# Article 15 - Accuracy, robustness and cybersecurity

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.
2.
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.
3.
The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying 
instructions of use.
4.
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.
The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include 
backup or fail-safe plans.
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.
5.
High-risk AI systems shall be resilient against attempts by unauthorised third parties to alter their use, outputs or 
performance by exploiting system vulnerabilities.
The technical solutions aiming to ensure the cybersecurity of high-risk AI systems shall be appropriate to the relevant 
circumstances and the risks.
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.

---
Source: Regulation (EU) 2024/1689 (Artificial Intelligence Act) as amended by Regulation (EU) 2026/1744, consolidated text of 27 July 2026 (Official Journal of the EU). This page is AI-generated legal information, not legal advice. Verify against the Official Journal.
Canonical: https://euaiactlaw.com/article/15