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border-top","layout":"SIMPLE","id":"container-66b7bbab7d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_894059747":{"id":"text-6bca45a3ab","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-12510e582e","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/\"\u003EAI Governance\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 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-cb97ced8d8","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":{"text":{"id":"text-4f92c3856f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EAI RISK MANAGEMENT DEFINED\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EAI risk management is the process of identifying, assessing, reducing and monitoring risks that may arise from the design, deployment or use of AI systems — including risks related to accuracy, bias, security, privacy, compliance, transparency and business impact.\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-2a64fafb4f","text":"\u003Cp\u003EAI risk management starts with a practical question: What could go wrong when a model, data pipeline, application, prompt, workflow or human decision relies on AI? The answer is rarely limited to model accuracy. A system might use stale data, expose sensitive fields through retrieval, generate a plausible but wrong answer, drift after deployment, or produce a decision that is difficult to explain when a regulator asks for evidence.\u003C/p\u003E\r\n\u003Cp\u003EBecause the risks are so wide-ranging, AI risk management has become a lifecycle concern. It gives organizations a structured way to understand where \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI systems\u003C/a\u003E can create harm, decide which risks are acceptable and apply controls that keep those risks within defined limits. It also helps teams decide what level of residual risk is acceptable, what controls are needed before release and what monitoring should continue after the system reaches users.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what_is_ai_ethics":{"id":"title-v2-1e3cb564d2","additionalClasses":"anchor-title anchor-title--what-is-ai-risk-management","type":"heading2","lines":["What is AI risk management?"],":type":"snowflake-site/components/title-v2"},"text_intro_0_copy":{"id":"text-b66bd951dd","text":"\u003Cp\u003EAI risk management is the process of identifying, assessing, mitigating and monitoring risks from AI systems across the full AI lifecycle — through development, deployment and in production. It covers the model, but it also extends to the data the model uses, the application context in which it runs, the people affected by its outputs, the policies that govern its use and the evidence needed to prove controls were applied.\u003C/p\u003E\r\n\u003Cp\u003EThis makes AI risk management broader than model risk management. Model risk management, especially in banking and other regulated financial settings, focuses on the risk that a model may produce incorrect, misused or misunderstood results. AI risk management includes that concern, but it also accounts for the full system around the model: training data provenance, retrieval context, access controls, user interaction, prompt behavior, downstream workflow impact, incident response and post-deployment monitoring.\u003C/p\u003E\r\n\u003Cp\u003EIn practice, an AI risk management program should answer operational questions such as:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003EWhat data sets were used to train, test or ground the system?\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EWho owns the use case, the model and the affected business process?\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EWhat risks were identified during development, testing and deployment?\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EWhich controls reduce those risks, and what residual risk remains?\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EHow will the organization detect drift, misuse, security events or harmful outcomes after release?\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EThose answers turn AI risk from an abstract concern into a set of reviewable decisions, controls and audit trails.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the_foundational_principles_of_ai_ethics":{"id":"title-v2-3a9c6d279e","additionalClasses":"anchor-title anchor-title--why-ai-risk-management-is-different","type":"heading2","lines":["Why AI risk management is different"],":type":"snowflake-site/components/title-v2"},"text_the_foundational_principles_of_ai_ethics_0":{"id":"text-bd38272973","text":"\u003Cp\u003EAI systems introduce risks that traditional enterprise risk management programs were not designed to evaluate on their own. A rules-based application typically behaves the same way when it receives the same input. AI systems, especially \u003Ca href=\"https://www.snowflake.com/en/fundamentals/generative-ai/\"\u003Egenerative AI systems\u003C/a\u003E, can be non-deterministic — the same prompt may produce different responses, and the system’s behavior may change as models, data, prompts or retrieval context change.\u003C/p\u003E\r\n\u003Cp\u003EThe risk also comes from more than one layer. A model can be technically sound and still cause harm if it’s trained on biased data, connected to sensitive records without appropriate access controls, used outside its intended purpose or deployed into a workflow where users over-rely on its outputs. A fraud model, for example, might perform well in validation but degrade when transaction patterns shift. Or a retrieval-augmented application might expose restricted information if its access logic does not align with the user’s permissions.\u003C/p\u003E\r\n\u003Cp\u003EAI risk also evolves after deployment. Data drift, adversarial inputs, prompt injection, changing regulations, vendor updates and new usage patterns can alter the risk profile of a system that passed its original review. For that reason, AI risk management cannot stop at sign-off. It needs monitoring, escalation paths, retraining triggers, incident reporting and decommissioning criteria.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_modern_ai_specific_ethical_principles":{"id":"title-v2-43bbec0bd1","additionalClasses":"anchor-title anchor-title--categories-of-ai-risk","type":"heading2","lines":["Categories of AI risk"],":type":"snowflake-site/components/title-v2"},"text_modern_ai_specific_ethical_principles_0":{"id":"text-1ba940b25c","text":"\u003Cp\u003EAI risk categories help separate the source of a risk from the symptom it produces. The categories below give teams a way to trace those risks to the right control, owner and review process.\u003C/p\u003E\r\n\u003Ch3\u003ETechnical and performance risk\u003C/h3\u003E\r\n\u003Cp\u003ETechnical risk includes accuracy, robustness, reliability, hallucination, model drift and system availability. In generative AI, the most visible failure may be a hallucinated answer, but the underlying cause could be weak retrieval context, poor data provenance, missing guardrails or a workflow that sends an AI-generated response to a customer without review. Risk management depends on whether the organization can test the system against its intended use, define acceptable performance thresholds and detect when outputs no longer meet those thresholds.\u003C/p\u003E\r\n\u003Ch3\u003EData risk\u003C/h3\u003E\r\n\u003Cp\u003EData risk includes data poisoning, leakage, consent gaps, provenance issues, quality problems and bias in training data. A model trained or grounded on poorly documented data can inherit errors that are difficult to trace later. For AI systems that rely on enterprise data, lineage, classification, access history and data quality controls become part of risk management rather than background governance.\u003C/p\u003E\r\n\u003Ch3\u003ESecurity risk\u003C/h3\u003E\r\n\u003Cp\u003EAI security risk includes prompt injection, model inversion, membership inference, model theft, data exfiltration and adversarial manipulation. AI security risks are high-stakes because AI systems often sit between users and sensitive data, tools or workflows. A compromised prompt or manipulated context can influence not only the answer a model generates, but also the action an AI-powered application attempts to take.\u003C/p\u003E\r\n\u003Ch3\u003EEthical and fairness risk\u003C/h3\u003E\r\n\u003Cp\u003EFairness risk, viewed through an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003EAI ethics\u003C/a\u003E lens, could include discriminatory outcomes, dignity harms, lack of transparency and failures of human oversight. These risks often appear at the point where technical systems encounter people: a hiring screening, credit decision, healthcare recommendation or employee productivity tool. Managing fairness risk effectively requires clarity about affected populations, intended use, review rights and escalation paths.\u003C/p\u003E\r\n\u003Ch3\u003ELegal and regulatory risk\u003C/h3\u003E\r\n\u003Cp\u003ELegal and regulatory risk includes nonconformity with AI-specific laws, privacy violations, intellectual property concerns and inadequate documentation. The \u003Ca href=\"https://artificialintelligenceact.eu/\" target=\"_blank\"\u003EEU AI Act\u003C/a\u003E, for example, requires providers of high-risk AI systems to establish, implement, document and maintain a risk management system, and describes that system as a continuous, iterative process across the lifecycle of the AI system. \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-compliance/\"\u003EAI compliance\u003C/a\u003E is a key component of risk management.\u003C/p\u003E\r\n\u003Ch3\u003EOperational risk\u003C/h3\u003E\r\n\u003Cp\u003EOperational risk includes vendor lock-in, cost overruns, unclear ownership, weak incident response and unmanaged dependencies. A team may deploy a useful AI application but struggle to operate it if no one owns monitoring, exception handling, model updates, access reviews or retirement decisions.\u003C/p\u003E\r\n\u003Ch3\u003EReputational risk\u003C/h3\u003E\r\n\u003Cp\u003EReputational risk includes public failures, user backlash and regulator scrutiny. These risks are often downstream effects of other failures, such as biased outputs, unsafe content, exposed data, unexplained decisions or a visible gap between what an organization promised and what its AI system actually did.\u003C/p\u003E\r\n\u003Ch3\u003ESocietal risk\u003C/h3\u003E\r\n\u003Cp\u003ESocietal risk includes labor displacement, democratic harm, concentration of power and broader systemic effects. Not every organization will assess these risks in the same way, but mature AI risk management programs consider whether a system’s impact extends beyond immediate users and direct business outcomes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_493317382":{"id":"text-6a6f838c5c","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EQUICK TIP\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EStart with the highest-impact use cases first. Not every AI system carries the same level of risk, so prioritize governance reviews for applications that affect customers, employees, sensitive data, regulated decisions or critical business operations.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_major_ai_ethics_frameworks":{"id":"title-v2-f208bad074","additionalClasses":"anchor-title anchor-title--nist-ai-risk-management-framework","type":"heading2","lines":["The NIST AI Risk Management Framework"],":type":"snowflake-site/components/title-v2"},"text_major_ai_ethics_frameworks_0":{"id":"text-548ffcb923","text":"\u003Cp\u003E\u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003EThe NIST AI Risk Management Framework\u003C/a\u003E (AI RMF) is a voluntary framework designed to help organizations manage risks to individuals, organizations and society from AI systems. NIST describes the framework as a way to incorporate trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems. NIST also released a \u003Ca href=\"https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence\" target=\"_blank\"\u003EGenerative AI Profile for the AI RMF\u003C/a\u003E in July 2024. The profile is a companion resource for applying the AI RMF to risks that are novel to or amplified by generative AI.\u003C/p\u003E\r\n\u003Cp\u003EThe AI RMF is organized around four core functions: Govern, Map, Measure and Manage. NIST emphasizes that these functions are not a simple checklist or a fixed sequence. They are designed to be applied iteratively across the AI lifecycle, with governance informing the other functions throughout.\u003C/p\u003E\r\n\u003Ch3\u003EGovern\u003C/h3\u003E\r\n\u003Cp\u003EThe govern function establishes the organizational foundation for AI risk management. It covers accountability, policies, roles, training, risk culture and oversight. In practical terms, this means defining who can approve an AI use case, who owns the model or application, which policies apply and how risk decisions are documented.\u003C/p\u003E\r\n\u003Ch3\u003EMap\u003C/h3\u003E\r\n\u003Cp\u003EThe map function identifies the context in which an AI system will operate. Teams define the intended use, out-of-scope uses, affected stakeholders, deployment environment and business process impact. The same model can carry different risks depending on where it’s used. For example, a summarization model used on public product documentation has a different risk profile than one used on clinical notes or legal contracts.\u003C/p\u003E\r\n\u003Ch3\u003EMeasure\u003C/h3\u003E\r\n\u003Cp\u003EThe measure function assesses risks against trustworthiness characteristics such as validity, reliability, robustness, safety, security, resilience, explainability, privacy and fairness. Measurement can include quantitative tests, qualitative reviews, red teaming, bias audits, privacy assessments and adversarial testing.\u003C/p\u003E\r\n\u003Ch3\u003EManage\u003C/h3\u003E\r\n\u003Cp\u003EThe manage function prioritizes risks, allocates resources, applies mitigations, monitors controls and communicates risk decisions. This is where risk assessment becomes action: approving a use case with conditions, adding a human review step, limiting access, changing the deployment pattern, accepting residual risk or declining to proceed.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_from_principles_to_practice":{"id":"title-v2-76117b3271","additionalClasses":"anchor-title anchor-title--ai-risk-management-across-the-model-lifecycle","type":"heading2","lines":["AI risk management across the model lifecycle"],":type":"snowflake-site/components/title-v2"},"text_from_principles_to_practice_0":{"id":"text-5008a9bd27","text":"\u003Cp\u003EAI risk management works best when it’s built into the lifecycle rather than tacked on at the end. The lifecycle view helps teams catch different risks at the point where they can still be reduced, tested or escalated.\u003C/p\u003E\r\n\u003Ch3\u003EPre-development\u003C/h3\u003E\r\n\u003Cp\u003EBefore a team trains, tunes or connects a model, it should validate the use case. This includes defining the business objective, identifying the affected users, assigning a risk tier, confirming whether AI is appropriate, and completing an \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003EAI ethics\u003C/a\u003E review or AI impact assessment where needed. A low-risk internal productivity assistant may need lightweight review, while a system that affects credit, employment, healthcare or public services may need formal impact assessment and executive sign-off.\u003C/p\u003E\r\n\u003Ch3\u003EDevelopment\u003C/h3\u003E\r\n\u003Cp\u003EDuring development, risk management focuses on data lineage, data quality, privacy controls, bias audits and documentation. Teams should know where training, testing and validation data came from, what transformations were applied, whether sensitive fields are present and whether the data set is representative for the intended use.\u003C/p\u003E\r\n\u003Cp\u003EModel documentation should capture design choices, assumptions, limitations, evaluation results and known risks. This evidence becomes important later when teams need to explain why a system was approved or why a model version changed.\u003C/p\u003E\r\n\u003Ch3\u003EPre-deployment\u003C/h3\u003E\r\n\u003Cp\u003EBefore release, teams should test the system under conditions that resemble production use. That can include red teaming, adversarial testing, explainability review, security review, privacy review and sign-off gates. For generative AI systems, testing should include prompts that attempt to produce unsafe content, leak sensitive data, override instructions or trigger out-of-scope behavior.\u003C/p\u003E\r\n\u003Cp\u003EThe bar is not to prove that no failure is possible, but rather to understand likely failure modes, apply targeted mitigations and decide whether remaining risk is acceptable.\u003C/p\u003E\r\n\u003Ch3\u003EDeployment\u003C/h3\u003E\r\n\u003Cp\u003EDeployment controls help reduce risk as the system moves into use. A team might use a staged rollout, canary deployment, limited user group, human oversight requirement, logging policy or restricted access pattern. These controls let organizations observe system behavior before expanding usage.\u003C/p\u003E\r\n\u003Cp\u003EFor high-risk systems, deployment should also include a clear escalation path. Users need to know when to trust the system, when to override it and how to report unexpected behavior.\u003C/p\u003E\r\n\u003Ch3\u003EPost-deployment\u003C/h3\u003E\r\n\u003Cp\u003EAI risk management continues after launch. Teams should monitor for drift, degraded performance, unusual usage patterns, security events, user complaints and regulatory changes. They should also define retraining triggers, incident reporting requirements and decommissioning criteria.\u003C/p\u003E\r\n\u003Cp\u003EPost-deployment monitoring is especially important because AI systems can fail gradually. A model may remain available and responsive while its accuracy erodes, its input data changes or its outputs become less aligned with the original use case.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_2083016062_copy":{"id":"text-e2d3696674","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003ECOMMON PITFALL\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EA common mistake is treating AI risk management as a onetime approval step. AI systems can change over time as models, data, users and business conditions evolve, so risk controls should be revisited regularly.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_ai_ethics_challenges":{"id":"title-v2-a10f037e83","additionalClasses":"anchor-title anchor-title--other-ai-risk-management-frameworks","type":"heading2","lines":["Other AI risk management frameworks"],":type":"snowflake-site/components/title-v2"},"text_ai_ethics_challenges_0":{"id":"text-47211f51ee","text":"\u003Cp\u003ENIST AI RMF is widely referenced, but it’s not the only framework organizations use.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.iso.org/standard/77304.html\" target=\"_blank\"\u003EISO/IEC 23894\u003C/a\u003E provides guidance for organizations that develop, produce, deploy or use AI-enabled products, systems and services to manage AI-related risk. ISO describes the standard as guidance for integrating risk management into AI-related activities and functions.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\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 a standard for establishing, implementing, maintaining and continually improving an AI management system, with attention to the risks and opportunities associated with AI.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/eu-ai-act/\"\u003EThe EU AI Act\u003C/a\u003E creates legal obligations for certain AI systems, especially high-risk AI systems. Its risk management provisions require continuous review, risk identification, risk evaluation, mitigation measures, testing and assessment of residual risk.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EBanking and financial services organizations may also rely on model risk guidance such as \u003Ca href=\"https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf\" target=\"_blank\"\u003ESR 11-7\u003C/a\u003E and OCC 2011-12, which are narrower than AI risk management but still important for regulated model governance.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EOther operational guidance, such as Singapore’s Model AI Governance Framework, can help organizations translate \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003EAI governance\u003C/a\u003E principles into roles, processes, documentation and risk-tiered controls.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai_ethics_on_snowflake":{"id":"title-v2-d741c1634a","additionalClasses":"anchor-title anchor-title--ai-risk-management-on-snowflake","type":"heading2","lines":["AI risk management on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_ai_ethics_on_snowflake_0":{"id":"text-90d4f24a20","text":"\u003Cp\u003EAI risk management depends on evidence. Teams need to see which data a model used, how that data was governed, who had access, which model version was deployed, what guardrails were applied and what happened after release. Snowflake helps support that evidence chain across data, models, applications and governance workflows.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E helps organizations make AI risk exposure more visible by surfacing governance context such as data discovery, access history, object tagging and compliance tooling. Cortex Guard adds runtime safeguards for generative AI use cases in Snowflake Cortex AI. It can filter potentially unsafe or harmful responses from a language model.\u003C/p\u003E\r\n\u003Cp\u003EThe Snowflake ML Model Registry helps teams manage models and model metadata in Snowflake and supports performance and drift monitoring through Snowflake ML Observability. Teams can also update metadata such as comments, tags and metrics, including tags that record a model’s purpose, algorithm, training data set or lifecycle stage.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/ISO-IEC-42001-AI-certification/\"\u003ESnowflake’s ISO/IEC 42001 certification\u003C/a\u003E for its artificial intelligence management system provides another layer of assurance. Together, these capabilities help organizations connect AI risk management to the operational controls that matter most: governed data access, \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-lineage/\"\u003Elineage\u003C/a\u003E, metadata, guardrails, monitoring and audit evidence.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_making_ai_ethics_accountable":{"id":"title-v2-114fa3fbc3","additionalClasses":"anchor-title anchor-title--from-risk-review-to-risk-control","type":"heading2","lines":["From risk review to risk control"],":type":"snowflake-site/components/title-v2"},"text_making_ai_ethics_accountable_0":{"id":"text-08ae605b14","text":"\u003Cp\u003EAI risk management is the operating model that lets organizations understand what an AI system is supposed to do, what could go wrong, which controls reduce that risk and what evidence shows the controls are working.\u003C/p\u003E\r\n\u003Cp\u003EAs AI moves deeper into business workflows, the risk surface expands across data, models, applications, users, vendors and downstream decisions. A practical AI risk management program gives teams a way to manage that surface without slowing every project to a halt. It defines risk tiers, assigns owners, documents decisions, tests systems before release and monitors them after deployment.\u003C/p\u003E\r\n\u003Cp\u003EFor enterprises, the strongest programs will be the ones that connect policy to working controls. This is where AI risk management starts to operationalize \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003Eresponsible AI\u003C/a\u003E — translating principles into working controls, monitored systems and auditable outcomes.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_365208365":{"id":"text-1259bce325","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003EKEY TAKEAWAY\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EEffective AI risk management helps organizations innovate with confidence by ensuring AI systems are useful, secure, fair, compliant and aligned with business and stakeholder expectations.\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_ethics","text_intro_0_copy","title_the_foundational_principles_of_ai_ethics","text_the_foundational_principles_of_ai_ethics_0","title_modern_ai_specific_ethical_principles","text_modern_ai_specific_ethical_principles_0","text_493317382","title_major_ai_ethics_frameworks","text_major_ai_ethics_frameworks_0","title_from_principles_to_practice","text_from_principles_to_practice_0","text_2083016062_copy","title_ai_ethics_challenges","text_ai_ethics_challenges_0","title_ai_ethics_on_snowflake","text_ai_ethics_on_snowflake_0","title_making_ai_ethics_accountable","text_making_ai_ethics_accountable_0","text_365208365"],"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","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","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-407d11c51c",":type":"snowflake-site/components/container",":items":{"text_943981956_copy_":{"id":"text-a708dfe5f6","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-cac8acb7e8","additionalClasses":"page-toc","text":"\u003Cul\u003E\r\n\u003Cli data-anchor=\"what-is-ai-risk-management\"\u003EWhat is AI risk management?\u003C/li\u003E\r\n\u003Cli data-anchor=\"why-ai-risk-management-is-different\"\u003EWhy AI risk management is different\u003C/li\u003E\r\n\u003Cli data-anchor=\"categories-of-ai-risk\"\u003ECategories of AI risk\u003C/li\u003E\r\n\u003Cli data-anchor=\"nist-ai-risk-management-framework\"\u003EThe NIST AI 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