RK-006MedioFornitoreInvestigativo
Registrazione delle metriche di performance e operative
I fornitori devono registrare le metriche di performance e operative pertinenti per verificare che il sistema di IA operi entro i parametri stabiliti nella documentazione tecnica, compresi indicatori di accuratezza e tempo di risposta.
Articoli:Article 12(1)Article 15(1)
Esempi di prove
- Operational metrics dashboard
- Performance log samples
- Threshold breach alert records
Norme
ISO 42001:2023 §9.1
Controlli correlati
- RK-001Automatic Event Logging CapabilityArticle 12(1) requires that high-risk AI systems are designed and developed with automatic logging capabilities, enabling the reconstruction of events…
- RK-002Log Integrity and Tamper ProtectionProviders must ensure that logs are protected against tampering, unauthorised deletion, or modification, using cryptographic integrity controls,…
- RK-003Log Retention for Minimum 10 YearsArticle 18(1) requires that providers retain technical documentation and logs for at least 10 years after the high-risk AI system is placed on the market…
- RK-004Log Accessibility for Competent AuthoritiesProviders must ensure that event logs are accessible to competent national authorities and market surveillance authorities upon request, with procedures…
- RK-005Event Traceability and ReconstructionLogs must enable the tracing and reconstruction of decision events to understand the inputs processed, the outputs generated, and the conditions under…
- RK-007Anomaly and Error Detection LoggingThe logging system must capture anomalous behaviour, errors, and unexpected outputs from the AI system in operation, enabling timely detection of…
Termini correlati del Regolamento IA
- Logging CapabilitiesThe automatic recording of events by a high-risk AI system during its operation — required under Article 12 to enable monitoring of its operation, post-hoc investigation of incidents, and to support the post-market monitoring obligations of providers and deployers.
- AccuracyThe requirement that high-risk AI systems achieve an appropriate level of accuracy in relation to their intended purpose, as specified in the technical documentation. Providers must declare the level of accuracy in the instructions for use.
- RobustnessThe ability of a high-risk AI system to maintain its level of performance under adverse conditions — including technical limitations, adversarial inputs, errors, or unexpected situations — or within foreseeable operating conditions outside the intended purpose.
- CybersecurityThe requirement that high-risk AI systems are resilient against attempts by third parties to alter their use, behaviour, or performance in ways that could result in risks to health, safety, or fundamental rights, including protection against data poisoning, adversarial examples, and model evasion attacks.
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