Agent · ai-act-2024-1689-15
AI Act artikel 15: Accuracy, robustness and cybersecurity
Structural tree: the article's own paragraphs, verbatim.
CELEX 32024R1689 · 2026-08-18 · Weight 88 · minimal-risk
PremiumHand written or high weight rule tree. Metered per call at the edge once metering is switched on, at the same address and with the same answer as today.
- What this page is
- Agent, AI Act artikel 15
- Checked against the official source
- 2026-08-18Current
- Responsible publisher
- ExploreWorld Legal, editorial deskLiability position
Short answer
What does AI Act Article 15 require, and what outcome does the rule tree give?
AI Act Article 15 is tested here by a deterministic rule tree of 9 rules, built from the article's own conditions. The tree reads your facts and names the outcome that applies, starting with Paragraph 1 applies, carrying paragraph citation, content hash and read date 2026-08-18 against CELEX 32024R1689. The outcome is a machine classification, not a compliance decision.
AI Act Article 15Checked against the publisher 2026-08-18Official text
- Paragraph 1 applies. 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.
- Paragraph 2 applies. 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.
- Paragraph 3 applies. 3. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.
A source reference, not legal advice.
Jurisdiction
The same agent, read through one country's lens.
Inputs
- in_scopeThe article applies to the situationboolean
- punktParagraph of the articleenum (1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9)
Rule tree
If: alla(in_scope = true, punkt = 1)
Paragraph 1 applies
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.
Paragraph 1
If: alla(in_scope = true, punkt = 2)
Paragraph 2 applies
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.
Paragraph 2
If: alla(in_scope = true, punkt = 3)
Paragraph 3 applies
3. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.
Paragraph 3
If: alla(in_scope = true, punkt = 4)
Paragraph 4 applies
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.
Paragraph 4
If: alla(in_scope = true, punkt = 5)
Paragraph 5 applies
The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include backup or fail-safe plans.
Paragraph 5
If: alla(in_scope = true, punkt = 6)
Paragraph 6 applies
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.
Paragraph 6
If: alla(in_scope = true, punkt = 7)
Paragraph 7 applies
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.
Paragraph 7
If: alla(in_scope = true, punkt = 8)
Paragraph 8 applies
The technical solutions aiming to ensure the cybersecurity of high-risk AI systems shall be appropriate to the relevant circumstances and the risks.
Paragraph 8
If: alla(in_scope = true, punkt = 9)
Paragraph 9 applies
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 mod…
Paragraph 9
If no rule matches: The article is not stated to apply, or no paragraph is selected. The agent abstains rather than guesses.
The article text as read
- 11. 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.
- 22. 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.
- 33. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.
- 44. 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.
- 5The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include backup or fail-safe plans.
- 6High-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.
- 75. High-risk AI systems shall be resilient against attempts by unauthorised third parties to alter their use, outputs or performance by exploiting system vulnerabilities.
- 8The technical solutions aiming to ensure the cybersecurity of high-risk AI systems shall be appropriate to the relevant circumstances and the risks.
- 9The 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.
Lineage
Interface
Hashes
Artefacts
No legal advice. Deterministisk regeluppslagning. Ingen juridisk rådgivning, inget efterlevnadsbeslut, ingen bedömning av ett enskilt ärende.
Citation: 32024R1689 art. 15, Accuracy, robustness and cybersecurity. ExploreWorld Legal, https://legal.exploreworldai.com/agent/ai-act-2024-1689/artikel-15 (hämtad 2026-08-18, bevis sha256:855cd1db897845dd, bygge legal-2026-08-25).