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Article 15 — Accuracy, Robustness and Cybersecurity

High-risk AI systems must achieve appropriate levels of accuracy, robustness and cybersecurity, including resilience against adversarial input and feedback loops.

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What the Article requires

Article 15 sets three substantive performance requirements. Accuracy: declared metrics and measurement methodology. Robustness: resilience against errors, faults, inconsistencies, and the technical state of the art. Cybersecurity: resilience against attempts to alter use, behaviour or performance, including data-poisoning, model-poisoning, adversarial examples, and confidentiality attacks. The Article also addresses 'feedback loops' (where outputs are reused as future training inputs) — these must be mitigated.

In engineering terms

Accuracy is the easy part for teams with mature ML/AI engineering — declare the eval metric, run the harness on every release, publish the result. Robustness implies a structured evaluation harness with adversarial scenarios. Cybersecurity for AI is the newest discipline: prompt-injection threat modelling, model-poisoning detection on continuous-learning systems, and the feedback-loop mitigation discipline for any system whose outputs train downstream models.

Compliance checklist

  • ✓Accuracy metrics declared and tracked across releases
  • ✓Robustness eval harness including adversarial scenarios
  • ✓Prompt-injection threat model documented
  • ✓Cybersecurity controls per ENISA AI guidelines
  • ✓Feedback-loop mitigation discipline

Terms used here

EU AI ActAnnex III high-risk AI

All Articles in the reference · The EU AI Act compliance architecture

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The EU AI Act Readiness Kit puts this checklist, and the other 13 Articles', into one fill-in PDF with room for an owner, evidence location and target date on every item. Plus an AI system inventory, a classification record, an Annex IV outline and a FRIA template.

  • 01How to use this kit. Four steps, and which checklists apply to each classification result.
  • 02AI system inventory. One row per AI system: owner, what it does, built or bought, your role and its classification.
  • 03Classification record. The five questions from our applicability checker, with space for answers, notes and the result.
  • 04Evidence checklists. 72 evidence items across Articles 9 to 15, 25, 26, 27, 50, 53, 55 and 72, each with an owner, evidence location and target date.
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