons on whom the AI systems are to be used. It does not require any specific level of AI literacy of any individual to be guaranteed.
Alongside staff, the text expressly names other persons dealing with the operation and use of AI systems on your behalf, and we read that as a functional boundary rather than a contractual one. On that reading a freelance editor using your tool inside your workflow and on your instruction sits within your measures, employment contract or not. The outside agency is a harder case: if it works in your environment and on your instruction, the argument that it acts on your behalf holds up, but if it runs its own tools on its own account it is a deployer in its own right, and Article 4 does not say your measures must cover that work. In practice, in our assessment, that means recording in your agreements who works in which role and what instruction you give, rather than trusting the other side to arrange it.
Editorial example. The rule above is in the Regulation. The situation was written by us to show how that rule plays out in this sector, and is not taken from a worked case in official guidance.
Article 4(1)
government and public services
An induction call with the customer at the moment of go-live
Asimov AI is a micro organisation of at most fifteen people that supplies AI services for legislative work to government institutions and companies. With every new contract it holds one or more induction calls with the team leads and officials who will use the platform, explaining how the platform and the underlying models work and how hallucinations arise in this domain and can be mitigated.
Provenance: This practice was submitted by the organisation itself to the Commission living repository. The repository collects and shares practices; it does not approve them or set them as a standard.
This practice puts literacy where the risk arises: with the people who will operate the system, at the moment they start. For a small provider that is also the only workable moment, because there is no training department to redo it later. Anyone adopting it should record who attended and what was explained, because otherwise the effort survives only in the participants memory a year on.
Living repository of AI literacy practices, practice submitted by the organisation concerned
No mandatory course format, no certificate, no exam and no AI officer
The Commission Q&A on AI literacy states that there is no one size fits all when it comes to AI literacy and that no strict requirements or mandatory trainings are imposed. On certification, the Q&A states literally that there is no need for a certificate and that organisations can keep an internal record of trainings or other guiding initiatives. On assessment, it states that Article 4 of the AI Act does not entail an obligation to measure the AI knowledge of employees. On governance, it states that no specific governance structure is mandated to comply with Article 4, so that unlike the data protection officer under the GDPR, no AI officer needs to be appointed. On the level, the Q&A states that following the Digital Omnibus amendment AI literacy remains an obligation for providers and deployers of AI systems, but that no specific or sufficient level is mandated and that the Regulation does not require guaranteeing any specific level of AI literacy of any individual. Against that, the Q&A states that simply relying on the AI systems' instructions for use or asking staff to read them might be ineffective, and that organisations should take into account general AI understanding within the organisation, whether they are a provider or a deployer, the risks associated with the systems deployed, staff knowledge gaps considering technical knowledge, experience, education and training, and contextual factors such as sector, purpose and affected populations. The Q&A further states that organisations may implement different levels of training or learning approaches depending on knowledge, experience, education and role, and that staff with a degree or experience in AI development are normally considered AI literate, while the organisation must still verify that those persons understand the specific AI systems of the organisation, know how to deal with them and are aware of all risks.
Commission Q&A on AI literacy, sections on required level, training formats, certificates, assessment of knowledge and governance structures (consulted 9 August 2026)
Article 4 reaches beyond your own staff, and the national supervisor enforces it
The Commission Q&A on AI literacy states that Article 4 applies to providers and deployers of AI systems and in addition to other persons dealing with the operation and use of AI systems on their behalf, covering persons broadly within the organisational remit, with a contractor, a service provider and a client given as examples. On clients, the Q&A states that they may need AI literacy depending on the specific risk, reasoning that affected persons should understand how decisions taken with the assistance of AI will have an impact on them. On geographic scope, the Q&A states that the AI Act's legal framework applies to both public and private actors inside and outside the EU as long as the AI system is placed on the Union market, used in the Union, or its use has an impact on people located in the EU. On supervision, the Q&A states that the supervision and enforcement of Article 4 is not with the AI Office but under the remit of national market surveillance authorities, and that supervision and enforcement began on 2 August 2026, while Article 4 itself entered into application on 2 February 2025. On sanctions, the Q&A states that national market surveillance authorities could impose penalties and other enforcement measures for infringements of Article 4, that this will be based on national laws that Member States were due to adopt by 2 August 2025, that any sanction must be proportionate and based on the individual case taking into account factors such as the nature and gravity of the infringement and its intentional or negligent character, and that sanctions are more likely if there is proof of an incident due to a lack of appropriate training and guidance. Article 4 is not listed in the enumeration in Article 99(4) of the AI Act, which covers only Articles 16, 22, 23, 24, 26, 31, 33(1), (3) and (4), 34 and 50, so the level of any penalty for Article 4 follows from national law rather than from the Regulation's own ceilings. The Q&A further states that Article 4 reinforces the transparency provisions of Article 13 and the human oversight provisions of Article 14 and indirectly contributes to the protection of affected persons, and that for deployers of high-risk systems the Article 26 obligation to ensure staff are trained to ensure human oversight is a distinct requirement; that requirement becomes applicable on 2 December 2027 for standalone Annex III systems and on 2 August 2028 for Annex I systems.
Commission Q&A on AI literacy, sections on target groups, geographic scope, supervision and enforcement, and sanctions (consulted 9 August 2026)
Integrating AI Act requirements into existing risk and quality systems
According to the draft guidelines of 19 May 2026, which are expressly published as a draft for stakeholder feedback and have no binding force, the AI Act provides mechanisms to reduce the compliance burden for economic operators. The draft guidelines cite Article 8(2) AI Act on the interplay with sectoral legislation, Article 9(10) AI Act on risk management and Article 17(3) AI Act on quality management, which allow economic operators to add, where necessary and appropriate, an assessment of AI-specific risks to already existing risk and quality management systems. Article 40 AI Act further requires that harmonised standards under the AI Act be consistent with standards developed under the Annex I harmonisation legislation. The draft guidelines state that these mechanisms enable economic operators to meet both the AI Act and the harmonisation legislation within a single compliance framework, thereby avoiding duplication of effort while maintaining a high level of protection of health, safety and fundamental rights.
Draft guidelines Annex I, points (61) and (62)
EN 18286:2026: quality management system for EU AI Act regulatory purposes
EN 18286:2026 (Artificial intelligence: Quality management system for EU AI Act regulatory purposes) was drafted by CEN/CLC/JTC 21 under standardisation request M/613 and approved by CEN-CENELEC on 12 July 2026. It is the first JTC 21 deliverable to reach publication. According to a published coverage statement accompanying the standard, not yet confirmed by a second independent source, it addresses Article 17(1) points (a) to (m) and Article 11(1) first sentence, and expressly not Article 17(2) to (4) or Article 72. The standard is NOT currently cited in the Official Journal. The Article 40 presumption of conformity only attaches after that citation.
EN ISO/IEC 42001: artificial intelligence management system
ISO/IEC 42001:2023 is the first certifiable international standard for an AI management system, published on 18 December 2023 and structured on the plan-do-check-act cycle. The text was adopted unchanged as EN ISO/IEC 42001:2026, approved by CEN on 13 March 2026, with national implementation by the member standards bodies. This adoption is not a deliverable under standardisation request M/613: the standard is not cited in the Official Journal and therefore confers no presumption of conformity under Article 40. For Article 17, the designated deliverable under M/613 is EN 18286:2026; that standard is likewise not cited in the Official Journal.
ISO/IEC 23894: guidance on risk management for AI
ISO/IEC 23894:2023 (Information technology: Artificial intelligence: Guidance on risk management) was published in February 2023 and is the first international standard dealing specifically with risk management for AI. It is non-prescriptive and built on the ISO 31000 structure. The text was adopted by CEN-CENELEC as EN ISO/IEC 23894:2024. It is not cited in the Official Journal and therefore confers no presumption of conformity under Article 40 of the AI Act. The deliverable intended to do so for Article 9 is prEN 18228.
prEN 18228: AI risk management for high-risk systems
prEN 18228 (AI risk management) is the JTC 21 deliverable under M/613 intended to confer presumption of conformity with Article 9 of the AI Act: the risk management system that providers of high-risk AI systems must establish, implement, document and maintain across the full lifecycle. The public Enquiry ran until 30 July 2026. The standard has not yet been published as an EN and is not cited in the Official Journal. The prEN designation means it is a draft text.
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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