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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-570328e9e4","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-2090740b3e",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-9ea37a19f9","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Compliance dell’AI: le normative e come renderle operative"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-1574151d72","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003ELa compliance dell’AI è una pratica operativa che collega ogni modello, assistente o workflow automatizzato alle leggi, ai controlli e alle prove applicabili. Questa guida illustra le principali normative e i principali framework per l’AI, quindi spiega come rendere operativa la compliance tramite inventari, livelli di rischio, documentazione, monitoraggio e prove pronte per gli audit.\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,"columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-1679c596a3",":type":"snowflake-site/components/container",":items":{"content_chip":{"id":"content-chip-512febaa09","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"800","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?preferwebp=true&quality=85","alt":"Laurie MacPherson","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_copy":{"id":"content-chip-55d6a2f5ed","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/it/blog/authors/david-gaule/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Leggi la bio"},"image":{"id":"image","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fd9454ea-3b59-4d19-95be-534774cbd226/david.jpg?preferwebp=true&quality=85","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","content_chip_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["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-bb830d3daf",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"youtube":{"id":"embed-a5a557f28b","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_731284311":{"id":"flexible-column-container-57dff018cf","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-88a537cfed",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text":{"id":"text-913b61e130","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_1902758988":{"id":"text-21e9023893","additionalClasses":"related-topics","text":"\u003Cul\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/iso-42001/\"\u003ENorma ISO 42001\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","text_1902758988"]},":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-c1b9748cff","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__0":{"id":"text-cb5dcbdd31","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003ECOMPLIANCE DELL’AI: LA DEFINIZIONE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELa compliance dell’AI è la pratica che rende verificabile la responsabilità dei sistemi AI, collegandone l’utilizzo a requisiti chiari, controlli documentati e registrazioni verificabili. Consente alle organizzazioni di dimostrare che l’AI opera nel rispetto delle prescrizioni legali, normative e organizzative.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-f54d95e421","text":"\u003Cp\u003ELa compliance dell’AI parte da una domanda apparentemente semplice: per questo modello, assistente, sistema di scoring o workflow automatizzato, quali regole si applicano e quali prove dimostrano che sono state rispettate?\u003C/p\u003E\n\u003Cp\u003ERispondere diventa sempre più difficile con l’impiego dell’AI nelle decisioni sul credito, nei workflow clinici, nella selezione del personale, nel rilevamento delle frodi, nell’assistenza clienti e negli strumenti interni per la produttività. Un modello può utilizzare dati di addestramento provenienti da un sistema, record dei clienti provenienti da un altro, output di un fornitore di foundation model e una fase di approvazione umana eseguita in un’applicazione separata. Ogni parte di questa catena può generare un obbligo normativo, un requisito previsto da una policy interna o una questione di audit a cui sarà necessario rispondere in seguito.\u003C/p\u003E\n\u003Cp\u003EI programmi di compliance dell’AI consentono alle organizzazioni di gestire questa complessità prima che un audit o la revisione di un incidente impongano di affrontarla. Collegano leggi e standard ai controlli, associano tali controlli alle prove e mantengono aggiornate le prove quando cambiano modelli, dati, fornitori e casi d’uso.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-compliance":{"id":"title-v2-620a705bb2","additionalClasses":"anchor-title anchor-title--what-is-ai-compliance","type":"heading2","lines":["Che cos’è la compliance dell’AI?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-compliance_0":{"id":"text-d30bf0e89e","text":"\u003Cp\u003ELa compliance dell’AI è la disciplina che garantisce che i \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/\"\u003Esistemi AI\u003C/a\u003E, e il ciclo di vita utilizzato per svilupparli e gestirli, siano conformi alle leggi, alle normative, agli standard di settore e alle policy interne applicabili. Comprende i controlli utilizzati da un’organizzazione per dimostrare che un sistema AI è stato progettato, testato, distribuito, monitorato e sottoposto a revisione nel rispetto dei relativi obblighi normativi.\u003C/p\u003E\n\u003Cp\u003ELa compliance dell’AI è quindi correlata a diverse discipline affini, ma se ne distingue:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003Ela \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/\"\u003E\u003Cstrong\u003Egovernance dell’AI\u003C/strong\u003E\u003C/a\u003E definisce il modello operativo: chi è responsabile di un caso d’uso dell’AI, chi lo approva, chi può sospenderlo e come avviene l’escalation delle decisioni.\u003C/li\u003E\n\u003Cli\u003EL’\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-ethics/\"\u003E\u003Cstrong\u003Eetica dell’AI\u003C/strong\u003E\u003C/a\u003E definisce i principi: equità, trasparenza, responsabilità, privacy, sicurezza e supervisione umana.\u003C/li\u003E\n\u003Cli\u003ELa \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/ai-risk-management/\"\u003E\u003Cstrong\u003Egestione del rischio dell’AI\u003C/strong\u003E\u003C/a\u003E definisce la metodologia: come l’organizzazione identifica, misura, assegna priorità e mitiga i rischi legati all’AI.\u003C/li\u003E\n\u003Cli\u003ELa \u003Cstrong\u003Ecompliance dell’AI\u003C/strong\u003E dimostra la conformità: quali regole si applicano, quali controlli le soddisfano e quali prove di audit supportano l’attestazione dei controlli.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EUn programma di compliance dell’AI parte in genere da elementi concreti, non da principi astratti: un inventario dell’AI, una scheda del modello, un riepilogo dei dati di addestramento, una persona responsabile del sistema, una policy di accesso, una dashboard di monitoraggio e un registro delle approvazioni. Questi elementi dimostrano se un sistema è stato classificato correttamente, se l’AI ad alto rischio è stata sottoposta alla revisione appropriata, se la supervisione umana è effettiva e se il monitoraggio successivo alla distribuzione viene realmente eseguito.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-ai-regulatory-landscape":{"id":"title-v2-92824e1749","additionalClasses":"anchor-title anchor-title--the-ai-regulatory-landscape","type":"heading2","lines":["Il quadro normativo dell’AI"],":type":"snowflake-site/components/title-v2"},"text_the-ai-regulatory-landscape_0":{"id":"text-1a9dfa6814","text":"\u003Cp\u003ELe organizzazioni operano in genere in un contesto normativo articolato su più livelli: norme generali sull’AI, disposizioni sulla privacy, orientamenti di vigilanza settoriali, requisiti di sicurezza dei prodotti, diritto del lavoro, norme a tutela dei consumatori e policy interne sui rischi.\u003C/p\u003E\r\n\u003Ch3\u003EAI Act dell’Unione europea\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EL’AI Act dell’Unione europea\u003C/a\u003E è ampiamente considerato uno dei framework normativi specifici per l’AI più completi attualmente in vigore. Adotta una struttura basata su livelli di rischio che distingue tra pratiche di AI vietate, sistemi di AI ad alto rischio, sistemi a rischio limitato, modelli di AI per finalità generali e utilizzi a rischio inferiore. L’AI Act ha inoltre portata extraterritoriale: può applicarsi a fornitori e deployer al di fuori dell’UE quando i sistemi di AI o i relativi output sono immessi sul mercato dell’UE o utilizzati al suo interno.\u003C/p\u003E\r\n\u003Cp\u003EL’AI Act si applica in più fasi: i divieti e i requisiti di alfabetizzazione in materia di AI sono entrati in vigore a febbraio 2025, gli obblighi per l’AI per finalità generali ad agosto 2025, mentre l’attuazione più ampia proseguirà fino al 2 agosto 2027.\u003C/p\u003E\r\n\u003Cp\u003EPer i sistemi di AI ad alto rischio, la compliance può richiedere gestione del rischio, \u003Ca href=\"https://www.snowflake.com/it/data-governance/\"\u003Egovernance dei dati\u003C/a\u003E, documentazione tecnica, registrazione degli eventi, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etrasparenza\u003C/a\u003E, sorveglianza umana, accuratezza, robustezza, cybersecurity e, in alcuni casi, una valutazione della conformità con il coinvolgimento di un organismo notificato.\u003C/p\u003E\r\n\u003Cp\u003ELe organizzazioni che sviluppano o forniscono sistemi di AI ad alto rischio devono in genere predisporre un fascicolo tecnico che documenti come il sistema è stato progettato, testato e descritto. Le organizzazioni che utilizzano questi sistemi devono in genere fornire prove che dimostrino come il sistema viene implementato, chi ne esamina gli output, come viene monitorato e come vengono inoltrate le segnalazioni dei problemi.\u003C/p\u003E\r\n\u003Ch3\u003ERegolamento generale sulla protezione dei dati (GDPR)\u003C/h3\u003E\r\n\u003Cp\u003EIl \u003Ca target=\"_blank\" href=\"https://gdpr.eu/\" rel=\"noopener noreferrer\"\u003EGDPR\u003C/a\u003E (Regolamento Generale sulla Protezione dei Dati, RGPD) si applica già a molti sistemi di AI che trattano dati personali, anche quando tali sistemi non sono disciplinati da una normativa autonoma sull’AI. L’articolo 22 disciplina determinati diritti relativi ai processi decisionali automatizzati, compresa la profilazione, quando le decisioni producono effetti giuridici o incidono in modo analogo significativamente sulle persone. L’articolo 35 richiede una valutazione d’impatto sulla protezione dei dati (DPIA) quando il trattamento può presentare un rischio elevato per le persone, ad esempio in caso di profilazione su larga scala, trattamento di dati sensibili o uso di nuove tecnologie.\u003C/p\u003E\r\n\u003Cp\u003EIn pratica, la \u003Ca href=\"https://www.snowflake.com/it/data-governance/regulatory-compliance/gdpr/\"\u003Econformità al GDPR\u003C/a\u003E spesso affianca quella specifica per l’AI. Una banca che usa l’intelligenza artificiale per le decisioni creditizie può dover valutare il caso d’uso in base alle disposizioni dell’AI Act dell’Unione europea sui sistemi ad alto rischio e, al contempo, completare una DPIA, documentare la base giuridica, applicare controlli degli accessi e dimostrare che i dati personali sono stati trattati nel rispetto dei requisiti di limitazione delle finalità, minimizzazione e conservazione.\u003C/p\u003E\r\n\u003Ch3\u003ENorme federali e statali degli Stati Uniti\u003C/h3\u003E\r\n\u003Cp\u003EL’AI Risk Management Framework La compliance dell’AI resta un mosaico di leggi e regolamenti. A livello federale, l’indirizzo politico è cambiato rispetto all’ordine esecutivo del 2023 sull’AI sicura, protetta e affidabile. A gennaio 2025, l’\u003Ca target=\"_blank\" href=\"https://www.federalregister.gov/documents/2025/01/31/2025-02172/removing-barriers-to-american-leadership-in-artificial-intelligence\" rel=\"noopener noreferrer\"\u003EExecutive Order 14179\u003C/a\u003E, “Removing Barriers to American Leadership in Artificial Intelligence”, ha revocato alcune policy precedenti sull’AI e incaricato l’amministrazione di riesaminare e modificare le misure in contrasto con i suoi obiettivi in materia di AI.\u003C/p\u003E\r\n\u003Cp\u003EQuesto cambiamento a livello federale non ha tuttavia ridotto la pressione normativa. Le organizzazioni devono continuare a gestire gli orientamenti delle agenzie federali, le attività di applicazione delle norme e i requisiti statali. L’\u003Ca target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\" rel=\"noopener noreferrer\"\u003EAI Risk Management Framework del National Institute of Standards and Technology (NIST)\u003C/a\u003E resta un framework volontario ampiamente utilizzato per gestire i rischi dell’AI, mentre le autorità di regolamentazione settoriali continuano ad applicare i poteri esistenti ai sistemi basati sull’AI.\u003C/p\u003E\r\n\u003Cp\u003EA livello statale e locale, diverse norme sono diventate importanti punti di riferimento:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EColorado AI Act / ADMT:\u003C/b\u003E il \u003Ca target=\"_blank\" href=\"https://leg.colorado.gov/bills/sb24-205\" rel=\"noopener noreferrer\"\u003EColorado AI Act del 2024\u003C/a\u003E è stato abrogato e sostituito dalla legge \u003Ca target=\"_blank\" href=\"https://leg.colorado.gov/bills/sb26-189\" rel=\"noopener noreferrer\"\u003ESB26-189\u003C/a\u003E, firmata il 14 maggio 2026. La nuova legge limita l’ambito del framework alle tecnologie decisionali automatizzate (ADMT) utilizzate per influire in modo sostanziale su decisioni rilevanti, continuando ad affrontare i problemi di \u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ebias algoritmico\u003C/a\u003E mediante obblighi di trasparenza, informazione, rettifica, ricorso e revisione umana. In generale, l’entrata in vigore degli obblighi applicabili è prevista per il 1° gennaio 2027; ulteriori dettagli attuativi sono attesi dalla normazione del procuratore generale del Colorado.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELocal Law 144 della città di New York:\u003C/b\u003E La \u003Ca target=\"_blank\" href=\"https://www.osc.ny.gov/state-agencies/audits/2025/12/02/enforcement-local-law-144-automated-employment-decision-tools\" rel=\"noopener noreferrer\"\u003Ecittà di New York disciplina\u003C/a\u003E gli strumenti decisionali automatizzati per l’impiego, prevedendo, per gli utilizzi lavorativi interessati, requisiti relativi agli audit sui bias e all’informazione.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENorme della California sui processi decisionali automatizzati:\u003C/b\u003E \u003Ca target=\"_blank\" href=\"https://cppa.ca.gov/announcements/2025/20250923.html\" rel=\"noopener noreferrer\"\u003ELa California ha adottato le norme attuative definitive del California Consumer Privacy Act (CCPA)\u003C/a\u003E in materia di tecnologie decisionali automatizzate, valutazioni del rischio e audit di cybersecurity. Le norme sono entrate in vigore il 1° gennaio 2026, con scadenze di conformità graduali per alcuni requisiti.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ENorme sull’AI specifiche per settore\u003C/h3\u003E\r\n\u003Cp\u003ELa compliance dell’AI dipende in larga misura dal contesto settoriale. Nei settori regolamentati, i sistemi di AI sono spesso valutati nell’ambito dei framework di vigilanza esistenti, anziché esclusivamente in base a norme specifiche per l’AI.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ENel settore bancario,\u003C/b\u003E gli orientamenti sulla gestione del rischio di modello definiscono da tempo le modalità con cui gli istituti convalidano i modelli, documentano le ipotesi e mantengono la governance. Negli Stati Uniti, gli orientamenti della Federal Reserve sul rischio di modello pongono l’accento su sviluppo, implementazione, utilizzo, convalida, controlli, compliance e supervisione da parte del consiglio di amministrazione e dell’alta dirigenza. Nel 2026, le agenzie bancarie federali hanno inoltre pubblicato orientamenti aggiornati sulla gestione del rischio di modello, basati su un approccio proporzionato al rischio. In Italia e nell'Unione Europea, i principali framework di riferimento per la gestione del rischio di modello includono la Guida BCE sui modelli interni, gli Orientamenti EBA sulla governance interna (GL/2021/05) e il quadro regolamentare CRR/CRD.\u003C/li\u003E\r\n\u003Cli\u003ENegli Stati Uniti\u003Cb\u003E nel settore sanitario\u003C/b\u003E l’\u003Ca target=\"_blank\" href=\"https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html\" rel=\"noopener noreferrer\"\u003EHIPAA\u003C/a\u003E può applicarsi quando i sistemi di AI trattano informazioni sanitarie protette, mentre la vigilanza della FDA può essere pertinente quando l’AI fa parte di un Software as a Medical Device (SaMD) o di un dispositivo medico basato sull’AI. La FDA mantiene un elenco dei dispositivi medici basati sull’AI autorizzati alla commercializzazione negli Stati Uniti, a conferma del ruolo crescente dell’AI nei prodotti medici regolamentati. In Italia, la protezione dei dati sanitari è disciplinata dal GDPR (Regolamento UE 2016/679) e dal Codice in materia di protezione dei dati personali (D.Lgs. 196/2003) con il Garante per la protezione dei dati personali quale autorità di supervisione. I dati sanitari rientrano nelle categorie particolari di dati personali, soggette a requisiti rafforzati di consenso, sicurezza e minimizzazione.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENel settore assicurativo,\u003C/b\u003E il bollettino modello della National Association of Insurance Commissioners statunitense definisce le aspettative sulle modalità con cui le compagnie assicurative governano i sistemi di AI e descrive le informazioni che le autorità possono richiedere durante un’indagine o un’ispezione. In Italia e nell'Unione Europea, la vigilanza sull'uso dell'AI nel settore assicurativo è guidata da EIOPA, che ha pubblicato l'Opinion on AI Governance and Risk Management (agosto 2025, EIOPA-BoS-25-360), definendo aspettative su governance dei dati, spiegabilità, equità e supervisione umana dei sistemi di AI. In Italia, IVASS ha condotto un'indagine sull'utilizzo degli algoritmi di machine learning nelle compagnie assicurative (2023) e ha emesso una comunicazione al mercato sulla resilienza operativa digitale e i modelli avanzati di AI (luglio 2026). Con l'entrata in vigore dell'AI Act (Regolamento UE 2024/1689), IVASS è designata quale autorità di vigilanza del mercato per i sistemi di AI ad alto rischio nell'ambito dei servizi assicurativi alla clientela.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENei servizi finanziari e nelle comunicazioni delle società quotate,\u003C/b\u003E la SEC ha indicato che le dichiarazioni sull’AI devono essere accurate e comprovabili. Nel 2024, la SEC ha accusato due consulenti d’investimento di aver rilasciato dichiarazioni false e fuorvianti sull’uso dell’AI, ricordando che la compliance dell’AI comprende anche il marketing, le comunicazioni societarie e le dichiarazioni rivolte alla clientela. In Italia, la CONSOB vigila sulle comunicazioni delle società quotate e dei soggetti che prestano servizi di investimento ai sensi del Regolamento UE sugli abusi di mercato (MAR, Reg. UE 596/2014) e della direttiva MiFID II, che impone requisiti di correttezza, chiarezza e non ingannevolezza nelle comunicazioni alla clientela. Dichiarazioni false o fuorvianti sull'utilizzo dell'AI da parte di società quotate o intermediari finanziari possono costituire violazioni del quadro normativo sugli abusi di mercato e delle regole di condotta MiFID II.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EOrientamenti internazionali sull’AI\u003C/h3\u003E\r\n\u003Cp\u003EAl di fuori dell’UE e degli Stati Uniti, le organizzazioni possono dover considerare anche l’approccio del Regno Unito alla regolamentazione dell’AI basato su principi, le misure cinesi sull’AI generativa e il Model AI Governance Framework di Singapore. Molti di questi regimi differiscono per forza giuridica, ambito di applicazione e maturità dei meccanismi di applicazione, ma tendono a convergere su aspettative ricorrenti: classificazione dei rischi, responsabilità, trasparenza, sorveglianza umana, governance dei dati, monitoraggio e documentazione.\u003C/p\u003E\r\n\u003Cp\u003EAnche gli strumenti non vincolanti sono importanti, perché spesso forniscono il linguaggio di riferimento alla base di leggi, standard e requisiti di approvvigionamento. I \u003Ca target=\"_blank\" href=\"https://www.oecd.org/en/topics/sub-issues/ai-principles.html\" rel=\"noopener noreferrer\"\u003EPrincipi dell’OCSE sull’AI\u003C/a\u003E promuovono la sicurezza mediante un’AI affidabile che rispetti i diritti umani e i valori democratici, mentre la \u003Ca target=\"_blank\" href=\"https://www.unesco.org/en/artificial-intelligence/recommendation-ethics\" rel=\"noopener noreferrer\"\u003ERaccomandazione dell’UNESCO sull’etica dell’intelligenza artificiale\u003C/a\u003E si applica a tutti i 194 Stati membri dell’UNESCO e pone l’accento su diritti umani, equità, trasparenza e sorveglianza umana.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_frameworks-that-structure-compliance":{"id":"title-v2-de83e4040a","additionalClasses":"anchor-title anchor-title--frameworks-that-structure-compliance","type":"heading2","lines":["Framework per strutturare la compliance"],":type":"snowflake-site/components/title-v2"},"text_frameworks-that-structure-compliance_0":{"id":"text-e74976dcf9","text":"\u003Cp\u003ELe leggi definiscono gli obblighi, mentre i framework consentono alle organizzazioni di trasformarli in pratiche operative ripetibili. Forniscono alle funzioni compliance, legale, dati, sicurezza e AI un metodo comune per classificare i sistemi, assegnare i controlli, raccogliere le prove e attestare l’efficacia dei controlli.\u003C/p\u003E\n\u003Ch3\u003EAI RMF del NIST\u003C/h3\u003E\n\u003Cp\u003EL’\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003EAI Risk Management Framework del NIST\u003C/a\u003E è un framework volontario per gestire i rischi associati all’AI per le persone, le organizzazioni e la società. Il NIST articola il framework in quattro funzioni principali: Govern, Map, Measure e Manage.\u003C/p\u003E\n\u003Cp\u003EL’AI RMF del NIST è utile per i team di compliance perché definisce una struttura di gestione dei rischi riconducibile agli obblighi legali e interni.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EGovern\u003C/b\u003E definisce ruoli, policy e responsabilità.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EMap\u003C/b\u003E descrive il contesto e l’uso previsto.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EMeasure\u003C/b\u003E valuta i rischi e le prestazioni del sistema\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EManage\u003C/b\u003E assegna la priorità alle risposte, al monitoraggio e alle misure correttive.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003ENORMA ISO/IEC 42001\u003C/h3\u003E\n\u003Cp\u003ELa \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003Enorma ISO/IEC 42001\u003C/a\u003E è uno standard certificabile per i sistemi di gestione dell’AI. L’ISO la descrive come il primo standard al mondo per i sistemi di gestione dell’AI, volto a consentire alle organizzazioni di gestire rischi e opportunità associati all’AI mediante un sistema strutturato di policy, processi, ruoli e miglioramento continuo.\u003C/p\u003E\n\u003Cp\u003EPer la compliance dell’AI, la norma ISO/IEC 42001 è particolarmente utile perché supporta evidenze formali relative al sistema di gestione: ambito, governance, valutazione dei rischi, valutazione d’impatto, controlli sul ciclo di vita, gestione dei fornitori, monitoraggio, audit interno e riesame della direzione. Le organizzazioni che necessitano di un framework certificabile affiancano spesso la norma ISO/IEC 42001 a linee guida più dettagliate sui rischi o specifiche per il settore.\u003C/p\u003E\n\u003Ch3\u003ENorma ISO/IEC 23894\u003C/h3\u003E\n\u003Cp\u003ELa \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.iso.org/standard/77304.html\"\u003Enorma ISO/IEC 23894\u003C/a\u003E fornisce linee guida per la gestione dei rischi dell’AI. Consente alle organizzazioni che sviluppano, producono, implementano o utilizzano sistemi di AI di integrare nelle proprie attività e funzioni una gestione dei rischi specifica per l’AI.\u003C/p\u003E\n\u003Cp\u003EA differenza della norma ISO/IEC 42001, che specifica i requisiti di un sistema di gestione dell’AI, la norma ISO/IEC 23894 fornisce linee guida. Consente di individuare le fonti di rischio, analizzare i potenziali impatti e strutturare il trattamento dei rischi lungo il ciclo di vita dell’AI.\u003C/p\u003E\n\u003Ch3\u003ESerie IEEE 7000\u003C/h3\u003E\n\u003Cp\u003ELa \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://standards.ieee.org/ieee/7000/6781/\"\u003Eserie IEEE 7000\u003C/a\u003E definisce standard incentrati sulla progettazione etica e responsabile delle tecnologie. In un programma di compliance dell’AI, questi standard consentono di strutturare aspetti quali trasparenza, bias, responsabilità, privacy e valori umani. Generalmente, non costituiscono l’unica base per la compliance, ma consentono di tradurre gli impegni etici in pratiche di progettazione e documentazione.\u003C/p\u003E\n\u003Ch3\u003EOCSE e UNESCO\u003C/h3\u003E\n\u003Cp\u003EI Principi OCSE sull’AI e la Raccomandazione UNESCO non sono vincolanti, ma influenzano le leggi, le policy e le aspettative delle autorità di regolamentazione. Costituiscono riferimenti normativi utili quando le organizzazioni devono spiegare perché le policy interne sull’AI pongono l’accento su equità, trasparenza, responsabilità, supervisione umana e rispetto dei diritti, anche in assenza di un’unica legge vincolante sull’AI.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/it/artificial-intelligence/ai-governance/responsible-ai/\"\u003EScopri di più sull’AI responsabile\u003C/a\u003E\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-core-ai-compliance-controls":{"id":"title-v2-2bc3929a6d","additionalClasses":"anchor-title anchor-title--the-core-ai-compliance-controls","type":"heading2","lines":["I controlli fondamentali per la compliance dell’AI"],":type":"snowflake-site/components/title-v2"},"text_the-core-ai-compliance-controls_0":{"id":"text-1e11fd1fd6","text":"\u003Cp\u003EI controlli di compliance dell’AI consentono alle organizzazioni di integrare framework e pratiche di compliance nei sistemi e nei flussi di lavoro quotidiani. Una policy può stabilire che l’AI ad alto rischio richieda la supervisione umana, ma un controllo specifica chi esamina il sistema, dove viene registrata l’approvazione, come la persona incaricata della revisione accede agli output pertinenti del modello e che cosa accade quando il monitoraggio rileva drift, bias o comportamenti non sicuri.\u003C/p\u003E\n\u003Ch3\u003EControlli di governance\u003C/h3\u003E\n\u003Cp\u003EI controlli di governance stabiliscono chi è responsabile dei sistemi di AI e come vengono prese le decisioni. In genere comprendono la supervisione dirigenziale, una policy sull’AI, criteri di classificazione per livello di rischio, assegnazioni dei ruoli e procedure di escalation.\u003C/p\u003E\n\u003Cp\u003EUn modello creditizio ad alto rischio, ad esempio, deve avere un responsabile del modello, un responsabile business, un responsabile tecnico e una persona incaricata della verifica di compliance chiaramente identificati. L’organizzazione deve poter dimostrare chi ha approvato il caso d’uso, quale livello di rischio è stato assegnato, quale insieme di controlli è stato applicato e chi può richiedere misure correttive, la sospensione o il rollback.\u003C/p\u003E\n\u003Cp\u003EI controlli di governance più comuni comprendono:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EPolicy sull’AI e standard di uso accettabile\u003C/li\u003E\n\u003Cli\u003EResponsabilità dell’inventario dell’AI\u003C/li\u003E\n\u003Cli\u003EMetodologia di classificazione per livello di rischio\u003C/li\u003E\n\u003Cli\u003EAssegnazione dei ruoli di responsabile dell’AI, del modello e dei dati\u003C/li\u003E\n\u003Cli\u003EApprovazione del comitato di revisione o del consiglio di governance\u003C/li\u003E\n\u003Cli\u003EGestione delle eccezioni con date di scadenza\u003C/li\u003E\n\u003Cli\u003EAttestazione dei controlli e revisione dell’audit interno\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EControlli sulla documentazione\u003C/h3\u003E\n\u003Cp\u003EI controlli sulla documentazione creano il registro scritto che autorità di regolamentazione, auditor, clienti e persone incaricate delle revisioni interne possono esaminare. Nella compliance dell’AI, la documentazione non è un’attività una tantum svolta al momento del lancio: deve accompagnare il sistema quando cambiano i dati, vengono aggiornate le versioni del modello, cambiano i fornitori e si ampliano i casi d’uso.\u003C/p\u003E\n\u003Cp\u003ELa documentazione tipica comprende schede dei modelli, datasheet, riepiloghi dei dati di addestramento, risultati delle valutazioni, documentazione del sistema, diagrammi architetturali e fascicoli tecnici. Ai sensi dell’AI Act dell’UE, l’allegato IV descrive le informazioni previste nella documentazione tecnica dei sistemi di AI ad alto rischio, che possono riguardare finalità, progettazione, sviluppo, dati, monitoraggio, convalida e gestione dei rischi del sistema.\u003C/p\u003E\n\u003Cp\u003EPer i \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003Esistemi di AI generativa\u003C/a\u003E, la documentazione può dover includere anche prompt, fonti di retrieval, filtri di sicurezza, revisione umana, usi vietati e attestazioni dei fornitori. La finalità non è produrre documenti fini a se stessi, ma preservare il contesto necessario per comprendere che cosa fa il sistema, come è stato testato e quali evidenze ne giustificano l’uso approvato.\u003C/p\u003E\n\u003Ch3\u003EControlli di valutazione\u003C/h3\u003E\n\u003Cp\u003EI controlli di valutazione verificano se un sistema AI è appropriato per l’uso previsto. Questi controlli possono includere valutazioni d’impatto dell’AI, DPIA, audit dei bias, report di red teaming, test avversariali, verifiche della privacy, verifiche della sicurezza dell’AI e test pre-deployment.\u003C/p\u003E\n\u003Cp\u003EAd esempio, uno strumento di screening per le assunzioni può richiedere un audit dei bias e un’informativa ai candidati ai sensi di una legge, una DPIA ai sensi di un’altra e una valutazione interna della pertinenza rispetto al ruolo, della spiegabilità, della documentazione del fornitore e della revisione umana. Un sistema AI clinico può richiedere la convalida rispetto all’uso previsto, una verifica della sicurezza dei pazienti e il monitoraggio delle variazioni delle prestazioni tra le diverse popolazioni.\u003C/p\u003E\n\u003Cp\u003EI controlli di valutazione devono produrre evidenze durature: risultati dei test, note di chi effettua la revisione, piani di rimedio, registri delle approvazioni ed eventuali rischi irrisolti formalmente accettati dai responsabili designati.\u003C/p\u003E\n\u003Ch3\u003EControlli operativi\u003C/h3\u003E\n\u003Cp\u003EI controlli operativi regolano il comportamento dei sistemi AI dopo il deployment. Includono la gestione degli accessi, i log di audit, il monitoraggio, la segnalazione degli incidenti, la supervisione umana e il monitoraggio post-commercializzazione.\u003C/p\u003E\n\u003Cp\u003EQuesti controlli sono essenziali perché la compliance dell’AI non termina quando un modello entra in produzione. Un modello può subire deriva, un template di prompt può cambiare, un fornitore di modelli foundation può aggiornare il modello sottostante, una pipeline di dati può iniziare a fornire valori diversi oppure una policy di accesso può esporre campi sensibili a un gruppo più ampio di utenti. La compliance dipende dalla capacità di rilevare questi cambiamenti e conservare le evidenze necessarie per spiegarli.\u003C/p\u003E\n\u003Cp\u003EI controlli operativi comuni includono:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/it/data-governance/data-security/rbac/\"\u003EControllo degli accessi basato sui ruoli\u003C/a\u003E (RBAC)\u003C/li\u003E\n\u003Cli\u003ELog di audit per le attività su dati, modelli e applicazioni\u003C/li\u003E\n\u003Cli\u003EMonitoraggio delle prestazioni e della deriva dei modelli\u003C/li\u003E\n\u003Cli\u003ERegistrazione di prompt e risposte, ove appropriato\u003C/li\u003E\n\u003Cli\u003ERevisione con intervento umano per le decisioni con conseguenze significative\u003C/li\u003E\n\u003Cli\u003ESegnalazione degli incidenti e procedure di escalation\u003C/li\u003E\n\u003Cli\u003EMonitoraggio post-commercializzazione o post-deployment\u003C/li\u003E\n\u003Cli\u003EAttestazione periodica dei controlli\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EControlli di terze parti\u003C/h3\u003E\n\u003Cp\u003EMolti sistemi AI dipendono da fornitori, fornitori di modelli foundation, strumenti SaaS, fornitori di dati o funzionalità AI integrate che l’organizzazione non ha sviluppato. I controlli di terze parti consentono di valutare queste dipendenze prima del deployment e monitorarle nel tempo.\u003C/p\u003E\n\u003Cp\u003ELa due diligence sui fornitori deve documentare l’uso previsto, il trattamento dei dati, la documentazione dei modelli, i controlli di sicurezza, i subappaltatori, le policy di conservazione, gli impegni relativi ai dati di addestramento, le condizioni di notifica degli incidenti e le attestazioni disponibili. Per i modelli foundation, le organizzazioni possono inoltre richiedere la documentazione del fornitore, valutazioni della sicurezza e informazioni a supporto di una distinta base dell’AI (AI bill of materials, AIBOM), che elenca i modelli, le fonti di dati, le librerie, le API e i fornitori coinvolti in un sistema AI.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_best-practices-for-ai-compliance":{"id":"title-v2-ec6569220e","additionalClasses":"anchor-title anchor-title--best-practices-for-ai-compliance","type":"heading2","lines":["Best practice per la compliance dell’AI"],":type":"snowflake-site/components/title-v2"},"text_best-practices-for-ai-compliance_0":{"id":"text-95e783c110","text":"\u003Cp\u003ELa compliance dell’AI è gestibile solo implementando la \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003Etracciabilità\u003C/a\u003E: ogni sistema AI è associato a un livello di rischio, ogni livello di rischio agli obblighi, ogni obbligo ai controlli e ogni controllo alle evidenze.\u003C/p\u003E\n\u003Ch3\u003ECensire ogni sistema AI\u003C/h3\u003E\n\u003Cp\u003EUn inventario dell’AI deve includere i modelli di ML tradizionali, le app di AI generativa, gli assistenti interni, l’AI integrata nelle piattaforme SaaS e le funzionalità AI fornite da terze parti. Non è possibile classificare, monitorare o attestare la conformità di sistemi che non sono stati censiti.\u003C/p\u003E\n\u003Cp\u003EUn inventario dell’AI efficace registra in genere il nome del sistema, il responsabile, la finalità aziendale, gli utenti, le fonti di dati, il tipo di modello, le dipendenze dai fornitori, le giurisdizioni, il livello di rischio, le popolazioni interessate, lo stato di approvazione e la frequenza delle revisioni. Per l’AI integrata, l’inventario deve inoltre indicare quale feature SaaS utilizza l’AI, quali dati tratta e se il fornitore usa i dati dei clienti per l’addestramento o il miglioramento.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_best-practices-for-ai-compliance_0":{"id":"text-b1b443dca5","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003ECONSIGLIO RAPIDO\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EInizia dall’inventario dell’AI prima di affrontare i requisiti normativi. La maggior parte delle lacune di compliance deriva dal non sapere quali modelli, assistenti, fornitori o app basate sull’AI siano già in uso nell’organizzazione.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_best-practices-for-ai-compliance_1":{"id":"text-32126bcaa0","text":"\u003Ch3\u003EClassificare i sistemi per livello di rischio\u003C/h3\u003E\n\u003Cp\u003EOgni sistema deve essere classificato in base ai criteri giuridici e interni applicabili. Ai sensi dell’AI Act dell’Unione europea, può essere necessario stabilire se un caso d’uso è vietato, ad alto rischio, a rischio limitato o a rischio inferiore. In base alle policy interne, può essere necessario classificare i sistemi per il loro potenziale impatto su diritti, sicurezza, risultati finanziari, occupazione, assistenza sanitaria, accesso ai servizi o esposizione normativa.\u003C/p\u003E\n\u003Cp\u003ELa classificazione per livello di rischio deve basarsi su evidenze. Un chatbot che risponde a domande generali sulle policy non comporta gli stessi obblighi di un modello che raccomanda trattamenti per i pazienti o classifica candidati per un impiego. Il registro di classificazione deve spiegare le motivazioni, non limitarsi ad assegnare un’etichetta.\u003C/p\u003E\n\u003Ch3\u003EAssociare gli obblighi a controlli ed evidenze\u003C/h3\u003E\n\u003Cp\u003EDopo aver identificato gli obblighi applicabili, è necessaria una mappa dei controlli. Per ogni obbligo, la mappa deve identificare il controllo pertinente, il relativo responsabile, la fonte delle evidenze e la frequenza delle revisioni.\u003C/p\u003E\n\u003Cp\u003EAd esempio:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EObbligo\u003C/th\u003E\u003Cth\u003EControllo\u003C/th\u003E\u003Cth\u003EEvidenza\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ESupervisione umana per l’AI ad alto rischio\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EUna persona incaricata della revisione approva gli output con conseguenze rilevanti prima dell’azione finale\u003C/td\u003E\u003Ctd\u003ELog del workflow di revisione, registri delle approvazioni, assegnazioni dei ruoli\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EValutazione d’impatto della protezione dei dati (DPIA) per il trattamento ad alto rischio dei dati personali\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EIl team responsabile della privacy completa e approva la DPIA prima del deployment\u003C/td\u003E\u003Ctd\u003EDocumento DPIA, data di approvazione, azioni correttive\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EDocumentazione tecnica\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ELa persona responsabile del modello mantiene aggiornati la scheda del modello e il fascicolo tecnico\u003C/td\u003E\u003Ctd\u003EScheda del modello, riepilogo dei dati, metriche di valutazione, cronologia delle versioni\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ELimitazione degli accessi\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EL’accesso ai dati sensibili è limitato in base al ruolo e alle policy\u003C/td\u003E\u003Ctd\u003EPolicy di accesso, cronologia degli accessi, log di audit\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EMonitoraggio successivo al deployment\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ELe prestazioni e il drift del modello sono esaminati con cadenza trimestrale\u003C/td\u003E\u003Ctd\u003EDashboard di monitoraggio, note di revisione, registri degli incidenti\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Ch3\u003EAutomatizzare la raccolta delle evidenze\u003C/h3\u003E\n\u003Cp\u003ELa raccolta manuale delle evidenze diventa inefficace con l’aumentare del numero di sistemi AI. I sistemi ad alto rischio possono richiedere revisioni trimestrali, rivalutazioni annuali, segnalazione degli incidenti e monitoraggio continuo; anche i sistemi a rischio limitato possono richiedere informative sulla trasparenza, registri degli accessi e aggiornamenti della documentazione.\u003C/p\u003E\n\u003Cp\u003EL’automazione delle evidenze acquisisce segnali direttamente dai sistemi in cui si svolgono le attività di AI: cronologia degli accessi, lineage, metadati del registro dei modelli, dashboard di monitoraggio, workflow di approvazione, sistemi di gestione degli incidenti e motori di policy. L’obiettivo è ridurre la discrepanza tra il comportamento effettivo del sistema e la documentazione di compliance. Quando chi esegue un audit chiede a quali dati abbia avuto accesso un modello, chi abbia modificato una policy o quale versione del modello fosse in deployment durante un incidente, le evidenze dovrebbero essere già disponibili.\u003C/p\u003E\n\u003Ch3\u003EDefinire una cadenza di revisione\u003C/h3\u003E\n\u003Cp\u003EI sistemi AI devono essere esaminati con una cadenza commisurata al loro rischio. I sistemi ad alto rischio richiedono spesso revisioni trimestrali, soprattutto quando incidono su diritti, sicurezza, idoneità, pricing, occupazione o decisioni regolamentate. I sistemi a rischio limitato possono essere esaminati annualmente, salvo modifiche sostanziali.\u003C/p\u003E\n\u003Cp\u003ELe revisioni devono verificare se siano cambiati lo scopo, i dati, il modello, il fornitore, il gruppo di utenti, la giurisdizione o il profilo di rischio del sistema. Un caso d’uso nato come assistente interno per la produttività può diventare uno strumento di supporto rivolto alla clientela; un modello addestrato per una regione può essere riutilizzato in un’altra; una feature SaaS può integrare l’AI generativa senza un ciclo di approvvigionamento completo. Una cadenza di revisione consente di rilevare questi cambiamenti prima che la documentazione di compliance diventi obsoleta.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_best-practices-for-ai-compliance_1":{"id":"text-6d5e744f60","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EL’ERRORE COMUNE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMolte organizzazioni considerano la compliance dell’AI un’attività di approvazione una tantum. In realtà, la compliance può deteriorarsi rapidamente quando modelli, fonti di dati, fornitori o casi d’uso cambiano senza aggiornamenti corrispondenti della documentazione, delle valutazioni dei rischi e dei controlli di monitoraggio.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_ai-compliance-on-snowflake":{"id":"title-v2-59888eacab","additionalClasses":"anchor-title anchor-title--ai-compliance-on-snowflake","type":"heading2","lines":["La compliance dell’AI su Snowflake"],":type":"snowflake-site/components/title-v2"},"text_ai-compliance-on-snowflake_0":{"id":"text-8cb1a816be","text":"\u003Cp\u003ELa compliance dell’AI dipende dalle evidenze: quali dati sono stati utilizzati, chi vi ha avuto accesso, come sono stati trasferiti, quale versione del modello era in deployment, quali policy erano applicabili e quali controlli erano operativi al momento della revisione.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E supporta i workflow di governance, individuazione e compliance rendendo disponibili informazioni contestuali come cronologia degli accessi, lineage, classificazione e applicazione delle policy. La vista ACCESS_HISTORY Snowflake registra quali dati sono stati consultati, quando è avvenuto l’accesso e come sono stati trasferiti dagli oggetti di origine a quelli di destinazione, consentendo di produrre evidenze di audit per i controlli su accessi, lineage e utilizzo dei dati.\u003C/p\u003E\n\u003Cp\u003EPer i sistemi ML, Snowflake ML Model Registry archivia e gestisce le versioni, le metriche e i metadati dei modelli, supporta la gestione del loro ciclo di vita e consente di monitorarne le prestazioni e il drift tramite Snowflake ML Observability. Supporta inoltre RBAC per l’accesso ai modelli, consentendo di collegare la governance dei modelli agli stessi tipi di controlli utilizzati per l’accesso ai dati.\u003C/p\u003E\n\u003Cp\u003EPer i casi d’uso di AI generativa, Cortex Guard identifica e filtra le risposte potenzialmente non sicure o dannose prima che l’output del modello venga restituito all’applicazione. A seconda dell’architettura, è possibile abbinare queste misure di protezione ai log di prompt, risposte, accessi e applicazioni per supportare gli audit trail dell’AI generativa.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://trust.snowflake.com/\"\u003ESnowflake Compliance Center\u003C/a\u003E consente alla clientela di accedere a risorse per la sicurezza e la compliance, comprese certificazioni e relazioni utili per la due diligence sui fornitori e gli audit condotti dalla clientela o dalle autorità di regolamentazione. Snowflake ha inoltre ottenuto la certificazione ISO/IEC 42001 per il proprio sistema di gestione dell’AI, soggetta ad audit di sorveglianza continui. La certificazione rappresenta un esempio concreto di come organizzare la governance dell’AI, la gestione dei rischi dell’AI e le pratiche di compliance secondo uno standard certificabile per sistemi di gestione.\u003C/p\u003E\n\u003Cp\u003ENel loro insieme, queste funzionalità consentono alle organizzazioni di associare le evidenze di compliance ai sistemi che le producono: percorsi di lineage, log degli accessi, metadati dei modelli, metriche di valutazione, registri dell’applicazione delle policy e artefatti di controllo. Ciò non elimina la necessità di revisione legale, classificazione dei rischi o responsabilità umana, ma riduce il lavoro manuale necessario per dimostrare che i controlli dell’AI sono operativi.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_building-ai-compliance-into-the-ai-lifecycle":{"id":"title-v2-96be30afb7","additionalClasses":"anchor-title anchor-title--building-ai-compliance-into-the-ai-lifecycle","type":"heading2","lines":["Integrare la compliance dell’AI nel ciclo di vita dell’AI"],":type":"snowflake-site/components/title-v2"},"text_building-ai-compliance-into-the-ai-lifecycle_0":{"id":"text-2658ad9ce6","text":"\u003Cp\u003ELa compliance dell’AI è più facile da dimostrare quando è integrata nel ciclo di vita. Un sistema AI deve essere inserito nell’inventario quando viene proposto il caso d’uso, ricevere un livello di rischio prima dell’avvio dello sviluppo, conservare la documentazione durante i test e il deployment, e preservare le evidenze man mano che cambiano utenti, dati, modelli e fornitori.\u003C/p\u003E\n\u003Cp\u003EQuesto modello operativo è importante perché i sistemi AI raramente restano invariati. I team responsabili della compliance possono gestire questi cambiamenti solo se riescono a tracciare il sistema dagli obblighi ai controlli e alle evidenze.\u003C/p\u003E\n\u003Cp\u003ELe organizzazioni che gestiscono efficacemente la compliance dell’AI non sono quelle con i documenti di policy più articolati. Sono quelle in grado di rispondere rapidamente a domande specifiche, come quali sistemi AI siano in uso, quali siano ad alto rischio, quali dati trattino, quali controlli si applichino, quando sia avvenuta l’ultima revisione e quali evidenze ne dimostrino la conformità.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_building-ai-compliance-into-the-ai-lifecycle_0":{"id":"text-9757196c91","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EIN SINTESI\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUna compliance dell’AI efficace dipende meno dalla creazione di policy e più dal mantenimento delle prove. Le organizzazioni in grado di collegare con continuità i sistemi AI ai relativi rischi, controlli e prove saranno nella posizione migliore per soddisfare le richieste di autorità di regolamentazione, auditor, clienti e stakeholder interni con la crescente adozione dell’AI.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-ai-compliance","text_what-is-ai-compliance_0","title_the-ai-regulatory-landscape","text_the-ai-regulatory-landscape_0","title_frameworks-that-structure-compliance","text_frameworks-that-structure-compliance_0","title_the-core-ai-compliance-controls","text_the-core-ai-compliance-controls_0","title_best-practices-for-ai-compliance","text_best-practices-for-ai-compliance_0","callout_best-practices-for-ai-compliance_0","text_best-practices-for-ai-compliance_1","callout_best-practices-for-ai-compliance_1","title_ai-compliance-on-snowflake","text_ai-compliance-on-snowflake_0","title_building-ai-compliance-into-the-ai-lifecycle","text_building-ai-compliance-into-the-ai-lifecycle_0","callout_building-ai-compliance-into-the-ai-lifecycle_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,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-98a3309532",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-d15f95a22b","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-cbab8f415b","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-ai-compliance\"\u003EChe cos’è la compliance dell’AI?\u003C/li\u003E\u003Cli data-anchor=\"the-ai-regulatory-landscape\"\u003EIl quadro normativo dell’AI\u003C/li\u003E\u003Cli data-anchor=\"frameworks-that-structure-compliance\"\u003EFramework per strutturare la compliance\u003C/li\u003E\u003Cli data-anchor=\"the-core-ai-compliance-controls\"\u003EI controlli fondamentali per la compliance dell’AI\u003C/li\u003E\u003Cli data-anchor=\"best-practices-for-ai-compliance\"\u003EBest practice per la compliance dell’AI\u003C/li\u003E\u003Cli data-anchor=\"ai-compliance-on-snowflake\"\u003ELa compliance dell’AI su Snowflake\u003C/li\u003E\u003Cli data-anchor=\"building-ai-compliance-into-the-ai-lifecycle\"\u003EIntegrare la compliance dell’AI nel ciclo di vita dell’AI\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1786318617":{"id":"flexible-column-container-bc34b331ec","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy":{"id":"title-v2-aeb7845fcb","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Domande frequenti"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-9e1bd147b5","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELe risposte degli esperti Snowflake alle domande più comuni sulla compliance dell’AI.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-b888420b22","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"La compliance dell’AI è obbligatoria per legge?","richText":"\u003Cp\u003EIn molti casi, sì. L’AI Act dell’Unione europea impone obblighi vincolanti per i sistemi AI che rientrano nel suo ambito di applicazione, mentre il GDPR, le leggi statali degli Stati Uniti, le norme sul lavoro, le normative settoriali e le leggi a tutela dei consumatori possono applicarsi in base al luogo e alle modalità di utilizzo di un sistema AI.\u003C/p\u003E"},{"title":"Qual è la differenza tra compliance dell’AI e governance dell’AI?","richText":"\u003Cp\u003ELa governance dell’AI è il modello operativo: definisce chi decide, chi approva, chi monitora e chi può intervenire. La compliance dell’AI è la disciplina che assicura la conformità: dimostra che l’organizzazione ha rispettato le leggi, gli standard e le policy interne applicabili.\u003C/p\u003E"},{"title":"Quale framework per la compliance dell’AI dovrebbe adottare la mia organizzazione?","richText":"\u003Cp\u003ELa maggior parte delle aziende ne utilizza più di uno. L’AI RMF del NIST è utile come metodologia per la gestione dei rischi, ISO/IEC 42001 definisce un sistema di gestione dell’AI certificabile e ISO/IEC 23894 fornisce linee guida specifiche per la gestione dei rischi legati all’AI. Le organizzazioni soggette a normative settoriali dovrebbero inoltre associare i controlli dell’AI agli orientamenti di vigilanza applicabili, come le linee guida sulla gestione del rischio dei modelli per le banche o i requisiti della FDA per i dispositivi medici basati sull’AI.\u003C/p\u003E"},{"title":"Come si dimostra la compliance dell’AI?","richText":"\u003Cp\u003EÈ possibile dimostrare la compliance dell’AI attraverso una catena di prove: policy documentate, inventari dei sistemi, classificazioni dei rischi, valutazioni d’impatto, DPIA, schede dei modelli, fascicoli tecnici, log degli accessi, registri del data lineage, dashboard di monitoraggio, registri degli incidenti e attestazioni di terze parti. Ogni artefatto deve essere associato a un obbligo e a un controllo specifici.\u003C/p\u003E"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1467213961":{"id":"flexible-column-container-c12350e334","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-deab99efd2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-c3878c9453","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Esplora le risorse 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