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This article covers transparency requirements from model cards to audit logs, regulatory drivers (EU AI Act, NIST AI RMF), operationalization approaches and how Snowflake provides connected records for disclosure and auditability.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_copy":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn 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border-top","id":"container-b29beb35d6","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-ae7d2479f3","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EAI Governance Topics:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-5a6bb43488","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-compliance/\"\u003EAI Compliance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003EAI Ethics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EAI Risk Management\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003EAI Traceability\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003EAlgorithmic Bias\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EEU AI Act\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO 42001\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003EResponsible AI\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"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-1792fdd7d4","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","id":"hub-body-content","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text":{"id":"text-a163dd2e1f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EAI TRANSPARENCY DEFINED\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EAI transparency means making an AI system’s purpose, inputs, limitations, behavior and governance records understandable to the people who build, use or oversee it.\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-49fe1ca7a1","text":"\u003Cp\u003EAI transparency is often framed as a problem of model complexity — the black box concern, the question of whether a system’s internal mechanics can ever be made legible. But the transparency failures that cause the most organizational pain are rarely that sophisticated. They occur when a team can’t identify which model version is running in production, or when training data provenance hasn’t been documented, or when an incident response team can’t reconstruct what happened because the records were never kept.\u003C/p\u003E\r\n\u003Cp\u003EResolving transparency problems requires that documentation be attached to the systems themselves: a model card that names the model’s intended use and known limitations, a data sheet that explains where training data came from and what gaps remain, a system prompt or guardrail record that shows how a generative application was instructed to behave, and an audit log that records which user, service or application accessed which data, prompt or output at runtime.\u003C/p\u003E\r\n\u003Cp\u003EAI transparency doesn’t require every user to understand each parameter, embedding or feature interaction — but it does require enough context for developers, business owners, regulators and affected users to understand what an AI system is supposed to do, how it was built, when it should not be used and what evidence exists when something goes wrong.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-transparency":{"id":"title-v2-a05508db29","additionalClasses":"anchor-title anchor-title--what-is-ai-transparency","type":"heading2","lines":["What is AI transparency?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-transparency_0":{"id":"text-71ff4aa27b","text":"\u003Cp\u003EAI transparency is the degree to which the inputs, mechanics, intended use and limitations of an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003Eartificial intelligence\u003C/a\u003E system are understandable and accessible to the people affected by it — including developers, users, regulators and the public.\u003C/p\u003E\r\n\u003Cp\u003EIn practice, transparency is a system-level discipline. It answers questions such as: What is this system? What model does it use? What data shaped it? What task is it designed for? What uses are out of scope? What disclosures should users see before they rely on its output? What records exist if an auditor, steward or incident response team needs to reconstruct what happened?\u003C/p\u003E\r\n\u003Cp\u003EAI transparency is different from explainability. Explainability usually operates at the decision level: Why did this model produce this score, ranking, classification or response? Transparency operates one level up. It describes the system’s purpose, design context, documentation, runtime behavior and governance boundaries.\u003C/p\u003E\r\n\u003Cp\u003EA transparent AI system typically includes artifacts such as model cards, data sheets, system prompt documentation, intended-use statements, out-of-scope-use statements, disclosure notices, audit logs and lineage records. Together, these artifacts help teams understand both the AI system and the conditions under which its output should be trusted.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-ai-transparency-matters":{"id":"title-v2-dd80e2a3e6","additionalClasses":"anchor-title anchor-title--why-ai-transparency-matters","type":"heading2","lines":["Why AI transparency matters"],":type":"snowflake-site/components/title-v2"},"text_why-ai-transparency-matters_0":{"id":"text-51e723c45b","text":"\u003Cp\u003EAI transparency matters because AI systems increasingly participate in decisions, workflows and interactions where the affected person may not be the person who built the system.\u003C/p\u003E\r\n\u003Cp\u003ERegulation is making this context harder to treat as optional. Under the \u003Ca rel=\"noopener noreferrer\" href=\"https://artificialintelligenceact.eu/\" target=\"_blank\"\u003EEU AI Act\u003C/a\u003E, Article 13 requires high-risk AI systems to be designed and developed in a way that lets deployers interpret outputs and use the system appropriately, with instructions for use that include the system’s capabilities, limitations and expected level of accuracy. Article 50 sets transparency obligations for certain AI systems, including requirements to inform people when they are interacting with AI systems in some contexts and to disclose or label certain synthetic or manipulated content.\u003C/p\u003E\r\n\u003Cp\u003EFor providers of general-purpose AI models, the \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EEU AI Act\u003C/a\u003E also introduces documentation obligations. Article 53 includes requirements to maintain technical documentation and make available information for downstream providers, while the \u003Ca rel=\"noopener noreferrer\" href=\"https://digital-strategy.ec.europa.eu/en/faqs/general-purpose-ai-models-ai-act-questions-answers\" target=\"_blank\"\u003EEuropean Commission’s materials\u003C/a\u003E describe a public summary of training content for general-purpose AI models.\u003C/p\u003E\r\n\u003Cp\u003EBut transparency is not only a compliance task. It’s also how organizations make AI usable in production. For example, a sales team is more likely to use a recommendation system when they understand the system’s scope and limitations. A risk team can respond faster when they can trace an unexpected output back to a model version, prompt template, data source or policy change. A data science team can improve a model more safely when they can compare current performance against the assumptions captured at release.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_9978331":{"id":"text-a702a359e9","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECOMMON PITFALL\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ETransparency shouldn’t be treated as static documentation. Documentation can quickly lose value if it's not tied to changing model versions, prompts, retrieval sources, guardrails and runtime logs.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_what-transparency-looks-like-in-practice":{"id":"title-v2-50a61ddc63","additionalClasses":"anchor-title anchor-title--what-transparency-looks-like-in-practice","type":"heading2","lines":["What transparency looks like in practice"],":type":"snowflake-site/components/title-v2"},"text_what-transparency-looks-like-in-practice_0":{"id":"text-72df6dac52","text":"\u003Cp\u003EAI transparency shows up in practice as concrete artifacts attached to models, data sets, prompts, applications and runtime events. The aim is legibility — enough context for the right person to evaluate the system before deployment, use it appropriately in context and investigate it afterward.\u003C/p\u003E\r\n\u003Cp\u003EThese artifacts also form the operational backbone of \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003Eresponsible AI\u003C/a\u003E. Principles like fairness, safety and accountability depend on documentation, disclosure and auditability to be enforced in practice. Without these records, responsible AI remains aspirational rather than actionable.\u003C/p\u003E\r\n\u003Ch3\u003EModel cards\u003C/h3\u003E\r\n\u003Cp\u003EA model card is a structured document that describes a model’s intended use, training context, performance characteristics, limitations and fairness considerations. The original model card framework was proposed to support transparent model reporting, especially for systems used in high-impact domains where aggregate accuracy can obscure uneven performance across groups or contexts.\u003C/p\u003E\r\n\u003Cp\u003EFor enterprise AI teams, a model card is often the minimum viable transparency artifact. It should name the model, model version, owner, intended use, out-of-scope use, evaluation metrics, test data, known limitations, approval status and monitoring expectations. For \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003Egenerative AI\u003C/a\u003E, it may also include information about prompting strategy, retrieval context, safety evaluations and expected failure modes.\u003C/p\u003E\r\n\u003Cp\u003EA useful model card doesn’t need to expose proprietary implementation details, but it should give developers, reviewers and downstream teams enough context to decide whether the model is suitable for a specific workload.\u003C/p\u003E\r\n\u003Ch3\u003EData sheets for data sets\u003C/h3\u003E\r\n\u003Cp\u003EA model’s behavior depends on the data that shaped it. Data sheets for data sets document the provenance, composition, collection process, preprocessing steps, recommended uses and known limitations of a data set. The concept was proposed to improve communication between data set creators and data set consumers, particularly where data may later be reused outside its original collection context.\u003C/p\u003E\r\n\u003Cp\u003EIn \u003Ca href=\"https://www.snowflake.com/en/data-governance/ai/\"\u003EAI data governance\u003C/a\u003E and transparency programs, data sheets help answer questions that model documentation alone cannot answer: Did the training data include the populations, geographies or time periods relevant to the current deployment? Were sensitive attributes removed, transformed or used for evaluation? Were labels created by humans, another model or a third-party provider? Which preprocessing steps changed the original data?\u003C/p\u003E\r\n\u003Cp\u003EThese details become especially important when a model is reused across teams, such as when table gets repurposed for a new feature, a data source changes ownership or a field that once served as a reasonable proxy stops matching the business concept it was meant to represent. Without data set documentation, those shifts often surface only after performance degrades or a user challenges an output.\u003C/p\u003E\r\n\u003Ch3\u003ESystem prompts and guardrail documentation\u003C/h3\u003E\r\n\u003Cp\u003EFor generative AI governance, transparency also depends on the application layer around the model. A system prompt can define the assistant’s role, response style, retrieval behavior, refusal rules, escalation path and constraints on tool use. Guardrails can define what the system should block, redact, route for review or log for audit.\u003C/p\u003E\r\n\u003Cp\u003EDocumenting these controls helps teams distinguish between model behavior and application behavior. If a customer support assistant gives an answer outside policy, the issue may sit in the base model, the retrieval source, the system prompt, the guardrail configuration or the orchestration logic that selected a tool. A transparency record should make those layers visible enough for review.\u003C/p\u003E\r\n\u003Cp\u003EDisclosure requirements also attach to this layer. A user-facing notice should make the system’s behavior clear before someone relies on it: including whether they are interacting with AI, content is AI-generated, sensitive features are being used or a human review path is available.\u003C/p\u003E\r\n\u003Ch3\u003EUser-facing disclosures\u003C/h3\u003E\r\n\u003Cp\u003ESome transparency artifacts are internal, while others must reach the user. A disclosure can tell someone they are interacting with a chatbot, that an image or video was generated or altered by AI, or that a system uses emotion recognition or biometric categorization.\u003C/p\u003E\r\n\u003Cp\u003EFor generated media, visible labeling can be paired with machine-readable provenance. The Coalition for Content Provenance and Authenticity (\u003Ca rel=\"noopener noreferrer\" href=\"https://c2pa.org/\" target=\"_blank\"\u003EC2PA\u003C/a\u003E) provides an open technical standard for establishing the origin and edit history of digital content, and its \u003Ca rel=\"noopener noreferrer\" href=\"https://spec.c2pa.org/specifications/specifications/2.4/explainer/Explainer.html\" target=\"_blank\"\u003EContent Credentials\u003C/a\u003E approach records provenance information in a cryptographically bound structure.\u003C/p\u003E\r\n\u003Cp\u003EDisclosure should be designed into the system, not tacked on at the end. A chatbot disclosure, a synthetic content label and a high-risk decision notice each serve a different audience and should appear at the moment when the information changes how someone interprets the system.\u003C/p\u003E\r\n\u003Ch3\u003ETraining-data summaries\u003C/h3\u003E\r\n\u003Cp\u003ETraining-data transparency is becoming more formal for general-purpose AI model providers. The EU AI Act requires providers of general-purpose AI models to maintain technical documentation, and the European Commission has described a public summary of training content for general-purpose AI models under Article 53.\u003C/p\u003E\r\n\u003Cp\u003EFor organizations that use third-party foundation models, this creates a downstream procurement and governance issue. Teams may not have direct access to full training data, but they can require provider-supplied documentation, summaries, acceptable use policies, known limitation statements and update notices as part of vendor review.\u003C/p\u003E\r\n\u003Cp\u003EThe same principle applies to internally developed models. A training-data summary should enumerate categories of data, source systems, collection windows, preprocessing steps, exclusions, known gaps and relevant rights or restrictions.\u003C/p\u003E\r\n\u003Ch3\u003EAudit logs and access history\u003C/h3\u003E\r\n\u003Cp\u003EDocumentation records design intent. An audit trail records what the system actually did: model versions, prompt and response pairs, tool calls, retrieval sources, user access, policy decisions and data movement. For incident response, that runtime record can be the difference between a vague concern and a specific reconstruction — which model version ran, which context was retrieved, which policy applied, which user accessed which output and which downstream application consumed it.\u003C/p\u003E\r\n\u003Cp\u003EIn Snowflake, for example, the\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/account-usage/access_history\"\u003E ACCESS_HISTORY view\u003C/a\u003E can be used to query access history for Snowflake objects — including tables, views and columns — within a 365-day period. Snowflake’s lineage capabilities retain object and column lineage history for one year. These records do not replace model documentation, but they give governance and security teams evidence they can inspect when an AI workload uses governed data.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_478476658":{"id":"text-552853d71e","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EQUICK TIP\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EAttach transparency artifacts directly to the objects teams already manage, such as model versions, training data sets, system prompts, access policies, lineage records and audit logs.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_v2":{"id":"title-v2-57ad5f5742","additionalClasses":"anchor-title anchor-title--ai-transparency-vs-explainability-vs-traceability","type":"heading2","lines":["AI transparency vs. explainability vs. traceability"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_ai-transparency-vs-explainability-vs-traceability_0":{"id":"text-bfdde6f14e","text":"\u003Cp\u003E\u003Cu\u003E\u003C/u\u003EAI transparency, explainability, and traceability overlap, but they are not interchangeable.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ETransparency\u003C/b\u003E is system-level disclosure. It describes the AI system’s purpose, owner, intended use, limits, documentation and user-facing notices. A model card, data sheet, training-data summary and chatbot disclosure are all transparency artifacts because they help people understand what the system is and how it should be used.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EExplainability\u003C/b\u003E is decision-level reasoning. It helps someone understand why a model produced a specific output, score or recommendation. In a predictive model, explainability may involve feature attribution, counterfactual examples or post hoc explanation methods. In a generative AI system, it may involve retrieved context, cited sources, prompt traces or reasoning summaries that help a reviewer evaluate how the response was produced.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003ETraceability\u003C/a\u003E\u003C/b\u003E is the lineage from data through model to output. It connects a feature to a source column, a model version to a training run, a prompt response to a retrieval set or a generated recommendation to the workflow that consumed it. Traceability matters because transparency without runtime evidence can become static documentation, while explainability without lineage can account for an output without establishing whether the underlying data was appropriate.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EA mature \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003EAI governance\u003C/a\u003E program needs all three. Transparency describes what the system is and how it should be used. Explainability covers why specific outputs were produced. Traceability connects those outputs back to the data, model versions and prompt configurations that shaped them — and becomes essential when an output needs to be reconstructed or challenged.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1870354977":{"id":"title-v2-2b19beb774","additionalClasses":"anchor-title anchor-title--common-transparency-gaps-and-how-to-close-them","type":"heading2","lines":["Common transparency gaps and how to close them"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_118447583":{"id":"text-e398e0f183","text":"\u003Cp\u003EThe most common AI transparency gaps appear when models, data and applications move faster than the documentation and governance processes around them.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003ETransparency Gap\u003C/th\u003E\r\n\u003Cth\u003EWhat It Looks Like\u003C/th\u003E\r\n\u003Cth\u003EHow to Close It\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EFoundation models used without provider documentation\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E\u003Ci\u003E\u003C/i\u003ETeams adopt a model through an API or embedded application, but have limited information about training context, evaluation, intended use or restrictions.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003ERequire model cards, training-data summaries, acceptable use policies, evaluation results and update notices contractually.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EGenerative outputs without AI disclosure\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EUsers receive generated text, images, summaries or recommendations without knowing AI was involved.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EDeploy system-level labeling and visible user-facing disclosures, supported by machine-readable provenance where relevant.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EBlack box scoring with no explainability layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EA score affects review, prioritization or routing, but reviewers cannot see which factors shaped the output.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EAdd post hoc explanation methods, document the explanation method and log inputs and outputs alongside each decision.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EOpaque training data pipelines\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003ETeams know which model is in production, but not which source tables, features, transformations or data versions shaped it.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EAdopt data lineage tooling that traces features and training data back to source systems, owners and refresh patterns.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EStatic documentation that drifts from runtime behavior\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EA model card exists, but model versions, prompts, retrieval sources or guardrails change without review.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003ETie documentation to release workflows, model registries, prompt management, access history and audit logs.\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_1205645823":{"id":"title-v2-f678604b8a","additionalClasses":"anchor-title anchor-title--ai-transparency-on-snowflake","type":"heading2","lines":["AI transparency on Snowflake"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_870755485":{"id":"text-b1c044d622","text":"\u003Cp\u003EAI transparency depends on metadata, lineage, access records and governance controls that stay close to the data and workloads they describe. On Snowflake, several capabilities support that foundation across model development, governed data access and runtime inspection.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake Model Registry stores and manages model versions, model metrics and model metadata, while also supporting model lifecycle management, model access controls and performance monitoring through Snowflake ML Observability. The registry is a practical anchor for model card documentation because model records can capture version, metrics, owner, signatures and the metadata needed to document an approved release.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E gives teams one place to find data resources with consistent metadata across Snowflake data, Apache Iceberg data, external relational sources, and BI tools. For AI transparency, catalog context helps teams see which data products are approved, who owns them, how they are classified and how they relate to downstream AI workloads.\u003C/p\u003E\r\n\u003Cp\u003EAccess history and lineage add the runtime evidence layer. Access History can show which Snowflake objects were accessed, including table, view and column references, while lineage helps teams inspect object and column relationships.\u003C/p\u003E\r\n\u003Cp\u003EFor agentic and generative AI workloads, runtime monitoring extends that evidence further. Snowflake Cortex Agents can log detailed traces of conversations for auditing and debugging, including conversation history, planning, tool selection, execution results and final response generation. Snowflake Cortex AI Guardrails documentation recommends reviewing guardrail logs to identify patterns in flagged prompts.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://trust.snowflake.com/\"\u003ESnowflake’s Compliance Center\u003C/a\u003E helps teams evaluate, monitor and reduce potential security risks in Snowflake accounts based on scanner findings and recommendations. AI governance does not sit apart from security, privacy and access governance. The same account controls that protect data help establish whether an AI workload is operating in a governed environment.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EHear what two top Snowflake executives, EVP of Product Christian Kleinerman and Principal Data Strategist Jennifer Belissent, have to say about generative AI and ethics:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube":{"id":"embed-3b4dacce09","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_v2_248983382":{"id":"title-v2-25d99499b4","additionalClasses":"anchor-title anchor-title--make-ai-systems-legible-before-they-become-consequential","type":"heading2","lines":["Make AI systems legible before they become consequential"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_793426992":{"id":"text-b15ef0ff25","text":"\u003Cp\u003EAI transparency is easiest to build before a system becomes widely used. Once a model is embedded in a workflow, a chatbot is answering customers or a generated summary is shaping a decision, missing documentation becomes harder to reconstruct and harder to trust.\u003C/p\u003E\r\n\u003Cp\u003EThe practical path is to attach transparency to the objects teams already manage: the model version, the training data set, the system prompt, the access policy, the lineage edge, the disclosure surface and the audit log. When those records stay connected, the evidence is there before it’s needed — available to a developer reviewing a release, a user relying on an output or a governance team explaining the system’s behavior.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_2114659006":{"id":"text-79c7a288ba","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EKEY TAKEAWAY\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EAI transparency is less about explaining every model parameter and more about making the whole system legible: its purpose, data, limits, disclosures, lineage and audit evidence.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["text","text_intro_0","title_what-is-ai-transparency","text_what-is-ai-transparency_0","title_why-ai-transparency-matters","text_why-ai-transparency-matters_0","text_9978331","title_what-transparency-looks-like-in-practice","text_what-transparency-looks-like-in-practice_0","text_478476658","title_v2","text_ai-transparency-vs-explainability-vs-traceability_0","title_v2_1870354977","text_118447583","title_v2_1205645823","text_870755485","youtube","title_v2_248983382","text_793426992","text_2114659006"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","id":"hub-body-aside","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sticky-sidebar","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-770fa3ff15","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-c5e154ff48","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EIn This Guide\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-162c86c630","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"what-is-ai-transparency\"\u003EWhat is AI transparency?\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-ai-transparency-matters\"\u003EWhy AI transparency matters\u003C/li\u003E\r\n\u003Cli data-anchor=\"what-transparency-looks-like-in-practice\"\u003EWhat transparency looks like in practice\u003C/li\u003E\r\n\u003Cli data-anchor=\"ai-transparency-vs-explainability-vs-traceability\"\u003EAI transparency vs. explainability vs. traceability\u003C/li\u003E\r\n\u003Cli data-anchor=\"common-transparency-gaps-and-how-to-close-them\"\u003ECommon transparency gaps and how to close them\u003C/li\u003E\r\n\u003Cli data-anchor=\"ai-transparency-on-snowflake\"\u003EAI transparency on Snowflake\u003C/li\u003E\r\n\u003Cli data-anchor=\"make-ai-systems-legible-before-they-become-consequential\"\u003EMake AI systems legible before they become consequential\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","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"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_1786318617":{"id":"flexible-column-container-0dea93d466","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":{"id":"hub-faq-intro","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy":{"id":"title-v2-4dc893a798","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently 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