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42001",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-daf7ae3019","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-9cae086ee7",":items":{"title_v2":{"id":"title-v2-22f8d36a25","additionalClasses":"hub-hero__headline","type":"heading1","lines":["ISO/IEC 42001:2023: requisiti, controlli e certificazione"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-f10821268f","additionalClasses":"hub-hero__subheadline","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003ELa norma ISO/IEC 42001 consente alle organizzazioni di rendere verificabile la governance dell’AI mediante ruoli, controlli, processi di gestione del rischio, audit e cicli di miglioramento documentati. Questo articolo spiega cosa prevede la norma, la confronta con l’AI RMF del NIST e l’AI Act dell’UE e illustra come una piattaforma certificata consenta alle organizzazioni di strutturare e gestire le evidenze relative a dati, ciclo di vita e controlli di governance.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn 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Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip":{"id":"content-chip-c77f826080","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--af497c82-10f0-4f45-805e-7f1c4677a824/david.jpg?preferwebp=true&quality=85","alt":"David Gaule","isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["David Gaule","Technical Editor, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip_copy","content_chip"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["title_v2","text","container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-933cee7637",":items":{"youtube":{"id":"embed-471bf65afa","youtubeVideoId":"-HWNc-Hd90U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-af92f34727","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-e76dc90b2f",":items":{"text_894059747":{"id":"text-f22e09624d","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EApprofondimenti sulla governance dell’AI:\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-5c53886e43","additionalClasses":"related-topics ","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-compliance/\"\u003ECompliance dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003EEtica dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EGestione del rischio dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ETracciabilità dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003ETrasparenza dell’AI\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EBias algoritmico\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EAI Act dell’Unione europea\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EAI responsabile\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-bb8631cc02","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":items":{"callout_what_is_ai_g":{"id":"text-a848b67e01","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EISO 42001: LA DEFINIZIONE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EISO/IEC 42001:2023 è uno standard internazionale che definisce requisiti e linee guida per creare, mantenere e migliorare continuamente un sistema di gestione dell’AI (AIMS), affinché le organizzazioni possano sviluppare, fornire o utilizzare l’AI in modo responsabile.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_intro_0":{"id":"text-f7b3e5bd97","text":"\u003Cp\u003EMolti programmi di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003Egovernance dell’AI\u003C/a\u003E privilegiano le policy, ma hanno processi deboli. Quando i modelli passano dallo sviluppo alla produzione, viene aggiunta una nuova sorgente dati o un fornitore aggiorna una feature, i controlli che sembravano adeguati al momento del deployment diventano rapidamente insufficienti e non esiste un meccanismo integrato per rilevare la lacuna.\u003C/p\u003E\n\u003Cp\u003ELa norma ISO/IEC 42001:2023 è il primo standard internazionale per i sistemi di gestione dell’AI e fornisce alle organizzazioni il modello operativo necessario. Specifica come le organizzazioni possono definire, implementare, mantenere e migliorare continuamente i processi utilizzati per governare i \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Esistemi di IA\u003C/a\u003E, trasformando la governance dell’AI da semplice documento a sistema di gestione verificabile, con responsabilità definite, trattamento dei rischi, cicli di audit e requisiti relativi alle evidenze.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sources_of_alg":{"id":"title-v2-17fd3aa5b7","additionalClasses":"anchor-title anchor-title--what-is-iso-42001","type":"heading2","lines":["Che cos’è ISO/IEC 42001?"],":type":"snowflake-site/components/title-v2"},"text_what-is-algorithmic-bias_0":{"id":"text-416908ed5a","text":"\u003Cp\u003ELa norma \u003Ca href=\"https://www.iso.org/standard/42001\" target=\"_blank\"\u003EISO/IEC 42001:2023\u003C/a\u003E è uno standard internazionale che specifica i requisiti di un sistema di gestione dell’intelligenza artificiale (AIMS). Un sistema di gestione dell’intelligenza artificiale (AIMS) è l’insieme di policy, ruoli, processi, controlli e informazioni documentate che un’organizzazione utilizza per governare lo sviluppo, la fornitura o l’uso dei sistemi di IA.\u003C/p\u003E\n\u003Cp\u003EPubblicata nel dicembre 2023 dall’ISO/IEC JTC 1/SC 42, la norma si applica a diversi settori e tipi di sistemi di IA, tra cui il machine learning predittivo, l’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EAI generativa\u003C/a\u003E e i \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/agents/ \"\u003Esistemi agentici\u003C/a\u003E. Può essere applicato alle organizzazioni che forniscono prodotti di AI, implementano sistemi di IA nei processi aziendali o utilizzano sistemi di IA in contesti di produzione.\u003C/p\u003E\n\u003Cp\u003EISO/IEC 42001 adotta la struttura armonizzata utilizzata da altre norme ISO per i sistemi di gestione, tra cui ISO 27001 e ISO 9001. Questo approccio è vantaggioso perché le organizzazioni con sistemi di gestione già maturi possono spesso riutilizzare pratiche consolidate: definire l’ambito, assegnare le responsabilità alla leadership, mantenere informazioni documentate, condurre audit interni, esaminare le performance e promuovere il miglioramento continuo.\u003C/p\u003E\n\u003Cp\u003ELa norma include anche i controlli dell’Appendice A, che riguardano temi specifici della governance dell’AI, come le policy sull’AI, l’organizzazione interna, le risorse, le valutazioni d’impatto, i processi del ciclo di vita, i dati, le informazioni per le parti interessate, l’uso dei sistemi di IA e i rapporti con terze parti. Come in ISO 27001, l’adozione dei controlli dell’Appendice A dipende dalla loro applicabilità; le organizzazioni documentano quindi quali controlli si applicano e per quale motivo mediante una Dichiarazione di applicabilità.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_sources-of-algorithmic-bias":{"id":"title-v2-2d543420f2","additionalClasses":"anchor-title anchor-title--why-iso-42001-matters","type":"heading2","lines":["Perché la norma ISO 42001 è importante"],":type":"snowflake-site/components/title-v2"},"text_sources-of-algorithmic-bias_0":{"id":"text-c5f282bc99","text":"\u003Cp\u003ELa norma ISO/IEC 42001 consente alle organizzazioni di dimostrare che la governance dell’AI è attuata concretamente e non solo descritta. Questa distinzione è importante quando clienti, autorità di regolamentazione, consigli di amministrazione o team responsabili degli acquisti chiedono come un’organizzazione controlla i sistemi di IA che sviluppa o utilizza.\u003C/p\u003E\r\n\u003Ch3\u003EAttestazione di terze parti\u003C/h3\u003E\r\n\u003Cp\u003ELa certificazione fornisce alle organizzazioni una garanzia indipendente che il loro AIMS soddisfa i requisiti della norma. L’ISO definisce la certificazione come una garanzia scritta, rilasciata da un organismo indipendente, che un prodotto, un servizio o un sistema soddisfa requisiti specifici. Per i team che si occupano di AI, ciò consente di dimostrare l’adozione di pratiche di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EAI responsabile\u003C/a\u003E senza esporre dettagli proprietari dei modelli, architetture interne o dati sensibili dei clienti.\u003C/p\u003E\r\n\u003Ch3\u003ESupporto al procurement\u003C/h3\u003E\r\n\u003Cp\u003ECon l’inserimento di domande sulla governance dell’AI nelle richieste di offerta e nelle valutazioni del rischio dei fornitori, ISO 42001 offre a fornitori e deployer una norma standard a cui fare riferimento. Una certificazione non risponde a ogni domanda tecnica, ma riduce l’incertezza sulla presenza di processi formali di governance dell’AI, cicli di audit e supervisione da parte della direzione.\u003C/p\u003E\r\n\u003Ch3\u003EAllineamento normativo\u003C/h3\u003E\r\n\u003Cp\u003EL’adozione di ISO 42001 è volontaria, ma la struttura del suo sistema di gestione è strettamente allineata alle nuove aspettative normative in materia di AI. \u003Ca href=\"https://eur-lex.europa.eu/eli/reg/2024/1689/oj?locale=it\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EL’AI Act dell’Unione europea\u003C/a\u003E impone ai fornitori di sistemi di IA ad alto rischio di mantenere sistemi documentati di gestione della qualità e dei rischi, nonché processi di monitoraggio successivo all’immissione sul mercato. ISO 42001 offre una base strutturata che le organizzazioni possono utilizzare per sostenere le iniziative di compliance dell’AI: le policy, le procedure e le evidenze richieste da tali obblighi.\u003C/p\u003E\r\n\u003Ch3\u003ECadenza operativa\u003C/h3\u003E\r\n\u003Cp\u003ELa governance dell’AI richiede una cadenza regolare. Le valutazioni dei rischi diventano obsolete, i dati cambiano, il comportamento dei modelli varia e i casi d’uso aziendali si ampliano. ISO 42001 formalizza questa cadenza mediante pianificazione, valutazione delle prestazioni, audit interno, riesame della direzione, azioni correttive e miglioramento continuo.\u003C/p\u003E\r\n\u003Ch3\u003EFiducia dei clienti\u003C/h3\u003E\r\n\u003Cp\u003ELa certificazione ISO/IEC 42001 dimostra ai clienti l’impegno di un’organizzazione verso \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etrasparenza\u003C/a\u003E, responsabilità e fiducia nelle pratiche di sicurezza dell’AI. Ad esempio, Snowflake ha ottenuto la certificazione ISO/IEC 42001 a seguito di un audit indipendente condotto da terzi, e i clienti possono accedere alle relative evidenze di audit tramite il \u003Ca href=\"https://trust.snowflake.com/\"\u003ECompliance Center Snowflake\u003C/a\u003E.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_detecting-and-measuring-algorithmic-bias":{"id":"title-v2-a0e63abbc9","additionalClasses":"anchor-title anchor-title--why-should-use-iso-42001","type":"heading2","lines":["Chi dovrebbe utilizzare ISO 42001"],":type":"snowflake-site/components/title-v2"},"text_detecting-and-measuring-algorithmic-bias_0":{"id":"text-069c6b8281","text":"\u003Cp\u003EISO/IEC 42001 si applica alle organizzazioni che forniscono, sviluppano, implementano o utilizzano sistemi di IA. Il rischio legato all’AI non riguarda soltanto chi sviluppa i modelli. Può emergere anche quando un’azienda configura un prodotto SaaS con funzionalità AI, integra un sistema di IA in un workflow, lo collega a dati sensibili o ne usa gli output per decisioni che incidono su clienti, dipendenti o attività operative.\u003C/p\u003E\n\u003Cp\u003ELa norma è rilevante per diversi tipi di organizzazioni:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EI fornitori\u003C/b\u003E sviluppano sistemi di IA, prodotti con funzionalità AI o servizi AI destinati all’uso da parte di terzi. Un fornitore può sviluppare un modello di rilevamento delle frodi, un assistente di Gen AI, un sistema di computer vision o un’applicazione AI integrata in un prodotto più ampio.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EI deployer\u003C/b\u003E mettono in uso i sistemi di IA. Un deployer può configurare un servizio AI fornito da un vendor, integrarlo con i sistemi interni, collegarlo a dati governati o definire il workflow in cui il personale utilizza gli output dell’AI.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGli utenti\u003C/b\u003E utilizzano sistemi di IA in ambienti di produzione. In pratica, possono essere team aziendali, enti pubblici, organizzazioni regolamentate e funzioni interne che usano l’AI per analisi, automazione, supporto decisionale o generazione di contenuti.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EISO 42001 è indipendente dal settore, ma la sua adozione anticipata è particolarmente rilevante per organizzazioni del settore pubblico, settori regolamentati e aziende che sviluppano prodotti AI, spesso soggetti a requisiti più rigorosi in materia di controlli documentati, evidenze per il procurement e processi di governance giustificabili.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what_is_ai_g_294410134":{"id":"text-e2802c1e28","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EL’ERRORE COMUNE\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003ELa certificazione ISO 42001 non deve essere considerata una casella da spuntare una tantum. La governance deve evolvere insieme a modelli, fonti dati, vendor e casi d’uso attraverso monitoraggio, riesame della direzione, azioni correttive e miglioramento continuo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_mitigating-algorithmic-bias":{"id":"title-v2-9cb3c9359f","additionalClasses":"anchor-title anchor-title--the-ten-clauses-of-iso-42001","type":"heading2","lines":["Le 10 clausole di ISO/IEC 42001"],":type":"snowflake-site/components/title-v2"},"text_mitigating-algorithmic-bias_0":{"id":"text-5619d9d058","text":"\u003Cp\u003EISO/IEC 42001 segue la struttura armonizzata adottata dalle norme ISO sui sistemi di gestione. Le clausole da 1 a 3 definiscono l’ambito di applicazione, i riferimenti normativi, i termini e le definizioni; le clausole da 4 a 10 specificano i requisiti del sistema di gestione che un’organizzazione deve implementare e mantenere.\u003C/p\u003E\n\u003Ch3\u003EClausole 1–3: Scopo e campo di applicazione, riferimenti normativi, termini e definizioni\u003C/h3\u003E\n\u003Cp\u003ELe clausole iniziali stabiliscono l’ambito della norma, i riferimenti applicabili e le definizioni dei termini principali. Nei programmi di governance dell’AI, questo vocabolario comune è importante perché i team delle aree business, legale, data science, ingegneria e sicurezza spesso attribuiscono significati diversi agli stessi termini. La norma offre a questi team un riferimento condiviso per l’AIMS.\u003C/p\u003E\n\u003Ch3\u003EClausola 4: Contesto dell’organizzazione\u003C/h3\u003E\n\u003Cp\u003ELa clausola 4 richiede all’organizzazione di comprendere i fattori interni ed esterni che influiscono sul suo sistema di gestione dell’AI. Sono inclusi le esigenze degli stakeholder, le aspettative legali e normative, i casi d’uso di AI, i confini organizzativi e l’ambito di applicazione dell’AIMS.\u003C/p\u003E\n\u003Cp\u003EIn termini pratici, un’azienda deve sapere quali sistemi di IA rientrano nell’AIMS, quali team ne sono responsabili, da quali fonti dati dipendono e quali soggetti esterni sono interessati dai loro output.\u003C/p\u003E\n\u003Ch3\u003EClausola 5: Leadership\u003C/h3\u003E\n\u003Cp\u003ELa clausola 5 riguarda l’impegno della leadership, la policy sull’AI, i ruoli, le responsabilità e le autorità. Richiede all’alta dirigenza di integrare la governance dell’AI nel sistema di gestione dell’organizzazione, anziché trattarla come un processo secondario affidato soltanto ai team tecnici.\u003C/p\u003E\n\u003Cp\u003EIn genere, ciò significa nominare responsabili a cui rendere conto, approvare una policy sull’AI, allineare le responsabilità tra team aziendali e tecnici, e garantire che chi ha obblighi di governance disponga dell’autorità necessaria per adempierli.\u003C/p\u003E\n\u003Ch3\u003EClausola 6: Pianificazione\u003C/h3\u003E\n\u003Cp\u003ELa clausola 6 riguarda la pianificazione, comprese la valutazione e il trattamento dei rischi legati all’AI e la definizione degli obiettivi per l’AI. Le organizzazioni devono identificare rischi e opportunità, decidere come affrontarli e definire obiettivi misurabili per l’AIMS.\u003C/p\u003E\n\u003Cp\u003EUna valutazione dei rischi può esaminare la qualità dei dati, le prestazioni del modello, la spiegabilità, la supervisione umana, la sicurezza, la privacy, il \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ebias algoritmico\u003C/a\u003E, la resilienza operativa o l’impatto a valle. Il piano di trattamento dei rischi collega quindi tali rischi a controlli, responsabili ed evidenze.\u003C/p\u003E\n\u003Ch3\u003EClausola 7: Supporto\u003C/h3\u003E\n\u003Cp\u003ELa clausola 7 riguarda le risorse necessarie per gestire l’AIMS: competenze, consapevolezza, comunicazione e informazioni documentate. Una policy non basta se i responsabili dei modelli non sanno quando eseguire una valutazione d’impatto, chi svolge le revisioni non trova le evidenze richieste o gli utenti business non comprendono le modalità d’uso accettabili dell’AI.\u003C/p\u003E\n\u003Cp\u003EIn questa clausola, le organizzazioni definiscono la formazione, le pratiche di documentazione, i canali di comunicazione e le registrazioni che attestano le attività di governance.\u003C/p\u003E\n\u003Ch3\u003EClausola 8: Attività operative\u003C/h3\u003E\n\u003Cp\u003ELa clausola 8 riguarda la pianificazione e il controllo operativi dei sistemi di IA. In questa fase, i processi del ciclo di vita diventano concreti: requisiti, decisioni di progettazione, preparazione dei dati, verifica e convalida, controlli sulla distribuzione, operatività, monitoraggio e dismissione.\u003C/p\u003E\n\u003Cp\u003EAd esempio, un’organizzazione può aver bisogno di un processo documentato per approvare un modello prima della messa in produzione, modificarne gli input di dati, estenderne l’uso a una nuova popolazione o dismetterlo quando non soddisfa più le aspettative definite dalla policy o in termini di prestazioni.\u003C/p\u003E\n\u003Ch3\u003EClausola 9: Valutazione delle prestazioni\u003C/h3\u003E\n\u003Cp\u003ELa clausola 9 richiede monitoraggio, misurazione, analisi, valutazione, audit interno e riesame della direzione. L’organizzazione deve sapere se l’AIMS funziona e se i controlli di governance restano adeguati ai propri sistemi di IA.\u003C/p\u003E\n\u003Cp\u003ECiò può includere test dei controlli, risultanze degli audit, risultati del monitoraggio dei modelli, tendenze degli incidenti, stato del trattamento dei rischi e registrazioni dei riesami della direzione.\u003C/p\u003E\n\u003Ch3\u003EClausola 10: Miglioramento\u003C/h3\u003E\n\u003Cp\u003ELa clausola 10 riguarda le non conformità, le azioni correttive e il miglioramento continuo. Quando emerge una risultanza di audit, un incidente, un problema di monitoraggio o un malfunzionamento di processo, l’organizzazione deve rispondere in modo strutturato.\u003C/p\u003E\n\u003Cp\u003EIn genere, la risposta comprende l’analisi del problema, la correzione immediata, la risoluzione delle cause profonde e l’aggiornamento dell’AIMS per ridurre la probabilità che lo stesso malfunzionamento si ripeta.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_regulatory-expectations-on-algorithmic-bias":{"id":"title-v2-b596b91916","additionalClasses":"anchor-title anchor-title--annex-a-38-ai-specific-controls","type":"heading2","lines":["Appendice A: 38 controlli specifici per l’AI"],":type":"snowflake-site/components/title-v2"},"text_regulatory-expectations-on-algorithmic-bias_0":{"id":"text-ef1b294ea4","text":"\u003Cp\u003EL’Appendice A definisce 38 controlli specifici per l’AI, raggruppati in categorie. Questi controlli aiutano le organizzazioni a tradurre i requisiti del sistema di gestione in pratiche operative, ma non sono automaticamente obbligatori in ogni contesto. Le organizzazioni ne valutano l’applicabilità e documentano le proprie motivazioni in una Dichiarazione di applicabilità.\u003C/p\u003E\n\u003Ch3\u003EA.2: Politiche relative all’AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.2 riguarda le policy sull’AI, compreso il modo in cui l’organizzazione definisce le aspettative per un’AI responsabile, assegna i ruoli e gestisce gli approvvigionamenti. Una policy deve essere sufficientemente specifica da orientare le decisioni sullo sviluppo e sull’uso dell’AI, indicando chi può approvare un sistema di IA, quali criteri di rischio si applicano e come vengono esaminati i sistemi di IA di terze parti.\u003C/p\u003E\n\u003Ch3\u003EA.3: Organizzazione interna\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.3 riguarda la struttura di governance, le linee gerarchiche e l’allocazione delle risorse. La governance dell’AI coinvolge spesso responsabili business, team di dati, funzioni legali, compliance, sicurezza, privacy, approvvigionamenti e team di piattaforma. L’obiettivo del controllo è quindi rendere chiare le responsabilità prima che emerga un problema.\u003C/p\u003E\n\u003Ch3\u003EA.4: Risorse per i sistemi di AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.4 riguarda le risorse necessarie per sviluppare, distribuire e gestire i sistemi di IA, inclusi dati, strumenti, infrastruttura, risorse di sistema e supervisione umana. Per un sistema di IA in produzione, queste risorse possono includere set di dati approvati, ambienti di sviluppo dei modelli, strumenti di monitoraggio, workflow di revisione e percorsi di escalation.\u003C/p\u003E\n\u003Ch3\u003EA.5: Valutazione degli impatti dei sistemi di AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.5 riguarda le valutazioni d’impatto. Queste valutazioni consentono alle organizzazioni di determinare in che modo un sistema di IA potrebbe influire su persone, gruppi, processi aziendali o stakeholder esterni prima e dopo la distribuzione.\u003C/p\u003E\n\u003Cp\u003EUna valutazione d’impatto può esaminare se un sistema di IA influenza l’accesso a un servizio, produce raccomandazioni che incidono sulle persone, tratta attributi sensibili o crea una dipendenza operativa in un workflow critico.\u003C/p\u003E\n\u003Ch3\u003EA.6: Ciclo di vita dei sistemi di AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.6 riguarda i processi del ciclo di vita, inclusi requisiti, progettazione, verifica, convalida, distribuzione, operatività e dismissione. La categoria di controllo riflette una realtà fondamentale della governance: il rischio legato all’AI cambia man mano che i sistemi attraversano le diverse fasi.\u003C/p\u003E\n\u003Cp\u003EUn modello che appare accettabile durante lo sviluppo può comportarsi diversamente dopo la connessione a dati live, quando riceve nuovi prompt, serve una base utenti più ampia o entra a far parte di un workflow automatizzato.\u003C/p\u003E\n\u003Ch3\u003EA.7: Dati per i sistemi di AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.7 riguarda l’acquisizione, la qualità, la provenienza e la preparazione dei dati. Questi controlli sono particolarmente importanti perché i sistemi di IA spesso ereditano i rischi dai propri dati. Se un set di addestramento, il contesto di un prompt, una tabella di feature o un corpus di recupero non dispone di una titolarità definita, del data lineage, di controlli di qualità o di vincoli di utilizzo, l’output del modello può diventare difficile da considerare affidabile o da giustificare.\u003C/p\u003E\n\u003Cp\u003EControlli solidi in questa categoria richiedono in genere di documentare la provenienza dei dati, le trasformazioni applicate, le soglie di qualità previste e le restrizioni d’uso che accompagnano i dati nei workflow di AI.\u003C/p\u003E\n\u003Ch3\u003EA.8: Informazioni per le parti interessate\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.8 riguarda le informazioni fornite alle parti interessate, tra cui documentazione, comunicazioni e reporting. A seconda del sistema di IA, le parti interessate possono includere clienti, autorità di regolamentazione, revisori interni, utenti aziendali, auditor o persone interessate dagli output del sistema.\u003C/p\u003E\n\u003Cp\u003EQuesta categoria può includere la documentazione del modello, le istruzioni per gli utenti, le informative sui rischi, le limitazioni note, i report di monitoraggio e i processi di comunicazione degli incidenti.\u003C/p\u003E\n\u003Ch3\u003EA.9: Uso dei sistemi di AI\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.9 riguarda l’utilizzo responsabile e la dismissione dei sistemi di IA. Le organizzazioni devono adottare processi che regolino l’utilizzo dei sistemi di IA dopo la messa in produzione, inclusi gli usi accettabili, le responsabilità degli utenti, i requisiti di monitoraggio e i criteri di dismissione.\u003C/p\u003E\n\u003Cp\u003EQuesto aspetto è importante per la Gen AI e i sistemi agentici, nei quali la stessa capacità di base può essere utilizzata in workflow diversi con profili di rischio differenti.\u003C/p\u003E\n\u003Ch3\u003EA.10: Relazioni con terze parti e clienti\u003C/h3\u003E\n\u003Cp\u003ELa sezione A.10 riguarda le responsabilità di fornitori e clienti. I sistemi di IA dipendono spesso da modelli esterni, prodotti SaaS, fornitori di dati, partner di integrazione o clienti downstream. I controlli chiariscono la ripartizione delle responsabilità, le evidenze richieste e le modalità di revisione delle modifiche ai servizi di terze parti.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what_is_ai_g_1310486392":{"id":"text-fecadc43b3","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECONSIGLIO RAPIDO\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EInizia definendo i sistemi di IA inclusi nell’ambito di applicazione e le evidenze necessarie per dimostrare l’efficacia dei controlli, poiché la certificazione ISO 42001 richiede di comprovare l’applicazione concreta della governance, non soltanto l’esistenza di policy scritte.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-snowflake-helps-organizations-address-algorithmic-bias":{"id":"title-v2-708d6b5552","additionalClasses":"anchor-title anchor-title--how-iso-42001-certification-works","type":"heading2","lines":["Come funziona la certificazione ISO 42001"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-helps-organizations-address-algorithmic-bias_0":{"id":"text-5d85de4bc3","text":"\u003Cp\u003ELa certificazione ISO/IEC 42001 segue il consolidato percorso di certificazione dei sistemi di gestione utilizzato per altre norme ISO. I dettagli variano in base all’organizzazione, all’ambito di applicazione e all’organismo di certificazione, ma la maggior parte dei programmi segue la stessa sequenza di base.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAnalisi della maturità o delle lacune:\u003C/b\u003E l’organizzazione valuta le proprie pratiche di governance dell’AI rispetto ai requisiti della norma ISO 42001. Questa analisi individua in genere ambiti di applicazione non definiti, responsabilità non documentate, valutazioni dei rischi incoerenti, controlli del ciclo di vita inadeguati o evidenze di audit insufficienti.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EImplementazione dell’AIMS:\u003C/b\u003E l’organizzazione istituisce o aggiorna l’AIMS. Ciò include policy, procedure, valutazioni dei rischi, valutazioni d’impatto, controlli del ciclo di vita, pratiche di documentazione, formazione, reporting e una Dichiarazione di applicabilità per i controlli dell’Allegato A.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAudit interno:\u003C/b\u003E prima dell’audit di certificazione, l’organizzazione esegue un audit interno per verificare se l’AIMS soddisfa i requisiti e se i controlli funzionano come descritto.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAudit di certificazione di fase 1: \u003C/b\u003El’organismo di certificazione esamina la maturità, l’ambito di applicazione e la documentazione. L’obiettivo è stabilire se l’organizzazione è pronta per l’audit di implementazione.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAudit di certificazione di fase 2:\u003C/b\u003E l’auditor verifica che l’AIMS sia implementato e operativo. Le attività possono includere interviste, esame delle evidenze, campionamento, verifica dei controlli e analisi della documentazione relativa al riesame della direzione.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAudit di sorveglianza e ricertificazione:\u003C/b\u003E la certificazione non è un evento una tantum. In genere, le organizzazioni sono sottoposte ad audit di sorveglianza annuali e a ricertificazione ogni tre anni, rafforzando così il modello di miglioramento continuo previsto dalla norma.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ELe organizzazioni possono rivolgersi a organismi di certificazione accreditati come BSI, Schellman, A-LIGN, Mazars, TÜV o DNV, a seconda dell’area geografica, dell’ambito di applicazione e della disponibilità. I tempi per ottenere la certificazione variano notevolmente, ma molte organizzazioni devono prevedere un percorso di 6–18 mesi. I tempi possono essere più brevi se dispongono già di sistemi di gestione maturi basati sulle norme ISO 27001, ISO 9001 o su norme analoghe.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bias-control-starts-before-the-model":{"id":"title-v2-6b27265cb4","additionalClasses":"anchor-title anchor-title--iso-42001-compared-to-other-ai-frameworks","type":"heading2","lines":["ISO 42001 a confronto con altri framework per l’AI"],":type":"snowflake-site/components/title-v2"},"text_bias-control-starts-before-the-model_0":{"id":"text-4f86bdfe9a","text":"\u003Cp\u003ELa norma ISO 42001 non sostituisce ogni \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/framework/\"\u003Eframework di governance dell’AI\u003C/a\u003E né gli obblighi di legge. Risulta più efficace quando le organizzazioni ne comprendono la finalità: istituire un sistema di gestione per l’AI certificabile.\u003C/p\u003E\n\u003Ctable\u003E\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework o norma\u003C/th\u003E\n\u003Cth\u003EChe cos’è\u003C/th\u003E\n\u003Cth\u003ERelazione con ISO 42001\u003C/th\u003E\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EAI RMF del NIST\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EFramework volontario statunitense per la gestione dei rischi dell’AI attraverso le funzioni Govern, Map, Measure e Manage\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EL’AI RMF del NIST fornisce una metodologia per la gestione dei rischi; ISO 42001 fornisce la struttura certificabile dell’AIMS.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EAI Act dell’Unione europea\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ENormativa vincolante dell’UE per i sistemi di IA, con obblighi basati sulla categoria di rischio\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EISO 42001 è una norma volontaria, ma supporta i processi di gestione della qualità, gestione dei rischi e monitoraggio richiesti per i sistemi di IA ad alto rischio.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EISO/IEC 23894\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ELinee guida per la gestione dei rischi dell’AI\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EISO/IEC 23894 consente alle organizzazioni di gestire i rischi specifici dell’AI; ISO 42001 è la norma certificabile per i sistemi di gestione che può integrare tali processi di gestione dei rischi.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EISO/IEC 22989\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ETerminologia e concetti dell’AI\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EISO/IEC 22989 promuove un vocabolario coerente per l’AI, che facilita la documentazione e la comunicazione nell’ambito di un AIMS.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EISO/IEC 23053\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EFramework concettuale per i sistemi di IA che utilizzano il ML\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EISO/IEC 23053 consente di descrivere i componenti e le funzioni dei sistemi di IA; ISO 42001 disciplina il sistema di gestione relativo allo sviluppo, al deployment e all’uso dell’AI.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ESOC 2\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ERelazione di attestazione sui controlli, comunemente utilizzata per sicurezza, disponibilità, riservatezza, integrità dell’elaborazione e privacy\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003ESOC 2 fornisce assurance su criteri di controllo selezionati, mentre ISO 42001 è una norma ISO per i sistemi di gestione specifica per la governance dell’AI.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cp\u003EMolte organizzazioni utilizzano questi framework congiuntamente. È possibile utilizzare l’\u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003EAI RMF del NIST\u003C/a\u003E per strutturare l’analisi dei rischi dell’AI, ISO/IEC 23894 per approfondire le pratiche di gestione dei rischi, ISO/IEC 22989 per uniformare il vocabolario e ISO 42001 per definire l’AIMS che rende tali pratiche ripetibili e verificabili tramite audit.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bias_control_s":{"id":"title-v2-c3201348ff","additionalClasses":"anchor-title anchor-title--iso-42001-and-snowflake","type":"heading2","lines":["ISO/IEC 42001 e Snowflake"],":type":"snowflake-site/components/title-v2"},"text_bias_control_st":{"id":"text-a7fa2bd67b","text":"\u003Cp\u003ESnowflake ha ottenuto la certificazione ISO/IEC 42001: una terza parte accreditata ha sottoposto ad audit l’AIMS Snowflake rispetto ai requisiti della norma per una governance responsabile dell’AI, e le evidenze dell’audit sono disponibili per i clienti tramite il Compliance Center Snowflake.\u003C/p\u003E\n\u003Cp\u003EPer i clienti che sviluppano un proprio programma ISO 42001, una piattaforma certificata supporta alcuni aspetti dell’ambiente di controllo della governance dei dati e dell’AI. La piattaforma non certifica l’AIMS del cliente, perché ogni organizzazione resta responsabile dei propri casi d’uso di AI, delle valutazioni dei rischi e degli impatti, delle policy e delle decisioni operative. Consente però alle organizzazioni di raccogliere evidenze che dimostrino che i controlli fondamentali su dati e infrastruttura sono governati, documentati e verificabili tramite audit.\u003C/p\u003E\n\u003Cp\u003ELe funzionalità Snowflake supportano diverse aree di controllo dell’Appendice A:\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E offre un’ampia \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003Etracciabilità\u003C/a\u003E e visibilità, consentendo di governare dati, app e modelli mediante metadati, data lineage, cronologia degli accessi, classificazione, tagging e applicazione delle policy. Horizon consente di gestire la governance dei dati nell’ambiente Snowflake e nello storage esterno; le funzionalità Access History e Time Travel supportano il riesame delle attività passate e degli stati precedenti dei dati. Queste funzionalità sono pertinenti ai controlli dell’Appendice A relativi ai dati per l’AI, alla documentazione del ciclo di vita, alla governance degli accessi e alla reportistica.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-cortex-ai-cortex-guard-llm-safeguards/\"\u003ECortex Guard\u003C/a\u003E supporta un uso più sicuro dei sistemi di IA filtrando input e output degli LLM potenzialmente non sicuri, e contribuisce così ai controlli relativi alla supervisione umana, all’uso responsabile e alle informazioni fornite agli utenti.\u003C/p\u003E\n\u003Cp\u003EModel Registry consente di gestire i metadati del ciclo di vita dei modelli, le versioni e il contesto di deployment, supportando i controlli relativi alla governance del ciclo di vita, alla documentazione e al riesame operativo.\u003C/p\u003E\n\u003Cp\u003ENel complesso, queste funzionalità consentono alle organizzazioni di applicare la governance agli oggetti da cui dipendono i sistemi di IA: set di dati, modelli, policy, percorsi di data lineage, cronologia degli accessi, documentazione e segnali di utilizzo. Questo aspetto è particolarmente importante per ISO 42001, perché la certificazione dipende dalle evidenze. Occorre dimostrare non solo l’esistenza di una policy, ma anche che i controlli pertinenti siano applicati, riesaminati e migliorati nel tempo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_bias_control_s_1606915274":{"id":"title-v2-b97bb63943","additionalClasses":"anchor-title anchor-title--making-ai-governance-auditable","type":"heading2","lines":["Rendere la governance dell’AI verificabile tramite audit"],":type":"snowflake-site/components/title-v2"},"text_bias_control_st_2142513559":{"id":"text-365955a70b","text":"\u003Cp\u003EISO 42001 offre alle organizzazioni un metodo pratico per trasformare la governance dell’AI da principio a processo. Occorre comunque decidere quali sistemi di IA rientrino nell’ambito di applicazione, chi ne sia responsabile, quali rischi siano rilevanti, quali controlli si applichino e quali evidenze ne dimostrino l’efficacia. La norma fornisce però la struttura necessaria per prendere tali decisioni in modo coerente. \u003C/p\u003E\n\u003Cp\u003EQuando un sistema di IA si basa su dati governati, un ciclo di vita del modello documentato, una supervisione umana definita, controlli sui fornitori, valutazioni d’impatto, monitoraggio e azioni correttive, la governance diventa parte del funzionamento del sistema. Il valore di ISO 42001 consiste nel fornire ai programmi di AI un sistema di gestione che può essere riesaminato, sottoposto ad audit e migliorato con l’evoluzione della tecnologia, dell’organizzazione e del contesto normativo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_what_is_ai_g_1939414635":{"id":"text-157d250cf8","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EIN SINTESI\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EISO/IEC 42001 consente di trasformare la governance dell’AI da un insieme di policy a un sistema di gestione verificabile tramite audit, con responsabilità definite, trattamento dei rischi, controlli, evidenze, audit interni e cicli di miglioramento continuo.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout_what_is_ai_g","text_intro_0","title_sources_of_alg","text_what-is-algorithmic-bias_0","title_sources-of-algorithmic-bias","text_sources-of-algorithmic-bias_0","title_detecting-and-measuring-algorithmic-bias","text_detecting-and-measuring-algorithmic-bias_0","callout_what_is_ai_g_294410134","title_mitigating-algorithmic-bias","text_mitigating-algorithmic-bias_0","title_regulatory-expectations-on-algorithmic-bias","text_regulatory-expectations-on-algorithmic-bias_0","callout_what_is_ai_g_1310486392","title_how-snowflake-helps-organizations-address-algorithmic-bias","text_how-snowflake-helps-organizations-address-algorithmic-bias_0","title_bias-control-starts-before-the-model","text_bias-control-starts-before-the-model_0","title_bias_control_s","text_bias_control_st","title_bias_control_s_1606915274","text_bias_control_st_2142513559","callout_what_is_ai_g_1939414635"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-d7dabe7a7d",":items":{"text_943981956_copy_":{"id":"text-fff3d45751","additionalClasses":"eyebrow-text","text":"\u003Cp\u003ECosa troverai in questa guida\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-11cf2ddc8a","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-iso-42001\"\u003EChe cos’è ISO/IEC 42001?\u003C/li\u003E\n\u003Cli data-anchor=\"why-iso-42001-matters\"\u003EPerché ISO 42001 è importante\u003C/li\u003E\n\u003Cli data-anchor=\"why-should-use-iso-42001\"\u003EChi dovrebbe utilizzare ISO 42001\u003C/li\u003E\n\u003Cli data-anchor=\"the-ten-clauses-of-iso-42001\"\u003ELe 10 clausole di ISO/IEC 42001\u003C/li\u003E\n\u003Cli data-anchor=\"annex-a-38-ai-specific-controls\"\u003EAppendice A: 38 controlli specifici per l’AI\u003C/li\u003E\n\u003Cli data-anchor=\"how-iso-42001-certification-works\"\u003ECome funziona la certificazione ISO 42001\u003C/li\u003E\n\u003Cli data-anchor=\"iso-42001-compared-to-other-ai-frameworks\"\u003EISO 42001 a confronto con altri framework per l’AI\u003C/li\u003E\n\u003Cli data-anchor=\"iso-42001-and-snowflake\"\u003EISO/IEC 42001 e Snowflake\u003C/li\u003E\n\u003Cli data-anchor=\"making-ai-governance-auditable\"\u003ERendere la governance dell’AI verificabile tramite audit\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_963294944":{"id":"flexible-column-container-045d3323bd","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":items":{"title_v2_copy":{"id":"title-v2-1086e991b3","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Domande frequenti"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-7b8b4557ec","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELe risposte degli esperti Snowflake alle domande più comuni sulla norma ISO/IEC 42001.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-67a701e738","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"La norma ISO 42001 è certificabile?","richText":"\u003Cp\u003ESì. ISO/IEC 42001 è una norma per i sistemi di gestione certificabile. Gli organismi di certificazione accreditati possono sottoporre ad audit l’AIMS di un’organizzazione e rilasciare la certificazione, in genere con audit di sorveglianza annuali e ricertificazione ogni tre anni.\u003C/p\u003E\n"},{"title":"Quanto tempo richiede la certificazione ISO 42001?","richText":"\u003Cp\u003ELa certificazione ISO 42001 richiede in genere 6–18 mesi, a seconda dell’ambito dell’organizzazione, del livello di maturità dell’AI e delle prassi esistenti per i sistemi di gestione. \u003C/p\u003E\n"},{"title":"La norma ISO 42001 è obbligatoria ai sensi dell’AI Act dell’UE?","richText":"\u003Cp\u003ENo. ISO 42001 è una norma volontaria, mentre l’AI Act dell’UE è una normativa vincolante. Tuttavia, ISO 42001 costituisce una solida base per raggiungere la maturità necessaria ai fini dell’AI Act dell’UE, poiché il regolamento prevede obblighi relativi ai sistemi di gestione della qualità, alla gestione dei rischi, compresa la gestione del rischio dei modelli, e al monitoraggio successivo all’immissione sul mercato dei sistemi di IA ad alto rischio.\u003C/p\u003E\n"},{"title":"Qual è il rapporto tra ISO 42001 e l’AI RMF del NIST?","richText":"\u003Cp\u003EISO 42001 e l’AI RMF del NIST sono complementari. L’AI RMF del NIST è un framework volontario per gestire i rischi dell’AI tramite le funzioni Govern, Map, Measure e Manage. ISO 42001 è una norma internazionale certificabile per i sistemi di gestione dell’AI. Molte organizzazioni adottano l’AI RMF del NIST come metodologia per la gestione dei rischi e ISO 42001 per la struttura dell’AIMS, che rende verificabile la governance.\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1467213961":{"id":"flexible-column-container-f62de07174","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-175a251e28",":items":{"title_v2":{"id":"title-v2-d957da574f","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Esplora le risorse 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