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dell’AI",":type":"snowflake-site/components/structure/page","appliedCssClassNames":"summit-page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"]},":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-8b1db6b445","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-d73a911982",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_470483971":{"id":"text-b5e75a8cde","additionalClasses":"eyebrow-text","text":"\u003Cp style=\"padding-top: 8.0px;\"\u003EGuida di riferimento\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-02"},"title_v2":{"id":"title-v2-f55dcbf97d","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Governance dell’AI: guida per le aziende"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-7702009c4e","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EUn’AI sicura e affidabile non dipende solo dalle prestazioni dei modelli. Questa guida illustra come la governance dell’AI consente alle aziende di controllare i rischi, applicare le policy e gestire i moderni sistemi di AI: dai modelli ML tradizionali all’AI generativa e agli agenti.\u003C/p\u003E","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 aem-GridColumn--default--12"},"id":"container-05335a2541",":type":"snowflake-site/components/container",":items":{"content_chip_copy":{"id":"content-chip-e1b20a59b3","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi la biografia"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie Macpherson headshot","lazyEnabled":true,"isLcpImage":true,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie Macpherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip":{"id":"content-chip-b28cef31c1","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 biografia"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--af497c82-10f0-4f45-805e-7f1c4677a824/david.jpg?quality=85&preferwebp=true","alt":"David Gaule","lazyEnabled":true,"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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["text_470483971","title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-871804031f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-4d9dafb43a","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","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-c7c4502b44","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-db50462e72",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-8789190627","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-26334ddfe3","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\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\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-content-governance/\"\u003EGovernance dei contenuti AI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003EEtica dell’AI\u003C/a\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003E\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-fairness/\"\u003EEquità dell’AI\u003C/a\u003E\u003C/li\u003E\r\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\r\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\r\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\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EBias algoritmico\u003C/a\u003E\u003C/li\u003E\r\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\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/iso-42001/\"\u003ENorma ISO 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/model-card/\"\u003EScheda del modello\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/model-governance/\"\u003EGovernance dei modelli\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EAI responsabile\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"]},":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-61f5247a53","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",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"callout_what-is-ai-governance_0":{"id":"text-fddf12e74f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EGovernance dell’AI: la definizione\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ELa governance dell’AI è il framework di policy, standard, controlli e supervisione con cui le organizzazioni sviluppano, distribuiscono e utilizzano l’AI in modo sicuro, equo e responsabile, nel rispetto dei requisiti interni ed esterni.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_intro_0":{"id":"text-3ad387ea8c","text":"\u003Cp\u003EAnche un modello di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Eintelligenza artificiale\u003C/a\u003E (AI) dalle prestazioni elevate può risultare inaffidabile in un sistema di produzione. In una demo, i prompt sono controllati, i dati sono puliti e l’accesso è limitato. In produzione, il sistema potrebbe mostrare policy obsolete, esporre dati ad accesso limitato o attivare azioni indesiderate.\u003C/p\u003E\r\n\u003Cp\u003ELe capacità del modello sono importanti, ma l’affidabilità dipende dall’intero sistema. La governance dell’AI è il modo in cui le organizzazioni definiscono e applicano tale sistema. Associa i casi d’uso a responsabili chiaramente identificati, i dati a policy applicabili e i modelli a workflow di approvazione, monitoraggio e audit trail. Rende gli output, soprattutto quelli ad alto impatto, più verificabili e tracciabili, oltre che coerenti con il funzionamento effettivo dell’azienda.\u003C/p\u003E\r\n\u003Cp\u003ELa governance assume maggiore importanza con l’evoluzione dei sistemi di AI. L’\u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003EAI generativa\u003C/a\u003E, la \u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003Eretrieveal augmented generation\u003C/a\u003E (RAG) e gli \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-agents/\"\u003Eagenti AI\u003C/a\u003E non si comportano come i modelli tradizionali. Recuperano dati, generano contenuti e, in alcuni casi, eseguono azioni. Gestire ogni nuova funzionalità come un caso particolare non è scalabile, mentre la governance offre un approccio coerente per gestirle tutte.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-governance":{"id":"title-v2-3b478756bd","additionalClasses":"anchor-title anchor-title--what-is-ai-governance","type":"heading2","lines":["Che cos’è la governance dell’AI?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-governance_0":{"id":"text-2549390b89","text":"\u003Cp\u003ELa governance dell’AI è l’insieme di policy, standard, controlli e processi di supervisione con cui le organizzazioni sviluppano, distribuiscono e gestiscono l’AI in modo sicuro, equo e responsabile, nel rispetto dei requisiti interni ed esterni. Si applica all’intero ciclo di vita dell’AI, dall’identificazione dei casi d’uso e dalla selezione dei dati allo sviluppo, alla distribuzione e al monitoraggio dei modelli, fino alla risposta agli incidenti e alla dismissione.\u003C/p\u003E\r\n\u003Cp\u003EL’ambito è più ampio di quanto molti team prevedano. La governance dell’AI comprende modelli di machine learning (ML), app di AI generativa, sistemi multimodali e workflow agentici in grado di eseguire azioni mediante diversi strumenti.\u003C/p\u003E\r\n\u003Cp\u003EUn modello di rilevamento delle frodi, un chatbot per l’assistenza clienti, un assistente basato su RAG e un agente AI possono avere architetture molto diverse. Tutti richiedono però gli stessi controlli fondamentali: una registrazione chiara delle operazioni consentite al sistema, dei dati a cui può accedere, di chi lo ha approvato e delle modalità di monitoraggio del suo comportamento nel tempo.\u003C/p\u003E\r\n\u003Cp\u003ELa governance dell’AI si sovrappone a diverse discipline correlate:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003ELa \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003E\u003Cb\u003Egovernance dei dati\u003C/b\u003E\u003C/a\u003E governa data asset come tabelle, viste, policy, metadati, lineage, qualità e accesso.\u003C/li\u003E\r\n\u003Cli\u003ELa \u003Cb\u003Egovernance dei modelli\u003C/b\u003E gestisce i modelli come asset aziendali, mediante registrazione, convalida, documentazione e monitoraggio.\u003C/li\u003E\r\n\u003Cli\u003EL’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003E\u003Cb\u003Eetica dell’AI\u003C/b\u003E\u003C/a\u003E definisce principi come equità, responsabilità e supervisione umana.\u003C/li\u003E\r\n\u003Cli\u003ELa \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/compliance/\"\u003E\u003Cb\u003Ecompliance dell’AI\u003C/b\u003E\u003C/a\u003E si concentra sul rispetto dei requisiti normativi, contrattuali e di audit.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-governance-matters-now":{"id":"title-v2-62f8f81d56","additionalClasses":"anchor-title anchor-title--why-ai-governance-matters-now","type":"heading2","lines":["Perché oggi la governance dell’AI è fondamentale"],":type":"snowflake-site/components/title-v2"},"text_why-ai-governance-matters-now_0":{"id":"text-a71eb11f97","text":"\u003Cp\u003ELa governance dell’AI è diventata un requisito aziendale perché i sistemi di AI sono ormai integrati in decisioni che riguardano pricing, assunzioni, rilevamento delle frodi, customer experience e servizi pubblici. L’AI generativa può far emergere informazioni sensibili. Gli agenti possono eseguire azioni su diversi sistemi. In ogni caso, la questione non è più soltanto se il modello funzioni bene, ma se l’organizzazione sia in grado di controllare e spiegare il sistema in cui opera.\u003C/p\u003E\r\n\u003Cp\u003ELa pressione normativa è uno dei fattori determinanti. L’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EAI Act dell’Unione europea\u003C/a\u003E stabilisce un approccio basato sul rischio che impone alle organizzazioni di classificare i sistemi di AI, implementare controlli e produrre documentazione pronta per l’audit per i casi d’uso a rischio più elevato. La norma ISO/IEC 42001 integra questo approccio fornendo uno standard per i sistemi di gestione dell’AI, finalizzato a definire, mantenere e migliorare le pratiche di governance. Sebbene l’adozione sia volontaria, sempre più organizzazioni scelgono di utilizzare la norma per allinearsi all’AI Act dell’Unione europea.\u003C/p\u003E\r\n\u003Cp\u003ELa regolamentazione, tuttavia, non è l’unico motivo per cui la governance è importante. I sistemi di AI possono produrre risultati discriminatori, divulgare dati sensibili, generare affermazioni prive di fondamento o automatizzare decisioni senza un’adeguata supervisione umana. Questi problemi possono creare rischi reputazionali e ridurre la fiducia di clienti, dipendenti e partner nei workflow basati sull’AI.\u003C/p\u003E\r\n\u003Cp\u003EJennifer Belissent, Principal Data Strategist Snowflake, \u003Ca href=\"https://www.snowflake.com/it/blog/ai-governance-customer-trust/\"\u003Edescrive così la realtà commerciale\u003C/a\u003E: “Il vero motore della governance dell’AI e dei dati è già in funzione. Sono i clienti a richiederla. Implementare controlli su sicurezza e privacy dei dati e governare l’utilizzo dell’AI è una questione di reputazione tanto quanto di conformità normativa”.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_why-ai-governance-matters-now_0":{"id":"quote-item-de1b3756c7","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"Il vero motore della governance dell’AI e dei dati è già in funzione. Sono i clienti a richiederla.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_why-ai-governance-matters-now_1":{"id":"text-0bcdadef27","text":"\u003Cp\u003EL’assurance dell’AI sta diventando uno dei criteri con cui le aziende valutano la propria maturità nell’AI. La questione non è se un’organizzazione disponga di una dichiarazione sull’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EAI responsabile\u003C/a\u003E, ma se possa dimostrare quali sistemi esistono, quali controlli si applicano, quali rischi sono stati accettati, quali risultati sono stati esaminati e quali incidenti sono stati segnalati e hanno insegnato qualcosa.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_why-ai-governance-matters-now_0":{"id":"card-v2-26815fb4e7","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: Town of Gilbert"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/town-of-gilbert/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--83f793a0-df9c-45fe-8949-f4e33942489a/gilbert%25403x.png?quality=85&preferwebp=true","alt":"Town of Gilbert Logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELa Town of Gilbert ha lanciato 11 dashboard pubbliche con Snowflake per migliorare la trasparenza di servizi come la prevenzione della criminalità, l’intervento sulla salute mentale, la riparazione delle buche stradali e il riciclo. L’amministrazione di Gilbert utilizza funzionalità di governance e sicurezza come il controllo degli accessi basato sui ruoli, i campi di metadati e il mascheramento dinamico dei dati per supportare un accesso affidabile ai dati sensibili del settore pubblico.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001":{"id":"title-v2-3e51f28a14","additionalClasses":"anchor-title anchor-title--key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001","type":"heading2","lines":["Principali framework di governance dell’AI: AI Act dell’Unione europea, AI RMF del NIST e norma ISO 42001"],":type":"snowflake-site/components/title-v2"},"text_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001_0":{"id":"text-8de8550431","text":"\u003Cp\u003EI \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/framework/\"\u003Eframework di governance dell’AI\u003C/a\u003E forniscono alle organizzazioni un linguaggio comune per classificare i rischi, definire i controlli e produrre evidenze. Nessun singolo framework copre ogni requisito aziendale, ma tre riferimenti sono oggi alla base di molte discussioni sulla governance:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EL’\u003Ca target=\"_blank\" href=\"https://artificialintelligenceact.eu/\" rel=\"noopener noreferrer\"\u003EAI Act dell’Unione europea\u003C/a\u003E è un framework giuridico vincolante per i sistemi di AI immessi o utilizzati sul mercato dell’UE. Utilizza la classificazione del rischio per distinguere pratiche vietate, sistemi di AI ad alto rischio, obblighi di trasparenza e requisiti per i modelli di AI per finalità generali. Per le aziende, l’AI Act è rilevante anche al di fuori dell’Europa, perché le sue categorie offrono ai team legali, di gestione del rischio e tecnologici un metodo concreto per classificare i casi d’uso di AI, associare gli obblighi e predisporre, ove richiesto, le evidenze per la valutazione di conformità.\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\" rel=\"noopener noreferrer\"\u003EL’AI Risk Management Framework (AI RMF) del National Institute of Standards and Technology (NIST)\u003C/a\u003E statunitense è un framework volontario che consente alle organizzazioni che progettano, sviluppano, distribuiscono o utilizzano l’AI di gestire i rischi e promuovere un’AI affidabile. Il NIST organizza il framework intorno a funzioni come Govern, Map, Measure e Manage. Ciò lo rende utile alle aziende che sviluppano programmi interfunzionali per la gestione dei rischi dell’AI, anche quando non sono soggette a un’unica legge specifica sull’AI.\u003C/li\u003E\r\n\u003Cli\u003ELa norma \u003Ca target=\"_blank\" href=\"https://www.iso.org/standard/42001\" rel=\"noopener noreferrer\"\u003EISO/IEC 42001\u003C/a\u003E è uno standard internazionale per i sistemi di gestione dell’AI. Definisce i requisiti per istituire, implementare, mantenere e migliorare continuamente un sistema di gestione dell’AI, risultando rilevante per le organizzazioni che desiderano una struttura di governance certificabile per lo sviluppo e l’utilizzo dell’AI.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESono rilevanti anche altri riferimenti globali. I \u003Ca target=\"_blank\" href=\"https://www.oecd.org/en/topics/ai-principles.html\" rel=\"noopener noreferrer\"\u003EPrincipi dell’OCSE sull’AI\u003C/a\u003E promuovono un’AI affidabile, rispettosa dei diritti umani e a sostegno dei valori democratici, mentre un \u003Ca target=\"_blank\" href=\"https://assets.publishing.service.gov.uk/media/65c1e399c43191000d1a45f4/a-pro-innovation-approach-to-ai-regulation-amended-governement-response-web-ready.pdf\" rel=\"noopener noreferrer\"\u003Ewhite paper del Dipartimento per la scienza, l’innovazione e la tecnologia del Regno Unito\u003C/a\u003E delinea un approccio alla regolamentazione dell’AI favorevole all’innovazione e basato sul contesto. In Italia, l’uso dell’intelligenza artificiale è disciplinato dalla \u003Ca href=\"https://www.normattiva.it/atto/caricaDettaglioAtto?atto.dataPubblicazioneGazzetta=2025-09-25&atto.codiceRedazionale=25G00143&atto.articolo.numero=0&atto.articolo.sottoArticolo=1&atto.articolo.sottoArticolo1=0&qId=bfae8708-a0ef-4637-9031-8eb93a15df02&tabID=0.5104177231527228&title=lbl.dettaglioAtto\" target=\"_blank\"\u003Elegge 132/2025 “Disposizioni e deleghe al Governo in materia di intelligenza artificiale”.\u003C/a\u003E L’\u003Ca href=\"https://www.agid.gov.it/it/ambiti-intervento/intelligenza-artificiale\" target=\"_blank\"\u003EAgenzia per l'Italia Digitale (AgID)\u003C/a\u003E cura la redazione di Linee guida e strategie per l’adozione dell’AI da parte delle aziende e della Pubblica Amministrazione. Nel complesso, questi riferimenti indicano lo stesso requisito operativo: le organizzazioni necessitano di meccanismi di governance in grado di classificare i rischi, assegnare le responsabilità, applicare i controlli e produrre evidenze man mano che i sistemi di AI cambiano.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EGuarda una conversazione sull’etica e sulla regolamentazione dell’AI generativa:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001_0":{"id":"embed-efc4017b65","youtubeVideoId":"DPs8nz59kss","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"},"title_key-components-of-ai-governance":{"id":"title-v2-9afd14488b","additionalClasses":"anchor-title anchor-title--key-components-of-ai-governance","type":"heading2","lines":["Componenti chiave della governance dell’AI"],":type":"snowflake-site/components/title-v2"},"text_key-components-of-ai-governance_0":{"id":"text-e6c94127c2","text":"\u003Cp\u003EUna governance efficace dell’AI si basa su un insieme di componenti interconnessi che garantiscono lo sviluppo e l’utilizzo responsabile e sicuro dei sistemi di AI, in linea con gli obiettivi dell’organizzazione. I seguenti componenti rappresentano una sintesi dei principali framework di governance dell’AI:\u003C/p\u003E\r\n\u003Ch3\u003EStrategia e supervisione\u003C/h3\u003E\r\n\u003Cp\u003ELa governance dell’AI parte da una direzione strategica chiara e da responsabilità definite. Le organizzazioni necessitano di strutture di leadership, policy e processi decisionali definiti per guidare l’adozione e la gestione dell’AI. In questo modo, le iniziative di AI si allineano agli obiettivi aziendali e alla propensione al rischio.\u003C/p\u003E\r\n\u003Ch3\u003EGestione del rischio\u003C/h3\u003E\r\n\u003Cp\u003EL’AI introduce diversi rischi, tra cui \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ebias algoritmico\u003C/a\u003E, vulnerabilità nella \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003Esicurezza dell’AI\u003C/a\u003E e conseguenze indesiderate. Un solido framework di governance identifica, valuta e mitiga questi rischi durante l’intero ciclo di vita dell’AI, attraverso processi di monitoraggio continuo e risposta agli incidenti.\u003C/p\u003E\r\n\u003Ch3\u003EData governance\u003C/h3\u003E\r\n\u003Cp\u003EPoiché i sistemi AI dipendono in larga misura dai dati, la governance deve garantirne l’accuratezza, la sicurezza e l’uso responsabile. Ciò comprende la gestione della qualità dei dati, della privacy e dei controlli degli accessi, nonché il mantenimento di una data lineage chiara.\u003C/p\u003E\r\n\u003Ch3\u003EGovernance dei modelli e gestione del ciclo di vita\u003C/h3\u003E\r\n\u003Cp\u003ELa governance dei modelli riguarda le modalità di sviluppo, test, deployment e manutenzione dei modelli AI. Comprende standard per convalida, documentazione, controllo delle versioni e monitoraggio continuo, affinché i modelli rimangano affidabili e idonei allo scopo nel tempo.\u003C/p\u003E\r\n\u003Ch3\u003EEtica e AI responsabile\u003C/h3\u003E\r\n\u003Cp\u003EOltre a garantire le prestazioni tecniche, i sistemi AI devono rispettare principi etici. I framework di governance affrontano equità, trasparenza, explainability e supervisione umana per ridurre i danni e allineare i risultati ai valori sociali e organizzativi.\u003C/p\u003E\r\n\u003Ch3\u003ECompliance e controlli legali\u003C/h3\u003E\r\n\u003Cp\u003ELe organizzazioni devono garantire che i propri sistemi AI rispettino le leggi e le normative applicabili. Ciò comprende la conservazione degli audit trail, il rispetto dei requisiti di documentazione, la gestione dei rischi associati a terze parti e la valutazione di questioni come responsabilità e proprietà intellettuale.\u003C/p\u003E\r\n\u003Ch3\u003ESicurezza e robustezza\u003C/h3\u003E\r\n\u003Cp\u003EI sistemi AI devono essere protetti da uso improprio, manipolazioni e attacchi. La governance comprende misure di protezione per dati e modelli, resilienza alle minacce avversarie e controlli per impedire accessi non autorizzati o usi dannosi.\u003C/p\u003E\r\n\u003Ch3\u003ETrasparenza e accountability\u003C/h3\u003E\r\n\u003Cp\u003EUna documentazione chiara e la tracciabilità sono essenziali per creare fiducia. Le organizzazioni devono conservare informazioni su come i sistemi AI sono progettati e gestiti, fornire spiegazioni adeguate dei risultati e definire le responsabilità in caso di problemi.\u003C/p\u003E\r\n\u003Ch3\u003EAttività operative e monitoraggio\u003C/h3\u003E\r\n\u003Cp\u003ELa governance dell’AI prosegue oltre il deployment. Il monitoraggio continuo garantisce che i sistemi funzionino come previsto, mentre cicli di feedback, audit e processi di gestione degli incidenti consentono alle organizzazioni di risolvere i problemi e migliorare nel tempo.\u003C/p\u003E\r\n\u003Ch3\u003ECultura, training e gestione del cambiamento\u003C/h3\u003E\r\n\u003Cp\u003EIl successo della governance dipende tanto dalle persone quanto dai processi. Le organizzazioni devono investire nel training, promuovere pratiche di AI responsabile e favorire una cultura in cui le considerazioni etiche e la consapevolezza dei rischi siano integrate nei processi decisionali quotidiani.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_key-components-of-ai-governance_0":{"id":"text-bb06bec639","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cb\u003EL’ERRORE COMUNE\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EImplementare la governance come insieme di controlli scollegati tra loro a dati, modelli, prompt e app spesso crea lacune nella visibilità e nell’accountability, rendendo difficile tracciare il comportamento dei sistemi AI o applicare le policy in modo coerente negli ambienti di produzione.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_responsible-ai-principles-that-operationalize-governance":{"id":"title-v2-f31cd66ac5","additionalClasses":"anchor-title anchor-title--responsible-ai-principles-that-operationalize-governance","type":"heading2","lines":["AI responsabile: principi che rendono operativa la governance"],":type":"snowflake-site/components/title-v2"},"text_responsible-ai-principles-that-operationalize-governance_0":{"id":"text-2664beb852","text":"\u003Cp\u003EL’AI responsabile definisce ciò che un’organizzazione intende tutelare quando l’AI entra nei processi aziendali: equità dei risultati, trasparenza sul comportamento del sistema, accountability delle decisioni, privacy delle persone interessate, sicurezza in condizioni previste e impreviste e inclusione dei gruppi che il sistema può coinvolgere.\u003C/p\u003E\n\u003Cp\u003EQuesti principi richiedono meccanismi operativi che li traducano in pratica. Un principio di equità si traduce in un audit dei bias con soglie documentate. Un principio di privacy richiede minimizzazione dei dati, mascheramento, limiti di conservazione e controlli degli accessi. Un principio di supervisione umana si traduce in un workflow di revisione che stabilisce quando una persona deve approvare, annullare o esaminare una raccomandazione generata dall’AI.\u003C/p\u003E\n\u003Cp\u003ELa governance dell’AI fornisce una struttura all’AI responsabile. Collega i principi a responsabili, artefatti e controlli. L’obiettivo non è rallentare ogni progetto AI applicando lo stesso processo, ma garantire che i sistemi a rischio più elevato siano sottoposti al livello di verifica, documentazione e monitoraggio richiesto dal loro impatto.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-transparency-and-explainability":{"id":"title-v2-42374b0d61","additionalClasses":"anchor-title anchor-title--ai-transparency-and-explainability","type":"heading2","lines":["Trasparenza ed explainability dell’AI"],":type":"snowflake-site/components/title-v2"},"text_ai-transparency-and-explainability_0":{"id":"text-59826689b8","text":"\u003Cp\u003ETrasparenza ed explainability rispondono a domande diverse, anche se spesso vengono considerate insieme. La \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etrasparenza dell’AI\u003C/a\u003E riguarda ciò che gli stakeholder possono conoscere del sistema: chi ne è responsabile, quale scopo persegue, quali fonti di dati utilizza, quali controlli si applicano e quali limitazioni sono state documentate. L’explainability riguarda il motivo per cui si è verificata una specifica previsione, classificazione, raccomandazione o generazione di output.\u003C/p\u003E\r\n\u003Cp\u003EUn sistema AI trasparente può rendere disponibili la propria scheda del modello, la documentazione, la cronologia delle versioni, lo stato di approvazione e le limitazioni note. Un sistema spiegabile può mostrare quali variabili hanno influenzato una previsione del rischio di credito o quali caratteristiche hanno contribuito maggiormente a un punteggio di abbandono. Per i sistemi di AI generativa, può anche mostrare quali documenti recuperati hanno supportato una risposta generata come evidenza di grounding.\u003C/p\u003E\r\n\u003Cp\u003EI metodi tecnici di explainability come \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://shap.readthedocs.io/en/latest/\"\u003ESHAP\u003C/a\u003E e \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://c3.ai/glossary/data-science/lime-local-interpretable-model-agnostic-explanations/\"\u003ELIME\u003C/a\u003E consentono ai team di interpretare determinati output dei modelli, in particolare nei contesti di ML tabellare, benché siano meno applicabili ai \u003Ca href=\"https://www.snowflake.com/it/fundamentals/large-language-model/\"\u003Emodelli linguistici di grandi dimensioni\u003C/a\u003E. Tuttavia, l’explainability non è soltanto una tecnica applicata ai modelli. Per l’AI generativa, può richiedere anche evidenze di grounding RAG, documenti sorgente citati, log di prompt e risposte, spiegazioni post hoc, indicatori del livello di confidenza degli output e una documentazione chiara dei casi in cui il sistema non deve essere utilizzato.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-traceability-and-auditability":{"id":"title-v2-aea89b3898","additionalClasses":"anchor-title anchor-title--ai-traceability-and-auditability","type":"heading2","lines":["Tracciabilità e auditabilità dell’AI"],":type":"snowflake-site/components/title-v2"},"text_ai-traceability-and-auditability_0":{"id":"text-74c8f7f3b5","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ELa tracciabilità dell’AI\u003C/a\u003E collega un output AI agli asset e alle azioni che lo hanno prodotto. In un sistema ML tradizionale, può includere il data lineage, i dati di addestramento, le trasformazioni delle feature, la versione del modello, i risultati della convalida e il momento del deployment. In un’applicazione di AI generativa, può includere anche il prompt di sistema, il prompt dell’utente, il contesto recuperato, il fornitore del modello, la configurazione dell’inferenza, le chiamate agli strumenti e la risposta generata. In un agente AI, la tracciabilità può estendersi allo stato della memoria, alla cronologia delle azioni e ai sistemi esterni a cui l’agente ha avuto accesso.\u003C/p\u003E\r\n\u003Cp\u003EL’auditabilità è la capacità di un revisore interno o indipendente di verificare che il sistema abbia funzionato come previsto e che siano stati applicati i controlli richiesti. Dipende da audit trail, log immutabili ove appropriato, documentazione delle schede dei modelli e dei modelli, registri delle segnalazioni degli incidenti AI, cronologia degli accessi e prove dell’applicazione dei controlli verificabili a posteriori.\u003C/p\u003E\r\n\u003Cp\u003EQueste funzionalità costituiscono la base per gli audit di compliance dell’AI. Un team di governance non può valutare in modo credibile se un sistema sia stato conforme alla policy se non è in grado di ricostruire quali dati sono stati utilizzati, quale versione del modello o del prompt era attiva, quale utente ha richiesto l’output e quale azione ne è seguita. Per questo motivo, la tracciabilità dell’AI richiede spesso sia il data lineage sia il model lineage, oltre a log a livello applicativo che acquisiscano prompt, contesto recuperato e utilizzo degli strumenti.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_training-data-governance-and-bias-auditing":{"id":"title-v2-b01962eb43","additionalClasses":"anchor-title anchor-title--training-data-governance-and-bias-auditing","type":"heading2","lines":["Governance dei dati di addestramento e audit dei bias"],":type":"snowflake-site/components/title-v2"},"text_training-data-governance-and-bias-auditing_0":{"id":"text-4617488b40","text":"\u003Cp\u003ELa governance dell’AI inizia dai dati utilizzati per addestrare, perfezionare, ancorare o valutare il modello. Un modello addestrato su dati scarsamente documentati può riprodurre bias storici, esporre attributi sensibili, generare output inaffidabili o non funzionare se applicato a una popolazione non rappresentata nei dati originali. In un sistema RAG, il modello potrebbe non essere addestrato su documenti aziendali, ma al contenuto recuperato si applicano comunque le stesse domande di governance: da dove proviene, chi ne è responsabile, quanto è aggiornato, quali policy di accesso si applicano e se è appropriato per la richiesta dell’utente.\u003C/p\u003E\n\u003Cp\u003ELa governance dei dati di addestramento riguarda provenienza, consenso, licenze, qualità, conservazione, accesso, attributi demografici, minimizzazione dei dati e usi approvati. Per la governance dei dati degli LLM, l’ambito può includere anche le informative sui dati di pre-addestramento, i dati di fine‑tuning, gli embedding, i log dei prompt e i dati di valutazione. La governance dei dati sintetici aggiunge un ulteriore livello: come sono stati generati, se mantengono l’utilità statistica, se lasciano trapelare dati sorgente e per quali casi d’uso ne è approvato l’impiego.\u003C/p\u003E\n\u003Cp\u003EL’audit dei bias verifica se un sistema AI produce disparità inaccettabili tra classi protette, attributi demografici o altri gruppi rilevanti. Questa attività dipende da definizioni chiare, dati rappresentativi e una gestione attenta degli attributi sensibili. In alcuni casi, i dati demografici possono essere necessari per verificare la presenza di bias. In altri, la raccolta o l’utilizzo di tali dati può creare rischi per la privacy e la compliance.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_generative-ai-and-ai-agent-governance":{"id":"title-v2-37e21555a2","additionalClasses":"anchor-title anchor-title--generative-ai-and-ai-agent-governance","type":"heading2","lines":["Governance dell’AI generativa e degli agenti AI"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-and-ai-agent-governance_0":{"id":"text-38ab0bbb38","text":"\u003Cp\u003EL’AI generativa e gli agenti AI ampliano la superficie soggetta a governance perché introducono nuovi artefatti e comportamenti che i precedenti programmi di governance dei modelli non erano concepiti per gestire. Un modello di classificazione restituisce un punteggio o una categoria. Un’applicazione di AI generativa può produrre un paragrafo, riassumere un documento, scrivere codice, generare un’immagine o rispondere a una domanda aziendale in base al contesto recuperato. Un agente AI può spingersi oltre, selezionando strumenti, elaborando piani, archiviando informazioni in memoria e intraprendendo azioni in più sistemi.\u003C/p\u003E\r\n\u003Ch3\u003EGovernance dell’AI generativa\u003C/h3\u003E\r\n\u003Cp\u003ELa governance dell’AI generativa riguarda dati, prompt, modelli, pipeline di retrieval, output e informative che determinano i contenuti generati. La governance dei prompt stabilisce chi può creare, modificare e approvare i prompt di sistema, soprattutto quando codificano una policy, comportamenti associati ai ruoli o vincoli di accesso. La governance degli LLM monitora quale modello foundation viene utilizzato, dove viene eseguito, quali dati vengono condivisi con il modello, come vengono filtrati gli output e quali obblighi contrattuali o di compliance si applicano.\u003C/p\u003E\r\n\u003Cp\u003ELa governance RAG è particolarmente importante per i casi d’uso aziendali, perché le risposte generate dipendono spesso da documenti, tabelle o voci di knowledge base recuperati. Un sistema RAG governato richiede metadati sulla provenienza, controlli degli accessi, verifiche dell’aggiornamento, citazioni delle fonti e metriche di valutazione che misurino la pertinenza delle risposte e la loro aderenza alle fonti. Senza questi controlli, il sistema può generare risposte formulate con sicurezza sulla base di contenuti obsoleti, soggetti a restrizioni o non adeguatamente compresi.\u003C/p\u003E\r\n\u003Cp\u003ELe informative sull’AI e la trasparenza dell’AI generativa stabiliscono quando utenti, clienti o dipendenti devono sapere che interagiscono con l’AI o ricevono contenuti generati dall’AI. La governance dei contenuti può includere anche standard di provenienza come \u003Ca target=\"_blank\" href=\"https://c2pa.org/\" rel=\"noopener noreferrer\"\u003EC2PA\u003C/a\u003E, la revisione umana degli output ad alto impatto, il watermarking ove appropriato e policy per i materiali generati dall’AI pubblicati esternamente.\u003C/p\u003E\r\n\u003Ch3\u003EGovernance degli agenti AI\u003C/h3\u003E\r\n\u003Cp\u003ELa governance degli agenti AI aggiunge la supervisione dell’uso degli strumenti, controlli sulla memoria degli agenti e regole per il coordinamento multiagente. Un agente AI di supporto che redige una risposta presenta un determinato profilo di rischio, mentre un agente che può emettere rimborsi, aggiornare i record degli account o eseguire query su sistemi interni sensibili ne presenta un altro. La governance deve definire quali strumenti può richiamare l’agente, quali autorizzazioni eredita, quali azioni richiedono approvazione e come vengono registrate le eccezioni.\u003C/p\u003E\r\n\u003Cp\u003ELa memoria dell’agente costituisce un ulteriore oggetto di governance. Se un agente archivia preferenze degli utenti, contesto del progetto o decisioni precedenti, i team devono definire regole per conservazione, eliminazione, accesso e privacy, nonché stabilire se la memoria possa influenzare le azioni future. I sistemi multiagente aggiungono rischi di coordinamento: un agente può recuperare i dati, un altro può elaborarli e un altro ancora può intraprendere un’azione. L’audit trail deve mostrare come sono collegati questi passaggi.\u003C/p\u003E\r\n\u003Cp\u003EIn pratica, la questione di governance è se l’organizzazione possa limitare il comportamento dell’agente senza affidarsi esclusivamente al modello. Guardrail, autorizzazioni, policy sui dati, approvazioni dei workflow, log di tracciamento e segnalazione degli incidenti contribuiscono a trasformare l’Agentic AI da workflow opaco a sistema controllato.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text":{"id":"text-51ada1d7d8","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECONSIGLIO RAPIDO\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003EMaggiore è l’autonomia di un agente AI, in termini di memoria, accesso agli strumenti o capacità di eseguire azioni, maggiori sono i controlli di governance necessari per autorizzazioni, approvazioni e audit trail.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_model-governance-model-risk-and-alignment":{"id":"title-v2-222f10e255","additionalClasses":"anchor-title anchor-title--model-governance-model-risk-and-alignment","type":"heading2","lines":["Governance dei modelli, rischio di modello e allineamento"],":type":"snowflake-site/components/title-v2"},"text_model-governance-model-risk-and-alignment_0":{"id":"text-0e99baa60e","text":"\u003Cp\u003ELa governance dei modelli gestisce ogni modello come un asset aziendale. In genere comprende registrazione dei modelli, schede dei modelli, documentazione, convalida, workflow di approvazione, monitoraggio, ritiro e responsabilità. Un Model Registry mostra quali modelli esistono e dove sono distribuiti. Una scheda del modello documenta l’uso previsto, i risultati delle valutazioni, le limitazioni, le considerazioni etiche e le condizioni in cui il modello non deve essere utilizzato.\u003C/p\u003E\r\n\u003Cp\u003EIl rischio di modello è il rischio che un modello produca output errati, inappropriati o dannosi oppure che sia utilizzato al di fuori del contesto previsto. Le organizzazioni di servizi finanziari considerano da tempo la gestione del rischio di modello una disciplina formale. La SR 11-7, linea guida statunitense di lunga data per il settore bancario sulla gestione del rischio di modello, poneva l’accento su governance, convalida, controlli e supervisione. Nell’aprile 2026, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf\"\u003Ela Federal Reserve\u003C/a\u003E ha pubblicato linee guida aggiornate volte a riflettere un approccio maggiormente basato sul rischio. In Italia e nell’UE, i principali riferimenti per la gestione del rischio di modello includono la \u003Ca href=\"https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.supervisory_guide_egim_202606.en.pdf\"\u003EECB Guide to Internal Models\u003C/a\u003E aggiornata di giugno 2026, le linee guida della European Banking Authority (EBA) sulla governance interna, il quadro normativo CRR/CRD e le disposizioni di vigilanza della Banca d’Italia, con requisiti relativi a governance, validazione, monitoraggio e controllo dei modelli.\u003C/p\u003E\r\n\u003Cp\u003ELa governance dell’AI riprende i principi della gestione del rischio di modello, estendendoli a un insieme più ampio di sistemi. Un modello challenger può essere utilizzato per confrontare le prestazioni con quelle di un modello in produzione. Il monitoraggio può rilevare deriva, accuratezza, latenza, costi, equità o percentuali di output non sicuri. La revisione dell’AI responsabile può verificare se il caso d’uso è in linea con i principi dell’organizzazione, gli obblighi legali e le aspettative degli stakeholder.\u003C/p\u003E\r\n\u003Cp\u003EL’allineamento dei modelli aggiunge un ulteriore livello, soprattutto per l’AI generativa. L’allineamento verifica se un modello o un’applicazione AI si comporta in modo coerente con gli obiettivi previsti, le policy e i valori umani. Tecniche come l’AI costituzionale possono orientare il comportamento del modello durante l’addestramento o il tuning, ma l’allineamento aziendale dipende anche dai controlli circostanti: casi d’uso approvati, revisione umana, guardrail, infrastruttura di valutazione e percorsi di escalation chiari in caso di comportamento imprevisto del sistema.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-governance-and-compliance":{"id":"title-v2-1759bdf423","additionalClasses":"anchor-title anchor-title--ai-governance-and-compliance","type":"heading2","lines":["Governance dell’AI e conformità"],":type":"snowflake-site/components/title-v2"},"text_ai-governance-and-compliance_0":{"id":"text-867c267862","text":"\u003Cp\u003ELa conformità dell’AI trasforma i controlli di governance in evidenze verificabili. Una policy può stabilire che i sistemi ad alto rischio richiedano la supervisione umana, ma un’autorità di regolamentazione o un cliente potrebbe richiedere il workflow di approvazione, i nomi di chi ha effettuato la revisione, le date delle revisioni, la cronologia dei problemi e la prova che il workflow sia stato applicato prima della distribuzione. La differenza tra una policy e un programma di compliance consiste nella capacità di dimostrare che la policy è stata applicata nella pratica.\u003C/p\u003E\r\n\u003Cp\u003ELa mappatura dei controlli collega le attività di governance dell’AI a requisiti specifici derivanti da leggi, standard e policy interne. Un singolo controllo, ad esempio la gestione di un inventario dei modelli, può supportare l’AI Act dell’Unione europea, la norma ISO/IEC 42001, la gestione interna dei rischi e i questionari di garanzia dei clienti. Un singolo artefatto, come una scheda del modello o una valutazione d’impatto dell’AI, può supportare trasparenza, responsabilità, revisione dei rischi ed evidenze di audit.\u003C/p\u003E\r\n\u003Cp\u003ELa documentazione destinata alle autorità di regolamentazione deve essere accurata, aggiornata e riferita al sistema effettivo. Deve includere lo scopo previsto del sistema AI, le fonti dei dati, la classificazione del rischio, i risultati delle valutazioni, il modello di supervisione umana, il piano di monitoraggio, il processo di segnalazione degli incidenti e la cronologia delle modifiche. Per l’AI generativa e i sistemi agentici, la documentazione deve includere anche prompt, grounding RAG, utilizzo degli strumenti, guardrail, provenienza dei contenuti e, ove pertinente, revisione degli output.\u003C/p\u003E\r\n\u003Cp\u003EI programmi di governance dell’AI spesso si arrestano quando le attività di compliance iniziano dopo la distribuzione. Un approccio più duraturo integra documentazione, monitoraggio ed evidenze dei controlli nella pipeline di governance, affinché ogni caso d’uso dell’AI approvato produca i registri necessari per esaminarlo, sottoporlo ad audit e migliorarlo.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-to-implement-ai-governance":{"id":"title-v2-17d2ad0673","additionalClasses":"anchor-title anchor-title--how-to-implement-ai-governance","type":"heading2","lines":["Come implementare la governance dell’AI"],":type":"snowflake-site/components/title-v2"},"text_how-to-implement-ai-governance_0":{"id":"text-1e254de1bb","text":"\u003Cp\u003ELa governance dell’AI è più efficace quando opera in prossimità dei sistemi che governa. Le regole definite dalle policy devono essere applicate nei registri dei modelli, nelle policy sui dati, nei controlli degli accessi, nei workflow di approvazione, nelle dashboard di monitoraggio e nei report sugli incidenti. In caso contrario, la governance diventa un processo parallelo che i team aggirano quando le attività legate all’AI avanzano rapidamente.\u003C/p\u003E\n\u003Cp\u003EUn’implementazione pratica inizia in genere con sei passaggi:\u003C/p\u003E\n\u003Col\u003E\n\u003Cli\u003E\u003Cstrong\u003EInventariare ogni modello e caso d’uso dell’AI:\u003C/strong\u003E Sviluppare un inventario dei modelli che includa ML tradizionale, applicazioni di AI generativa, AI integrata nelle soluzioni dei fornitori, sistemi RAG, agenti AI e sistemi sperimentali che potrebbero già influenzare le attività.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EClassificare in base al rischio:\u003C/strong\u003E Utilizzare un modello di classificazione per livelli di rischio che consideri impatto, autonomia, sensibilità dei dati, esposizione degli utenti e ambito normativo.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAssegnare responsabili e revisori formalmente incaricati:\u003C/strong\u003E Ogni sistema richiede un responsabile formalmente incaricato, un referente tecnico e un percorso di revisione.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EIntegrare i controlli dove dati e modello sono già in esecuzione:\u003C/strong\u003E I controlli sono più efficaci quando fanno parte del control plane per dati, modelli e applicazioni.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EDocumentare, monitorare e sottoporre ad audit continuo:\u003C/strong\u003E La documentazione dei modelli, le schede dei modelli, le valutazioni d’impatto dell’AI, la lineage, la cronologia degli accessi e gli audit trail devono aggiornarsi con l’evoluzione dei sistemi.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003ESegnalare gli incidenti e trarne insegnamento:\u003C/strong\u003E La segnalazione degli incidenti AI deve documentare che cosa è accaduto, chi ha subito conseguenze, quale controllo non ha funzionato o era assente e che cosa è cambiato in seguito.\u003C/li\u003E\n\u003C/ol\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_item":{"id":"quote-item-5e98a4bcf5","alignment":"left","layout":"simple","showQuoteIcon":true,"quote":"La governance dell’AI è più efficace quando opera in prossimità dei sistemi che governa.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_why-ai-governance-on-snowflake":{"id":"title-v2-bd9ea9edcf","additionalClasses":"anchor-title anchor-title--why-ai-governance-on-snowflake","type":"heading2","lines":["Perché la governance dell’AI su Snowflake"],":type":"snowflake-site/components/title-v2"},"text_why-ai-governance-on-snowflake_0":{"id":"text-92e64f166c","text":"\u003Cp\u003ELa governance dell’AI beneficia della prossimità ai dati governati. Se le policy sui dati risiedono in un sistema, lo sviluppo dei modelli in un altro, i prompt in un altro ancora e le applicazioni AI altrove, i team dedicano gran parte delle attività di governance a riconciliare gli inventari, copiare le evidenze e cercare di ricostruire gli eventi a posteriori.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake offre funzionalità che supportano la governance dell’AI dove risiedono già i dati governati.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon\"\u003ESnowflake Horizon Catalog\u003C/a\u003E  offre un livello unificato di governance per dati e AI, con funzionalità come controllo degli accessi, mascheramento dinamico dei dati, policy di accesso righe e classificazione dei dati.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://trust.snowflake.com\"\u003ESnowflake Compliance Center\u003C/a\u003E  aggiunge un livello di monitoraggio della postura di sicurezza degli account. Valuta gli account Snowflake rispetto alle raccomandazioni dello scanner, genera risultati quando rileva potenziali rischi per la sicurezza e fornisce indicazioni per la risoluzione.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/snowflake-cortex-ai-cortex-guard-llm-safeguards\"\u003ECortex Guard\u003C/a\u003E  offre un livello di sicurezza in fase di esecuzione per l’AI generativa, filtrando le risposte degli LLM potenzialmente non sicure o dannose quando viene attivato nelle funzioni Snowflake Cortex AI.\u003C/p\u003E\r\n\u003Cp\u003ELa certificazione ISO/IEC 42001 di Snowflake rappresenta un ulteriore indicatore di credibilità. La certificazione attesta la presenza di strutture di governance e supervisione per la gestione del ciclo di vita dell’AI, con particolare attenzione alla trasparenza e all’attribuzione delle responsabilità nello sviluppo e nell’utilizzo dell’AI.\u003C/p\u003E\r\n\u003Cp\u003EIn Italia, Snowflake possiede inoltre la \u003Ca href=\"https://www.acn.gov.it/portale/catalogo-delle-infrastrutture-digitali-e-dei-servizi-cloud?tipologia=100093&r=snowflake\" target=\"_blank\"\u003EQualificazione dell’Agenzia per la Cybersicurezza Nazionale (ACN) come servizio cloud di livello QC2\u003C/a\u003E.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_why-ai-governance-on-snowflake_0":{"id":"card-v2-266d496d81","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Customer story: Merkle"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Leggi il case study"},"image":{"id":"image","height":"351","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14562da5-7f82-4c21-b8ff-55054c4d6c0c/merkle%25403x.png?quality=85&preferwebp=true","alt":"Merkle logo","lazyEnabled":true,"isLcpImage":false,"width":"624",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EConsolidando dati di marketing sensibili, governance e flussi di collaborazione in Snowflake, Merkle ha migliorato del 64% il proprio ciclo di sviluppo dei dati e ottenuto risparmi sui costi fino al 20% (dato di marzo 2025). Il team ha inoltre creato una soluzione per la gestione delle risposte alle RFP con Document AI in Snowflake Cortex, riducendo l’inserimento manuale dei dati per almeno 25 membri del team.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_a-practical-path-to-safer-ai":{"id":"title-v2-a08bbc91c3","additionalClasses":"anchor-title anchor-title--a-practical-path-to-safer-ai","type":"heading2","lines":["Un percorso concreto verso un’AI più sicura"],":type":"snowflake-site/components/title-v2"},"text_a-practical-path-to-safer-ai_0":{"id":"text-5c9aceefe3","text":"\u003Cp\u003EPer le aziende, l’adozione dell’AI non può avvenire senza governance. La governance consente a un’organizzazione di comprendere che cosa fa un sistema AI, chi ne è responsabile, quali dati e modelli ne hanno determinato il comportamento e se sono stati applicati i controlli appropriati prima che un output entrasse a far parte di una decisione.\u003C/p\u003E\n\u003Cp\u003ELa governance diventa ancora più importante quando l’AI si estende ad app generative e agenti capaci di recuperare dati aziendali, generare contenuti, chiamare strumenti e influenzare i flussi di lavoro operativi.\u003C/p\u003E\n\u003Cp\u003EPer le aziende, questo è il valore concreto della governance dell’AI. Consente ai team di procedere più rapidamente con una visione più chiara dei rischi, utilizzare l’AI in ambiti in cui fiducia e responsabilità sono essenziali e mostrare a clienti, autorità di regolamentazione e stakeholder interni come vengono gestiti nel tempo i sistemi AI.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_a-practical-path-to-safer-ai_0":{"id":"text-83aa00970d","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EIN SINTESI\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ELa governance dell’AI consente alle aziende di utilizzare l’intelligenza artificiale in modo affidabile, allineando policy, controlli e responsabilità lungo l’intero ciclo di vita dei sistemi e garantendo che i diversi approcci all’AI (dai modelli tradizionali all’AI generativa e agli agenti) rimangano trasparenti, controllati e idonei all’utilizzo in contesti reali.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout_what-is-ai-governance_0","text_intro_0","title_what-is-ai-governance","text_what-is-ai-governance_0","title_why-ai-governance-matters-now","text_why-ai-governance-matters-now_0","quote_why-ai-governance-matters-now_0","text_why-ai-governance-matters-now_1","card_v2_why-ai-governance-matters-now_0","title_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001","text_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001_0","yt_key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001_0","title_key-components-of-ai-governance","text_key-components-of-ai-governance_0","callout_key-components-of-ai-governance_0","title_responsible-ai-principles-that-operationalize-governance","text_responsible-ai-principles-that-operationalize-governance_0","title_ai-transparency-and-explainability","text_ai-transparency-and-explainability_0","title_ai-traceability-and-auditability","text_ai-traceability-and-auditability_0","title_training-data-governance-and-bias-auditing","text_training-data-governance-and-bias-auditing_0","title_generative-ai-and-ai-agent-governance","text_generative-ai-and-ai-agent-governance_0","text","title_model-governance-model-risk-and-alignment","text_model-governance-model-risk-and-alignment_0","title_ai-governance-and-compliance","text_ai-governance-and-compliance_0","title_how-to-implement-ai-governance","text_how-to-implement-ai-governance_0","quote_item","title_why-ai-governance-on-snowflake","text_why-ai-governance-on-snowflake_0","card_v2_why-ai-governance-on-snowflake_0","title_a-practical-path-to-safer-ai","text_a-practical-path-to-safer-ai_0","callout_a-practical-path-to-safer-ai_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":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-f87ae396be",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-a93b20aa4c","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-3e47e3e605","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"what-is-ai-governance\"\u003EChe cos’è la governance dell’AI?\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-ai-governance-matters-now\"\u003EPerché oggi la governance dell’AI è fondamentale\u003C/li\u003E\r\n\u003Cli data-anchor=\"key-ai-governance-frameworks-eu-ai-act-nist-ai-rmf-and-iso-42001\"\u003EPrincipali framework di governance dell’AI: AI Act dell’Unione 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allineamento\u003C/li\u003E\r\n\u003Cli data-anchor=\"ai-governance-and-compliance\"\u003EGovernance dell’AI e conformità\u003C/li\u003E\r\n\u003Cli data-anchor=\"how-to-implement-ai-governance\"\u003ECome implementare la governance dell’AI\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-ai-governance-on-snowflake\"\u003EPerché la governance dell’AI su Snowflake\u003C/li\u003E\r\n\u003Cli data-anchor=\"a-practical-path-to-safer-ai\"\u003EUn percorso concreto verso un’AI più sicura\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small 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