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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/ai-transparency/\"\u003EAI Transparency\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/responsible-ai/\"\u003EResponsible 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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAI COMPLIANCE DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAI compliance is the practice of making AI systems accountable by linking their use to clear requirements, documented controls and verifiable records. It ensures organizations can demonstrate that their AI operates within legal, regulatory and organizational expectations.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-74c7be2cc1","text":"\u003Cp\u003EAI compliance starts with a deceptively simple question: For this model, assistant, scoring system or automated workflow, what rules apply, and what evidence proves those rules were followed?\u003C/p\u003E\n\u003Cp\u003EThat question is becoming harder to answer as AI moves into credit decisions, clinical workflows, employee screening, fraud detection, customer support and internal productivity tools. A model may rely on training data from one system, customer records from another, outputs from a foundation model provider and a human approval step that happens in a separate application. And each part of that chain can create a regulatory obligation, an internal policy requirement or an audit question that has to be answered later.\u003C/p\u003E\n\u003Cp\u003EAI compliance programs give organizations a way to manage that complexity before an audit or incident review forces the issue. They connect laws and standards to controls, map those controls to evidence, and keep the evidence current as models, data, vendors and use cases change.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-compliance":{"id":"title-v2-1573854632","additionalClasses":"anchor-title anchor-title--what-is-ai-compliance","type":"heading2","lines":["What is AI compliance?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-compliance_0":{"id":"text-d6dbfd71f0","text":"\u003Cp\u003EAI compliance is the discipline of ensuring that \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI systems\u003C/a\u003E, and the lifecycle used to develop and operate them, conform to applicable laws, regulations, industry standards and internal policies. It covers the controls an organization uses to prove that an AI system was designed, tested, deployed, monitored and reviewed in line with its regulatory obligations.\u003C/p\u003E\n\u003Cp\u003EThat makes AI compliance related to, but distinct from, several neighboring disciplines:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003E\u003Cstrong\u003EAI governance\u003C/strong\u003E\u003C/a\u003E defines the operating model — who owns an AI use case, who approves it, who can pause it and how decisions escalate.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003E\u003Cstrong\u003EAI ethics\u003C/strong\u003E\u003C/a\u003E defines the principles — fairness, transparency, accountability, privacy, security and human oversight.\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003E\u003Cstrong\u003EAI risk management\u003C/strong\u003E\u003C/a\u003E defines the methodology — how the organization identifies, measures, prioritizes and mitigates AI-related risk.\u003C/li\u003E\n\u003Cli\u003E\u003Cstrong\u003EAI compliance\u003C/strong\u003E proves conformance — which rules apply, which controls address them and what audit evidence supports control attestation.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EAn AI compliance program usually starts with concrete objects, not abstract principles: an AI inventory, a model card, a training-data summary, a system owner, an access policy, a monitoring dashboard and a record of approvals. These artifacts show whether a system has been classified correctly, whether high-risk AI received the right review, whether human oversight is meaningful and whether post-deployment monitoring is actually happening.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-ai-regulatory-landscape":{"id":"title-v2-e62820c3c8","additionalClasses":"anchor-title anchor-title--the-ai-regulatory-landscape","type":"heading2","lines":["The AI regulatory landscape"],":type":"snowflake-site/components/title-v2"},"text_the-ai-regulatory-landscape_0":{"id":"text-cd98fa61ee","text":"\u003Cp\u003EOrganizations typically face a layered regulatory environment: broad AI laws, privacy rules, sector-specific supervisory guidance, product safety requirements, employment laws, consumer protection rules and internal risk policies.\u003C/p\u003E\r\n\u003Ch3\u003EEU AI Act\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EThe EU AI Act\u003C/a\u003E is widely regarded as one of the most comprehensive AI-specific regulatory frameworks currently in force. It uses a risk-tiered structure that distinguishes prohibited AI practices, high-risk AI systems, limited-risk systems, general-purpose AI models and lower-risk uses. The Act also has extraterritorial reach, meaning it can apply to providers and deployers outside the EU when AI systems or outputs are placed on, or used in, the EU market.\u003C/p\u003E\r\n\u003Cp\u003EThe Act applies in phases, with prohibitions and AI literacy requirements beginning in February 2025, general-purpose AI obligations beginning in August 2025 and a broader rollout continuing through Aug. 2, 2027.\u003C/p\u003E\r\n\u003Cp\u003EFor high-risk AI systems, compliance can require risk management, \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E, technical documentation, logging, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etransparency\u003C/a\u003E, human oversight, accuracy, robustness, cybersecurity and, in some cases, a conformity assessment involving a notified body.\u003C/p\u003E\r\n\u003Cp\u003EOrganizations that build or supply high-risk AI systems typically need a technical file that shows how the system was designed, tested and documented. Organizations that use those systems typically need evidence that demonstrates how the system is deployed, who reviews its outputs, how it’s monitored and how issues are escalated.\u003C/p\u003E\r\n\u003Ch3\u003EGDPR\u003C/h3\u003E\r\n\u003Cp\u003EThe \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://gdpr.eu/\"\u003EGDPR\u003C/a\u003E already applies to many AI systems that process personal data, even when those systems are not regulated under a standalone AI law. Article 22 addresses certain rights related to automated decision-making, including profiling, when decisions produce legal or similarly significant effects. Article 35 requires a data protection impact assessment (DPIA) when processing is likely to result in high risk to individuals, which can include large-scale profiling, sensitive data processing or novel technologies.\u003C/p\u003E\r\n\u003Cp\u003EIn practice, \u003Ca href=\"https://www.snowflake.com/en/data-governance/regulatory-compliance/gdpr/\"\u003EGDPR compliance\u003C/a\u003E often sits beside AI-specific compliance. A bank using AI for credit decisioning may need to evaluate the use case under the EU AI Act’s high-risk provisions, while also completing a DPIA, documenting lawful basis, enforcing access controls and proving that personal data was handled according to purpose limitation, minimization and retention requirements.\u003C/p\u003E\r\n\u003Ch3\u003EU.S. federal and state rules\u003C/h3\u003E\r\n\u003Cp\u003EThe U.S. AI compliance landscape remains a patchwork of laws and regulations. At the federal level, policy has shifted since the 2023 executive order on safe, secure and trustworthy AI. In January 2025, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.federalregister.gov/documents/2025/01/31/2025-02172/removing-barriers-to-american-leadership-in-artificial-intelligence\"\u003EExecutive Order 14179\u003C/a\u003E, “Removing Barriers to American Leadership in Artificial Intelligence,” revoked certain prior AI policies and directed the administration to review and revise actions that conflicted with its AI policy goals.\u003C/p\u003E\r\n\u003Cp\u003EThat federal shift has not removed compliance pressure, however. Instead, organizations still have to manage federal agency guidance, enforcement activity and state-level requirements. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003ENIST’s AI Risk Management Framework\u003C/a\u003E remains a widely used voluntary framework for managing AI risks, and sector regulators continue to apply existing authorities to AI-enabled systems.\u003C/p\u003E\r\n\u003Cp\u003EAt the state and local level, several rules have become important reference points:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EColorado AI Act / ADMT:\u003C/b\u003E \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://leg.colorado.gov/bills/sb24-205\"\u003EColorado’s 2024 AI Act\u003C/a\u003E has been repealed and replaced by \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://leg.colorado.gov/bills/sb26-189\"\u003ESB26-189\u003C/a\u003E, signed on May 14, 2026. The new law narrows the framework to automated decision-making technology (ADMT) used to materially influence consequential decisions, while still addressing \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ealgorithmic bias\u003C/a\u003E concerns through transparency, notice, correction, appeal, and human-review obligations. Covered obligations are generally scheduled to take effect January 1, 2027, with additional implementation details expected through Colorado Attorney General rulemaking.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENYC Local Law 144:\u003C/b\u003E \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.osc.ny.gov/state-agencies/audits/2025/12/02/enforcement-local-law-144-automated-employment-decision-tools\"\u003ENew York City regulates\u003C/a\u003E automated employment decision tools, including bias audit and notice requirements for covered employment uses.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECalifornia automated decision-making rules:\u003C/b\u003E \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://cppa.ca.gov/announcements/2025/20250923.html\"\u003ECalifornia finalized CCPA regulations\u003C/a\u003E covering automated decision-making technology, risk assessments and cybersecurity audits, with the regulations taking effect Jan. 1, 2026, and phased compliance timing for some requirements.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ESector-specific AI rules\u003C/h3\u003E\r\n\u003Cp\u003EAI compliance also depends heavily on industry context. In regulated sectors, AI systems are often reviewed through existing supervisory frameworks rather than AI-only laws.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EIn banking,\u003C/b\u003E model risk management guidance has long shaped how firms validate models, document assumptions and maintain governance. The Federal Reserve’s model risk guidance emphasizes development, implementation, use, validation, controls, compliance and board and senior management oversight; in 2026, federal banking agencies also issued revised model risk management guidance emphasizing a risk-based approach.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIn healthcare,\u003C/b\u003E \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html\"\u003EHIPAA\u003C/a\u003E can apply when AI systems process protected health information, while FDA oversight may apply when AI is part of Software as a Medical Device (SaMD) or an AI-enabled medical device. FDA maintains a list of AI-enabled medical devices authorized for marketing in the U.S., reflecting the growing role of AI in regulated medical products.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIn insurance,\u003C/b\u003E the National Association of Insurance Commissioners’ model bulletin sets expectations for how insurers govern AI systems, and describes the information regulators may request during an investigation or examination.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EIn financial services and public company disclosure,\u003C/b\u003E the SEC has signaled that AI claims must be accurate and supportable. In 2024, the SEC charged two investment advisers with making false and misleading statements about their use of AI, a reminder that AI compliance also includes marketing, disclosure and customer-facing claims.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EInternational AI guidance\u003C/h3\u003E\r\n\u003Cp\u003EOutside the EU and U.S., organizations may also need to account for the U.K.’s principles-based AI regulation approach, China’s generative AI measures and Singapore’s Model AI Governance Framework. Many of these regimes differ in legal force, scope and enforcement maturity, but they tend to converge around recurring expectations: risk classification, accountability, transparency, human oversight, data governance, monitoring and documentation.\u003C/p\u003E\r\n\u003Cp\u003ENonbinding instruments also matter because they often become the reference language behind laws, standards and procurement requirements. The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.oecd.org/en/topics/sub-issues/ai-principles.html\"\u003EOECD AI Principles\u003C/a\u003E promote AI safety through trustworthy AI that respects human rights and democratic values, while \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.unesco.org/en/artificial-intelligence/recommendation-ethics\"\u003EUNESCO’s Recommendation on the Ethics of Artificial Intelligence\u003C/a\u003E applies to all 194 UNESCO member states and emphasizes human rights, fairness, transparency and human oversight.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_frameworks-that-structure-compliance":{"id":"title-v2-58f84dad4d","additionalClasses":"anchor-title anchor-title--frameworks-that-structure-compliance","type":"heading2","lines":["Frameworks that structure compliance"],":type":"snowflake-site/components/title-v2"},"text_frameworks-that-structure-compliance_0":{"id":"text-94efe23eb7","text":"\u003Cp\u003ELaws define obligations, but frameworks help organizations turn those obligations into repeatable operating practices. They give compliance, legal, data, security and AI teams a common way to classify systems, assign controls, collect evidence and attest to control performance.\u003C/p\u003E\r\n\u003Ch3\u003ENIST AI RMF\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003EThe NIST AI Risk Management Framework\u003C/a\u003E is a voluntary framework for managing risks to individuals, organizations and society associated with AI. NIST organizes the framework around four core functions: Govern, Map, Measure and Manage.\u003C/p\u003E\r\n\u003Cp\u003EFor compliance teams, the NIST AI RMF is useful because it creates a risk-management structure that can be mapped to legal and internal obligations.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EGovern\u003C/b\u003E defines roles, policies and accountability.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EMap\u003C/b\u003E captures context and intended use.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EMeasure\u003C/b\u003E evaluates risks and system performance\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EManage\u003C/b\u003E prioritizes responses, monitoring and remediation.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EISO/IEC 42001\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO/IEC 42001\u003C/a\u003E is a certifiable AI management system standard. ISO describes it as the world’s first AI management system standard, designed to help organizations manage AI-related risks and opportunities through a structured system of policies, processes, roles and continual improvement.\u003C/p\u003E\r\n\u003Cp\u003EFor AI compliance, ISO/IEC 42001 is especially useful because it supports formal management-system evidence: scope, governance, risk assessment, impact assessment, lifecycle controls, supplier management, monitoring, internal audit and management review. Organizations that need a certifiable framework often pair ISO/IEC 42001 with more detailed risk or sector guidance.\u003C/p\u003E\r\n\u003Ch3\u003EISO/IEC 23894\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.iso.org/standard/77304.html\"\u003EISO/IEC 23894\u003C/a\u003E provides guidance for AI risk management. It helps organizations that develop, produce, deploy or use AI systems integrate AI-specific risk management into their activities and functions.\u003C/p\u003E\r\n\u003Cp\u003EUnlike ISO/IEC 42001, which specifies requirements for an AI management system, ISO/IEC 23894 is guidance-oriented. It can help teams define risk sources, analyze potential impacts and structure risk treatment across the AI lifecycle.\u003C/p\u003E\r\n\u003Ch3\u003EIEEE 7000 series\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://standards.ieee.org/ieee/7000/6781/\"\u003EThe IEEE 7000 series\u003C/a\u003E provides standards focused on ethical and responsible technology design. In an AI compliance program, these standards can help structure topics such as transparency, bias, accountability, privacy and human values. They are typically not the only basis for compliance, but they can help translate ethical commitments into design and documentation practices.\u003C/p\u003E\r\n\u003Ch3\u003EOECD and UNESCO\u003C/h3\u003E\r\n\u003Cp\u003EThe OECD AI Principles and UNESCO Recommendation are nonbinding, but they influence law, policy and regulator expectations. They are useful as normative references when organizations need to explain why internal AI policies emphasize fairness, transparency, accountability, human oversight and respect for rights, even where no single binding AI law applies.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003ELearn more about responsible AI\u003C/a\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-core-ai-compliance-controls":{"id":"title-v2-aac58335aa","additionalClasses":"anchor-title anchor-title--the-core-ai-compliance-controls","type":"heading2","lines":["The core AI compliance controls"],":type":"snowflake-site/components/title-v2"},"text_the-core-ai-compliance-controls_0":{"id":"text-f7c9d9d385","text":"\u003Cp\u003EAI compliance controls provide a way for organizations to implement frameworks and compliance practices into day-to-day workflows and systems. A policy may say that high-risk AI requires human oversight, but a control specifies who reviews the system, where that approval is recorded, how the reviewer sees relevant model outputs and what happens when monitoring shows drift, bias or unsafe behavior.\u003C/p\u003E\n\u003Ch3\u003EGovernance controls\u003C/h3\u003E\n\u003Cp\u003EGovernance controls establish who is accountable for AI systems and how decisions are made. They typically include executive oversight, an AI policy, risk-tiering criteria, role assignments and escalation paths.\u003C/p\u003E\n\u003Cp\u003EA high-risk credit model, for example, should have a named model owner, business owner, technical owner and compliance reviewer. The organization should be able to show who approved the use case, which risk tier was assigned, which control set applied and who can require remediation, suspension or rollback.\u003C/p\u003E\n\u003Cp\u003ECommon governance controls include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EAI policy and acceptable-use standards\u003C/li\u003E\n\u003Cli\u003EAI inventory ownership\u003C/li\u003E\n\u003Cli\u003ERisk-tiering methodology\u003C/li\u003E\n\u003Cli\u003EAI officer, model owner and data owner assignments\u003C/li\u003E\n\u003Cli\u003EReview board or governance council approval\u003C/li\u003E\n\u003Cli\u003EException management with expiration dates\u003C/li\u003E\n\u003Cli\u003EControl attestation and internal audit review\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EDocumentation controls\u003C/h3\u003E\n\u003Cp\u003EDocumentation controls create the written record that regulators, auditors, customers and internal reviewers can inspect. In AI compliance, documentation is not a onetime launch task; it has to follow the system as data changes, model versions update, vendors change and use cases expand.\u003C/p\u003E\n\u003Cp\u003ETypical documentation artifacts include model cards, data sheets, training-data summaries, evaluation results, system documentation, architectural diagrams and technical files. Under the EU AI Act, Annex IV describes technical documentation expectations for high-risk AI systems, which can include system purpose, design, development, data, monitoring, validation and risk management information.\u003C/p\u003E\n\u003Cp\u003EFor \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003Egenerative AI systems\u003C/a\u003E, documentation may also need to cover prompts, retrieval sources, safety filters, human review, prohibited uses and vendor attestations. The goal is not to produce more paperwork for its own sake, but to preserve the context needed to understand what the system does, how it was tested and what evidence supports its approved use.\u003C/p\u003E\n\u003Ch3\u003EAssessment controls\u003C/h3\u003E\n\u003Cp\u003EAssessment controls evaluate whether an AI system is appropriate for its intended use. These controls may include AI impact assessments, DPIAs, bias audits, red-teaming reports, adversarial testing, privacy reviews, AI security reviews and pre-deployment testing.\u003C/p\u003E\n\u003Cp\u003EA hiring-screening tool, for example, may require a bias audit and candidate notice under one law, a DPIA under another, and an internal assessment that evaluates job relevance, explainability, vendor documentation and human review. A clinical AI system may require validation against intended use, patient safety review and monitoring for performance changes across populations.\u003C/p\u003E\n\u003Cp\u003EAssessment controls should produce durable evidence: test results, reviewer notes, remediation plans, approval records and any unresolved risk accepted by accountable owners.\u003C/p\u003E\n\u003Ch3\u003EOperational controls\u003C/h3\u003E\n\u003Cp\u003EOperational controls govern how AI systems behave after deployment. They include access management, audit logs, monitoring, incident reporting, human oversight and post-market monitoring.\u003C/p\u003E\n\u003Cp\u003EThese controls matter because AI compliance does not end when a model goes live. A model can drift, a prompt template can change, a foundation model provider can update an underlying model, a data pipeline can begin feeding different values or an access policy can expose sensitive fields to a broader group of users. Compliance depends on seeing those changes and preserving the evidence needed to explain them.\u003C/p\u003E\n\u003Cp\u003ECommon operational controls include:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003ERole-based access control\u003C/a\u003E (RBAC)\u003C/li\u003E\n\u003Cli\u003EAudit logs for data, model and application activity\u003C/li\u003E\n\u003Cli\u003EModel performance and drift monitoring\u003C/li\u003E\n\u003Cli\u003EPrompt and response logging where appropriate\u003C/li\u003E\n\u003Cli\u003EHuman-in-the-loop review for consequential decisions\u003C/li\u003E\n\u003Cli\u003EIncident reporting and escalation procedures\u003C/li\u003E\n\u003Cli\u003EPost-market or post-deployment monitoring\u003C/li\u003E\n\u003Cli\u003EPeriodic control attestation\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EThird-party controls\u003C/h3\u003E\n\u003Cp\u003EMany AI systems depend on vendors, foundation model providers, SaaS tools, data providers or embedded AI capabilities that the organization did not build. Third-party controls help teams assess those dependencies before deployment and monitor them over time.\u003C/p\u003E\n\u003Cp\u003EVendor due diligence should capture intended use, data handling, model documentation, security controls, subcontractors, retention policies, training-data commitments, incident notification terms and any available attestations. For foundation models, organizations may also request provider documentation, safety evaluations and information that supports an AI bill of materials (AIBOM), which enumerates the models, data sources, libraries, APIs and vendors involved in an AI system.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_best-practices-for-ai-compliance":{"id":"title-v2-2b705d95ca","additionalClasses":"anchor-title anchor-title--best-practices-for-ai-compliance","type":"heading2","lines":["Best practices for AI compliance"],":type":"snowflake-site/components/title-v2"},"text_best-practices-for-ai-compliance_0":{"id":"text-b444eb1765","text":"\u003Cp\u003EAI compliance is only manageable when teams implement \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-traceability/\"\u003Etraceability\u003C/a\u003E: each AI system maps to a risk tier, each risk tier maps to obligations, each obligation maps to controls, and each control maps to evidence.\u003C/p\u003E\n\u003Ch3\u003EInventory every AI system\u003C/h3\u003E\n\u003Cp\u003EAn AI inventory should include traditional ML models, generative AI applications, internal assistants, embedded AI in SaaS platforms and vendor-provided AI capabilities. Teams cannot classify, monitor or attest to systems they have not mapped.\u003C/p\u003E\n\u003Cp\u003EA useful AI inventory typically captures the system name, owner, business purpose, users, data sources, model type, vendor dependencies, jurisdictions, risk tier, affected populations, approval status and review cadence. For embedded AI, the inventory should also record which SaaS feature uses AI, what data it processes and whether the vendor uses customer data for training or improvement.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_best-practices-for-ai-compliance_0":{"id":"text-72a5d8e4f8","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EStart with an AI inventory before tackling regulatory requirements. Most compliance gaps stem from not knowing which models, assistants, vendors or AI-enabled applications are already in use across the organization.\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-1290548073","text":"\u003Ch3\u003EClassify systems by risk tier\u003C/h3\u003E\n\u003Cp\u003EEach system should be classified against applicable legal and internal criteria. Under the EU AI Act, that may mean determining whether a use case is prohibited, high risk, limited risk or lower risk. Under internal policy, it may mean classifying systems by potential impact on rights, safety, financial outcomes, employment, healthcare, access to services or regulatory exposure.\u003C/p\u003E\n\u003Cp\u003ERisk tiering should be evidence-based. A chatbot answering general policy questions does not create the same obligations as a model recommending patient treatment or ranking candidates for employment. The classification record should explain the reasoning, not merely assign a label.\u003C/p\u003E\n\u003Ch3\u003EMap obligations to controls and evidence\u003C/h3\u003E\n\u003Cp\u003EOnce teams identify the applicable obligations, they need a control map. For each obligation, the map should identify the relevant control, control owner, evidence source and review frequency.\u003C/p\u003E\n\u003Cp\u003EFor example:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EObligation\u003C/th\u003E\u003Cth\u003EControl\u003C/th\u003E\u003Cth\u003EEvidence\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EHuman oversight for high-risk AI\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ENamed reviewer approves consequential outputs before final action\u003C/td\u003E\u003Ctd\u003EReview workflow logs, approval records, role assignments\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EDPIA for high-risk personal data processing\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EPrivacy team completes and approves DPIA before deployment\u003C/td\u003E\u003Ctd\u003EDPIA document, approval date, remediation actions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003ETechnical documentation\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EModel owner maintains model card and technical file\u003C/td\u003E\u003Ctd\u003EModel card, data summary, evaluation metrics, version history\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EAccess limitation\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003ESensitive data access restricted by role and policy\u003C/td\u003E\u003Ctd\u003EAccess policy, access history, audit logs\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cstrong\u003EPost-deployment monitoring\u003C/strong\u003E\u003C/td\u003E\u003Ctd\u003EModel performance and drift reviewed quarterly\u003C/td\u003E\u003Ctd\u003EMonitoring dashboards, review notes, incident records\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Ch3\u003EAutomate evidence collection\u003C/h3\u003E\n\u003Cp\u003EManual evidence collection breaks down as the number of AI systems grows. High-risk systems may require quarterly reviews, annual reassessments, incident reporting and ongoing monitoring; even limited-risk systems may need transparency notices, access records and documentation updates.\u003C/p\u003E\n\u003Cp\u003EEvidence automation helps by capturing signals directly from the systems where AI work happens: access history, lineage, model registry metadata, monitoring dashboards, approval workflows, incident systems and policy engines. The goal is to reduce the gap between actual system behavior and the compliance record. When an auditor asks which data a model accessed, who changed a policy or which model version was deployed during an incident window, the evidence should already exist.\u003C/p\u003E\n\u003Ch3\u003EEstablish a review cadence\u003C/h3\u003E\n\u003Cp\u003EAI systems should be reviewed on a cadence that reflects their risk. High-risk systems often warrant quarterly review, especially when they affect rights, safety, eligibility, pricing, employment or regulated decisions. Limited-risk systems may be reviewed annually unless a major change occurs.\u003C/p\u003E\n\u003Cp\u003EReviews should evaluate whether the system’s purpose, data, model, vendor, user group, jurisdiction or risk profile has changed. A use case that began as an internal productivity assistant can become a customer-facing support tool; a model trained for one region can be reused in another; a SaaS feature can add generative AI without a full procurement cycle. A review cadence helps catch those changes before the compliance record becomes stale.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_best-practices-for-ai-compliance_1":{"id":"text-2be2448242","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMany organizations treat AI compliance as a onetime approval exercise. In reality, compliance can quickly break down when models, data sources, vendors or use cases change without corresponding updates to documentation, risk assessments and monitoring controls.\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-v2-5b8b9c7bba","additionalClasses":"anchor-title anchor-title--ai-compliance-on-snowflake","type":"heading2","lines":["AI compliance on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_ai-compliance-on-snowflake_0":{"id":"text-14d85dce9a","text":"\u003Cp\u003EAI compliance depends on evidence: which data was used, who accessed it, how it moved, which model version was deployed, what policies applied and which controls operated at the time of review.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E supports governance, discovery and compliance workflows by surfacing context such as access history, lineage, classification and policy enforcement. Snowflake’s ACCESS_HISTORY view provides a record of what data was accessed, when access occurred and how data moved from source objects to target objects, which can help teams produce audit evidence for access, lineage and data-use controls.\u003C/p\u003E\n\u003Cp\u003EFor ML systems, Snowflake ML Model Registry stores and manages model versions, model metrics and model metadata, supports model lifecycle management and can monitor model performance and drift through Snowflake ML Observability. It also supports RBAC for model access, helping teams connect model governance to the same kinds of controls they use for data access.\u003C/p\u003E\n\u003Cp\u003EFor generative AI use cases, Cortex Guard is designed to help identify and filter potentially unsafe or harmful responses before model output returns to the application. Depending on the architecture, teams can pair these safeguards with prompt, response, access and application logs to support generative AI audit trails.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://trust.snowflake.com/\"\u003ESnowflake Compliance Center\u003C/a\u003E gives customers access to security and compliance resources, including certifications and reports that can support vendor due diligence and customer or regulator audits. Snowflake has also achieved ISO/IEC 42001 certification for its AI management system, subject to ongoing surveillance audits, which provides a concrete example of how AI governance, AI risk management and compliance practices can be organized under a certifiable management-system standard.\u003C/p\u003E\n\u003Cp\u003ETogether, these capabilities help organizations attach compliance evidence to the systems that produce it: lineage paths, access logs, model metadata, evaluation metrics, policy enforcement records and control artifacts. That does not eliminate the need for legal review, risk classification or human accountability, but it can reduce the manual work required to prove that AI controls are operating.\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-v2-8a53f47a09","additionalClasses":"anchor-title anchor-title--building-ai-compliance-into-the-ai-lifecycle","type":"heading2","lines":["Building AI compliance into the AI lifecycle"],":type":"snowflake-site/components/title-v2"},"text_building-ai-compliance-into-the-ai-lifecycle_0":{"id":"text-b0b5218c86","text":"\u003Cp\u003EAI compliance is easiest to prove when it’s built into the lifecycle. An AI system should enter the inventory when the use case is proposed, receive a risk tier before development proceeds, carry documentation through testing and deployment, and preserve evidence as users, data, models and vendors change.\u003C/p\u003E\n\u003Cp\u003EThat operating model matters because AI systems rarely stay still. Compliance teams can only manage that movement if they can trace the system from obligation to control to evidence.\u003C/p\u003E\n\u003Cp\u003EThe organizations that handle AI compliance well will not be the ones with the most robust policy documents. They will be the ones that can answer specific questions quickly, such as which AI systems are in use, which are high risk, which data they touch, which controls apply, when they were last reviewed and what evidence proves conformance.\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-03216cd1dd","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EEffective AI compliance is less about creating policies and more about maintaining proof. Organizations that can consistently connect AI systems to their risks, controls and evidence will be best positioned to satisfy regulators, auditors, customers and internal stakeholders as AI adoption expands.\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"]},"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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-39cff83b2e",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-ad56b2dcdb","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-7fbe52f584","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-ai-compliance\"\u003EWhat is AI compliance?\u003C/li\u003E\u003Cli data-anchor=\"the-ai-regulatory-landscape\"\u003EThe AI regulatory landscape\u003C/li\u003E\u003Cli data-anchor=\"frameworks-that-structure-compliance\"\u003EFrameworks that structure compliance\u003C/li\u003E\u003Cli data-anchor=\"the-core-ai-compliance-controls\"\u003EThe core AI compliance controls\u003C/li\u003E\u003Cli data-anchor=\"best-practices-for-ai-compliance\"\u003EBest practices for AI compliance\u003C/li\u003E\u003Cli data-anchor=\"ai-compliance-on-snowflake\"\u003EAI compliance on Snowflake\u003C/li\u003E\u003Cli data-anchor=\"building-ai-compliance-into-the-ai-lifecycle\"\u003EBuilding AI compliance into the AI lifecycle\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"]}},":itemsOrder":["container"]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_1786318617":{"id":"flexible-column-container-85e1c285df","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","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-a8ab4d8f52","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Frequently Asked Questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-434d9603e9","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003EYour common questions about AI compliance, answered by Snowflake experts.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"]},"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-2991eda782","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"Is AI compliance legally required?","richText":"\u003Cp\u003EIn many cases, yes. The EU AI Act imposes binding obligations for covered AI systems, while GDPR, U.S. state laws, employment rules, sector-specific regulations and consumer protection laws may apply depending on where and how an AI system is used.\u003C/p\u003E"},{"title":"What’s the difference between AI compliance and AI governance?","richText":"\u003Cp\u003EAI governance is the operating model: who decides, who approves, who monitors and who can intervene. AI compliance is the conformance discipline: proving that the organization met applicable laws, standards and internal policies.\u003C/p\u003E"},{"title":"Which AI compliance framework should my organization adopt?","richText":"\u003Cp\u003EMost enterprises use more than one. NIST AI RMF is useful for risk methodology, ISO/IEC 42001 provides a certifiable AI management system, and ISO/IEC 23894 offers AI-specific risk management guidance. Sector-regulated organizations should also map AI controls to applicable supervisory guidance, such as model risk management guidance for banks or FDA expectations for AI-enabled medical devices.\u003C/p\u003E"},{"title":"How do I prove AI compliance?","richText":"\u003Cp\u003ETeams can prove AI compliance through an evidence trail: documented policies, system inventories, risk classifications, impact assessments, DPIAs, model cards, technical files, access logs, lineage records, monitoring dashboards, incident records and third-party attestations. Each artifact should map to a specific obligation and control.\u003C/p\u003E"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"]},"isActiveTOC":false,"isBlogPage":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1467213961":{"id":"flexible-column-container-14470cbbf9","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-97dbe6bfd7",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2":{"id":"title-v2-8e689cedaa","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Explore AI 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