What EU AI Act - Quality Management System Obligations for High-Risk AI Providers (Article 17) requires
EU AI Act (Regulation 2024/1689) Article 17 requires providers of high-risk AI systems to put in place a quality management system (QMS) that ensures compliance with the EU AI Act; Article 17(1) - providers of high-risk AI systems shall put in place a quality management system that ensures compliance with this Regulation; the quality management system shall be documented in a systematic and orderly manner in the form of written policies, procedures and instructions, and shall cover at least: (a) a strategy for regulatory compliance, including compliance with conformity assessment procedures and procedures for managing modifications to the high-risk AI system; (b) techniques, procedures and systematic actions to be used for the design, design control and design verification of the high-risk AI system; (c) techniques, procedures and systematic actions to be used for the development, quality control and quality assurance of the high-risk AI system; (d) examination, test and validation procedures to be carried out before, during and after the development of the high-risk AI system and the frequency with which they have to be carried out; (e) technical specifications, including standards, to be applied and, where the relevant harmonised standards are not applied in full, the means to be used to ensure that the high-risk AI system meets the requirements set out in Chapter III Section 2; (f) systems and procedures for data management, including data collection, data analysis, data labelling, data storage, data filtration, data mining, data aggregation, data retention and any other operation regarding the data that is performed before and for the purposes of the placing on the market or the putting into service of high-risk AI systems; (g) the risk management system referred to in Article 9; (h) the setting up, implementation and maintenance of a post-market monitoring system, in accordance with Article 61; (i) procedures related to the reporting of serious incidents in accordance with Article 62 and of malfunctions; (j) the handling of communication with national competent authorities, other competent authorities, the European AI Office, notified bodies, other operators, customers or other interested parties; (k) systems and procedures for record keeping of all relevant documentation and information; (l) resource management, including measures related to supply chain security; (m) an accountability framework setting out the responsibilities of the management and other staff with regard to all aspects listed in this Article 17.
Pillar: AI Governance & Law · Authority: European Parliament and Council of the EU · Version: 1.0.0 · Last updated:
Primary source: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689
SHA-256 integrity: cd30e39bef22e2274b4410ac93efd1278b45d487ab6c2e06df5f2ab1bde5716d
Primary Citations — 5 traced to source
- {"title":"Regulation (EU) 2024/1689 - EU AI Act: Article 17 - Quality Management System","url":"https://eur-lex.europa.eu","relevance":"Primary EU AI Act provision establishing the mandatory quality management system requirement for high-risk AI system providers - Article 17 specifies the documentation format requirement (written policies, procedures, and instructions) and all 13 mandatory QMS elements (Article 17(1)(a)-(m)) including regulatory compliance strategy, design procedures, testing, data management, risk management, post-market monitoring, incident reporting, authority communications, record keeping, resource management, and accountability framework"}
- {"title":"Regulation (EU) 2024/1689 - EU AI Act: Article 9 - Risk Management System","url":"https://eur-lex.europa.eu","relevance":"EU AI Act provision cross-referenced in Article 17(1)(g) - the risk management system required by Article 9 must be integrated into the Article 17 QMS as a mandatory element; the QMS and the risk management system are interdependent: QMS procedures govern how the risk management system is operated, reviewed, and documented; risk management findings inform QMS design and testing procedures"}
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