Source: OJ L, 2024/1689, 12.7.2024

Current language: EN

Article 15 Accuracy, robustness and cybersecurity


Summary What does Article 15 of the AI Act say?

This article sets out the technical performance requirements that providers must build into high-risk AI systems, covering three core pillars: accuracy, robustness, and cybersecurity.

It sits within the broader set of requirements for high-risk AI systems established in Chapter III, and compliance with it feeds directly into the conformity assessment process under Article 43.

The article is notably technical in character, going beyond high-level principles to specify the types of threats and failure modes — such as feedback loops, data poisoning, and adversarial attacks — that system design must actively address.

Important points:

  • Design and develop high-risk AI systems to achieve and consistently maintain appropriate levels of accuracy, robustness, and cybersecurity throughout their lifecycle.
  • Declare the accuracy levels and relevant accuracy metrics of the high-risk AI system in the instructions for use.
  • Build in resilience against AI-specific cybersecurity threats, including data poisoning, model poisoning, adversarial examples, and confidentiality attacks, with technical solutions appropriate to the relevant circumstances and risks.

Springlex's summary of the article, a reading aid, not a substitute for the legal text.

    1. High-risk AI systems shall be designed and developed in such a way that they achieve an appropriate level of accuracy, robustness, and cybersecurity, and that they perform consistently in those respects throughout their lifecycle.

    1. To address the technical aspects of how to measure the appropriate levels of accuracy and robustness set out in paragraph 1 and any other relevant performance metrics, the Commission shall, in cooperation with relevant stakeholders and organisations such as metrology and benchmarking authorities, encourage, as appropriate, the development of benchmarks and measurement methodologies.

    1. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.

    1. High-risk AI systems shall be as resilient as possible regarding errors, faults or inconsistencies that may occur within the system or the environment in which the system operates, in particular due to their interaction with natural persons or other systems. Technical and organisational measures shall be taken in this regard.

    2. The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include backup or fail-safe plans.

    3. High-risk AI systems that continue to learn after being placed on the market or put into service shall be developed in such a way as to eliminate or reduce as far as possible the risk of possibly biased outputs influencing input for future operations (feedback loops), and as to ensure that any such feedback loops are duly addressed with appropriate mitigation measures.

    1. High-risk AI systems shall be resilient against attempts by unauthorised third parties to alter their use, outputs or performance by exploiting system vulnerabilities.

    2. The technical solutions aiming to ensure the cybersecurity of high-risk AI systems shall be appropriate to the relevant circumstances and the risks.

    3. The technical solutions to address AI specific vulnerabilities shall include, where appropriate, measures to prevent, detect, respond to, resolve and control for attacks trying to manipulate the training data set (data poisoning), or pre-trained components used in training (model poisoning), inputs designed to cause the AI model to make a mistake (adversarial examples or model evasion), confidentiality attacks or model flaws.

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