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callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EAI SAFETY DEFINED\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAI safety is the practice of ensuring AI systems behave reliably, stay within defined boundaries, and can be monitored and corrected in production.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-0ca0cc4600","text":"\u003Cp\u003EThe central question in AI safety is whether a system will continue behaving within acceptable bounds once it moves into production. In real-world workflows, the consequences of failure can be significant: a hallucinated answer might impact a customer, a classification error might affect access to credit or healthcare, an agent’s tool call might update a record or escalate a permission without approval.\u003C/p\u003E\n\u003Cp\u003EAI safety focuses on reducing the risk associated with using \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/\"\u003EAI systems\u003C/a\u003E by defining what acceptable behavior looks like, testing where that behavior breaks, and giving operators a way to investigate and intervene when the system moves outside those boundaries.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-ai-safety":{"id":"title-v2-b2c2c25723","additionalClasses":"anchor-title anchor-title--what-is-ai-safety","type":"heading2","lines":["What is AI safety?"],":type":"snowflake-site/components/title-v2"},"text_what-is-ai-safety_0":{"id":"text-7ea00b7f06","text":"\u003Cp\u003EAI safety is the research and engineering practice dedicated to ensuring AI systems behave reliably and in alignment with their intended use and human values. In practical terms, it’s about making sure a model, agent or AI application does what it’s supposed to do, avoids foreseeable harm and can be corrected when behavior moves outside acceptable bounds.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_what-is-ai-safety_0":{"id":"quote-item-baf00be9b4","alignment":"left","layout":"simple","showQuoteIcon":true,"quote":"The central question in AI safety is whether a system will continue behaving within acceptable bounds once it moves into production.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"title_the-ai-safety-research-landscape":{"id":"title-v2-e4f6e55ab8","additionalClasses":"anchor-title anchor-title--the-ai-safety-research-landscape","type":"heading2","lines":["The AI safety research landscape"],":type":"snowflake-site/components/title-v2"},"text_the-ai-safety-research-landscape_0":{"id":"text-b4ab5f41ca","text":"\u003Cp\u003EAI safety research spans technical, organizational and sociotechnical questions, but most of the field starts with a concrete problem: an AI system optimizes for something, and the thing it optimizes for may not match what humans actually wanted.\u003C/p\u003E\n\u003Ch3\u003EAlignment\u003C/h3\u003E\n\u003Cp\u003EAI alignment focuses on ensuring models pursue intended objectives rather than proxies that look correct during training but produce poor behavior in production. In enterprise settings, this can mean a customer support assistant gives policy-compliant answers instead of merely plausible answers, or a loan model optimizes for risk assessment without introducing hidden bias through correlated variables.\u003C/p\u003E\n\u003Cp\u003EAlignment includes technical research into model objectives, preference learning and behavioral alignment. It also includes operational practices such as documenting the system’s intended use, defining unacceptable outputs and testing whether the model follows those boundaries under realistic prompts.\u003C/p\u003E\n\u003Ch3\u003EInterpretability\u003C/h3\u003E\n\u003Cp\u003EInterpretability focuses on understanding why a model produces the outputs it does. For traditional ML models, that may involve feature importance, model cards or explainability methods that help teams understand which inputs influenced a decision. For frontier models, mechanistic interpretability studies the internal structures and representations that contribute to model behavior.\u003C/p\u003E\n\u003Cp\u003EIn practice, interpretability helps teams investigate failures. If a model denies a claim, flags a transaction or summarizes a legal clause, the organization needs more than the final answer. It needs enough evidence to trace which inputs, retrieved documents, tool calls or model versions shaped the output.\u003C/p\u003E\n\u003Ch3\u003ERobustness\u003C/h3\u003E\n\u003Cp\u003ERobustness is the ability of an AI system to behave correctly under distribution shift, adversarial inputs and edge cases. A predictive model trained on last year’s transaction data may degrade when customer behavior changes. A generative AI assistant may behave differently when users include long context, malformed instructions or domain-specific phrasing the system rarely saw during testing.\u003C/p\u003E\n\u003Cp\u003ERobustness research tries to identify those weak spots before they matter. Enterprise teams turn that research into stress tests, drift monitoring, red-team prompts, domain-specific eval sets and incident thresholds that flag when behavior has moved outside the expected range.\u003C/p\u003E\n\u003Ch3\u003ESpecification gaming\u003C/h3\u003E\n\u003Cp\u003ESpecification gaming occurs when a model satisfies the letter of an objective while violating its intent. In reinforcement learning, this is often called reward hacking: the model finds a way to maximize the reward signal without completing the task in the way humans expected.\u003C/p\u003E\n\u003Cp\u003EThe same pattern appears in business systems. For example, a model optimized only for response speed may produce shorter, less useful answers. Or a recommendation engine optimized only for engagement may promote low-quality content.\u003C/p\u003E\n\u003Ch3\u003ECorrigibility\u003C/h3\u003E\n\u003Cp\u003ECorrigibility means an AI system can be safely corrected, paused, constrained or shut down by operators. For a simple classifier, corrigibility may be as basic as rollback, retraining or deactivation. For tool-using agents, it becomes more complex — operators need approval gates, permissions, kill-switch-style controls, escalation paths and logs showing which action the agent attempted and why.\u003C/p\u003E\n\u003Cp\u003ENo evaluation process catches every failure mode. A safe system needs a path for intervention when the model encounters a new condition, violates policy or produces behavior the team did not anticipate.\u003C/p\u003E\n\u003Ch3\u003EScalable oversight\u003C/h3\u003E\n\u003Cp\u003EScalable oversight asks how humans can supervise AI systems whose outputs, reasoning paths or action chains are too complex for manual review at production scale. Research areas include debate, reinforcement learning from human feedback (RLHF), constitutional AI and other methods that try to transfer human preferences and constraints into model behavior.\u003C/p\u003E\n\u003Cp\u003EFor enterprises, scalable oversight is less abstract. It means deciding which AI decisions require human-in-the-loop review, which outputs can be sampled for quality control, which evaluation metrics matter by domain and which incidents should trigger escalation.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_safety-concerns-across-ai-system-types":{"id":"title-v2-b89a3bd643","additionalClasses":"anchor-title anchor-title--safety-concerns-across-ai-system-types","type":"heading2","lines":["Safety concerns across AI system types"],":type":"snowflake-site/components/title-v2"},"text_safety-concerns-across-ai-system-types_0":{"id":"text-6de06430e4","text":"\u003Cp\u003EAI safety concerns vary by system type. A predictive model, a generative AI assistant and a multiagent workflow can all fail, but they fail through different mechanisms and leave different evidence behind.\u003C/p\u003E\r\n\u003Ch3\u003EPredictive ML\u003C/h3\u003E\r\n\u003Cp\u003EIn predictive ML, safety concerns often involve classification errors with consequential impact. For example, a credit model may deny an applicant who should have been approved. A healthcare model may miss a diagnosis. A supply chain model may forecast demand incorrectly after market conditions shift. These failures often come from data quality issues, drift, biased training data, weak monitoring or an incomplete understanding of where the model is safe to use.\u003C/p\u003E\r\n\u003Ch3\u003EGenerative AI\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/\"\u003EGenerative AI\u003C/a\u003E adds a different set of concerns. A model may hallucinate a citation, produce toxic content, become sycophantic in response to user pressure, leak sensitive information or fail under a jailbreak. Prompt injection can cause a connected application to ignore system instructions, retrieve the wrong context or expose data the user should not see. In these systems, safety depends not only on the model but also on the prompt, retrieval layer, grounding data, guardrails and logging around it.\u003C/p\u003E\r\n\u003Ch3\u003EAgentic AI\u003C/h3\u003E\r\n\u003Cp\u003EMultiagent and tool-using systems raise the stakes further. An \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003Eagent\u003C/a\u003E may call APIs, update records, trigger workflows or hand work to another agent. The safety concern is not just whether the model says something incorrect — the more significant risk is when a sequence of individually reasonable actions compounds into unintended behavior. Tool-use safety, privilege boundaries, action approvals and audit trails become essential because failures can move from text output to operational impact.\u003C/p\u003E\r\n\u003Ch3\u003EFrontier models\u003C/h3\u003E\r\n\u003Cp\u003EFrontier model safety concerns include deceptive alignment, situational awareness and large-scale societal impact. These issues are most visible in frontier labs and government evaluation programs, but the underlying lesson carries into the enterprise: As systems become more capable, connected and autonomous, safety practices have to move from one-time testing to lifecycle management.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_safety-concerns-across-ai-system-types_0":{"id":"text-7728e6ab0a","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003ECOMMON PITFALL\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDon’t assume a safe model automatically creates a safe application. In practice, risks often arise from prompts, retrieval systems, tool integrations, permissions and workflows — not just the model itself.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_enterprise-ai-safety-techniques":{"id":"title-v2-4e36892879","additionalClasses":"anchor-title anchor-title--enterprise-ai-safety-techniques","type":"heading2","lines":["Enterprise AI safety techniques"],":type":"snowflake-site/components/title-v2"},"text_enterprise-ai-safety-techniques_0":{"id":"text-36287301dd","text":"\u003Cp\u003EEnterprise AI safety is a stack of practices that starts before deployment, continues at runtime and produces enough evidence for teams to investigate what has occurred when behavior changes. The right mix depends on the system’s use case, risk level, data sensitivity and degree of autonomy.\u003C/p\u003E\n\u003Ch3\u003ESafety-focused training\u003C/h3\u003E\n\u003Cp\u003ESafety-focused training methods shape model behavior before deployment, such as reinforcement learning from human feedback, constitutional AI, and rejection sampling.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ERLHF uses human preferences to guide model responses.\u003C/li\u003E\n\u003Cli\u003EConstitutional AI uses written principles or rules to help a model critique and revise its own outputs.\u003C/li\u003E\n\u003Cli\u003ERejection-sampling fine-tuning selects better candidate outputs during training or adaptation, reinforcing examples that match desired behavior.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003EThese techniques are most often associated with model providers, but enterprises still need to understand them. A business application that relies on a third-party model inherits some of that model’s safety behavior, then adds its own policies, retrieval context and runtime controls. The enterprise team still has to test whether the resulting application behaves safely in its own domain.\u003C/p\u003E\n\u003Ch3\u003EPre-deployment evals\u003C/h3\u003E\n\u003Cp\u003EPre-deployment evals test whether an AI system meets defined quality and safety expectations before it reaches users. General benchmarks such as TruthfulQA, ToxiGen and harmful-use suites can help test broad risks, while custom domain evals test the scenarios that matter inside the organization.\u003C/p\u003E\n\u003Cp\u003EFor enterprise teams, the custom eval set is often the most important artifact. It may include known edge cases, policy-sensitive questions, representative user prompts, expected SQL outputs, approved source documents, refusal examples and escalation triggers. An eval harness then runs those cases consistently across model versions, prompt changes, retrieval configurations and guardrail settings.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_enterprise-ai-safety-techniques_0":{"id":"text-7f491d471e","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EBuild a custom evaluation set before deploying an AI application. Testing against real business scenarios, edge cases and policy-sensitive prompts often reveals risks that generic AI benchmarks miss.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_enterprise-ai-safety-techniques_1":{"id":"text-d121c9a466","text":"\u003Ch3\u003ERed teaming and adversarial testing\u003C/h3\u003E\n\u003Cp\u003EAI red-teaming tests how a system behaves when users push against its boundaries. Red teams may attempt jailbreaks, prompt injection, sensitive-information extraction, unsafe tool calls or harmful content generation. The goal is to find plausible failure modes before they appear in production.\u003C/p\u003E\n\u003Cp\u003ERed teaming should feed directly into engineering work. For example, a prompt injection that bypasses an instruction should become a new test case. A sensitive data exposure should trigger a review of retrieval permissions, masking policies and output filters. An unsafe tool call should lead to tighter allow-lists, approval gates or role separation.\u003C/p\u003E\n\u003Ch3\u003ERuntime guardrails\u003C/h3\u003E\n\u003Cp\u003ERuntime guardrails apply controls when the AI system receives inputs, retrieves context, calls tools or returns outputs. These controls can include input and output filtering, policy-based refusal, jailbreak detection, prompt-injection detection, allow-lists and restrictions on which tools an agent can call.\u003C/p\u003E\n\u003Cp\u003EGuardrails are especially important because unsafe behavior often appears only after deployment. A user may combine instructions in a novel way. A retrieved document may contain hidden instructions. An agent may encounter a tool response the development team did not anticipate. Runtime controls provide a second layer of defense when pre-deployment evals are no longer enough.\u003C/p\u003E\n\u003Ch3\u003ERetrieval grounding\u003C/h3\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERetrieval-augmented generation\u003C/a\u003E (RAG) architectures reduce hallucination by anchoring model responses in retrieved context. Instead of asking a model to answer from its training data alone, the application retrieves relevant documents, records or knowledge base entries and asks the model to respond based on that context.\u003C/p\u003E\n\u003Cp\u003EThe retrieved content still needs to be accurate, current and governed. The system also needs to check whether the answer is grounded in the retrieved source, whether the user has permission to see that source and whether the model cites or summarizes it correctly. But when implemented carefully, retrieval grounding gives teams a clearer path to evaluate factuality and trace unsafe answers back to specific data.\u003C/p\u003E\n\u003Ch3\u003EHuman oversight\u003C/h3\u003E\n\u003Cp\u003EHuman-in-the-loop review gates are often required for high-stakes decisions, ambiguous outputs and actions that can affect customers, employees or regulated processes. A healthcare summary, legal clause extraction, fraud escalation or credit decision may use AI to accelerate analysis, but a human reviewer generally still needs the authority and context to approve, reject or revise the output.\u003C/p\u003E\n\u003Cp\u003EOversight works best when it’s designed into the workflow. Reviewers should have the original input, retrieved context, model output, confidence signals, policy flags and prior decisions. Without context, human review is merely symbolic.\u003C/p\u003E\n\u003Ch3\u003EMonitoring and incident response\u003C/h3\u003E\n\u003Cp\u003EAI safety continues after deployment. Teams need logging, anomaly detection, drift detection, usage monitoring and incident playbooks that define what happens when an unsafe output, unexpected tool call or model degradation is detected.\u003C/p\u003E\n\u003Cp\u003EAn incident playbook should answer practical questions, such as:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EWhich logs show the prompt, model version, retrieved data and output?\u003C/li\u003E\n\u003Cli\u003EWho owns the application?\u003C/li\u003E\n\u003Cli\u003ECan the team roll back to a previous model or prompt?\u003C/li\u003E\n\u003Cli\u003EWhich downstream systems may have been affected?\u003C/li\u003E\n\u003Cli\u003EWhich policy exception, if any, allowed the behavior?\u003C/li\u003E\n\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-safety-vs-ai-security-vs-ai-ethics":{"id":"title-v2-03232a92d1","additionalClasses":"anchor-title anchor-title--ai-safety-vs-ai-security-vs-ai-ethics","type":"heading2","lines":["AI safety vs. AI security vs. AI ethics"],":type":"snowflake-site/components/title-v2"},"text_ai-safety-vs-ai-security-vs-ai-ethics_0":{"id":"text-f4e3b79d98","text":"\u003Cp\u003EAI safety, AI security and AI ethics are related, but each lens asks a different question:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAI safety\u003C/b\u003E asks whether the system behaves as intended and avoids unintended harm, even when there’s no attacker. A benign failure might involve a model hallucinating a policy, misclassifying a patient risk score or following an instruction too literally.\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003E\u003Cb\u003EAI security\u003C/b\u003E\u003C/a\u003E asks whether the system can resist adversarial behavior. The threat model includes attackers who try to manipulate prompts, poison training data, extract sensitive information, abuse APIs or cause the model to perform unauthorized actions.\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-ethics/\"\u003E\u003Cb\u003EAI ethics\u003C/b\u003E\u003C/a\u003E asks whether the intended behavior itself is morally sound. A system can be safe in the narrow technical sense, meaning it performs as designed, while still raising ethical concerns if the design produces unfair, opaque or harmful outcomes.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EIn practice, the boundaries overlap. A safety failure can become a security incident if an attacker learns how to trigger it. A security weakness can create unsafe behavior. And unsafe behavior is often unethical, especially when it affects people’s rights, access, privacy or well-being.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-safety-standards-and-policy":{"id":"title-v2-aad7feb1d4","additionalClasses":"anchor-title anchor-title--ai-safety-standards-and-policy","type":"heading2","lines":["AI safety standards and policy"],":type":"snowflake-site/components/title-v2"},"text_ai-safety-standards-and-policy_0":{"id":"text-7111087e91","text":"\u003Cp\u003EAI safety is increasingly reflected in standards, policy frameworks and government evaluation programs. The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003ENIST AI Risk Management Framework\u003C/a\u003E identifies “safe” as one of the characteristics of trustworthy AI, alongside valid and reliable, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/algorithmic-bias/\"\u003Ealgorithmic bias\u003C/a\u003E managed.\u003C/p\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 places obligations on high-risk AI systems, including \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-risk-management/\"\u003EAI risk management\u003C/a\u003E, testing, monitoring, \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Edata governance\u003C/a\u003E and technical documentation. Article 9 specifically requires high-risk AI systems to be tested to identify appropriate risk management measures and to perform consistently for their intended purpose.\u003C/p\u003E\r\n\u003Cp\u003EGovernment safety evaluation bodies are also becoming more important. The \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.aisi.gov.uk/\"\u003EU.K. AI Security Institute\u003C/a\u003E conducts research and builds infrastructure to understand advanced AI capabilities, impacts and mitigations, while U.S. AI evaluation efforts have expanded through the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.nist.gov/caisi\"\u003ECenter for AI Standards and Innovation.\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003EVoluntary commitments from frontier labs, including model cards, red teaming and safety evaluations, are another part of the landscape. These commitments do not replace internal enterprise controls, but they shape expectations for \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/ai-transparency/\"\u003Etransparency\u003C/a\u003E, evaluation and responsible deployment.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/iso-42001/\"\u003EISO/IEC 42001\u003C/a\u003E adds a management-system layer. Rather than treating AI safety as a onetime technical assessment, it frames \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/responsible-ai/\"\u003Eresponsible AI\u003C/a\u003E as an organizational system of policies, roles, processes, monitoring and continual improvement.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-safety-on-snowflake":{"id":"title-v2-32a9b70087","additionalClasses":"anchor-title anchor-title--ai-safety-on-snowflake","type":"heading2","lines":["AI safety on Snowflake"],":type":"snowflake-site/components/title-v2"},"text_ai-safety-on-snowflake_0":{"id":"text-ca685860f8","text":"\u003Cp\u003EAI safety depends on the model, the data the model can access, the prompts and tools around it, the policies enforced at runtime and the evidence available when teams need to investigate behavior. Snowflake supports AI safety practices through \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/ai-governance/\"\u003EAI governance\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/observability/\"\u003EAI observability\u003C/a\u003E capabilities that help teams evaluate, constrain and trace AI systems in the same environment where enterprise data is governed.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-ai-guardrails\"\u003ECortex AI Guardrails\u003C/a\u003E help filter potentially unsafe or harmful model responses before they are returned to the application. Cortex AI Guardrails provide protections such as prompt injection detection, jailbreak prevention and zero-day style protection for AI applications on Snowflake.\u003C/p\u003E\r\n\u003Cp\u003ECortex Agent evaluations and AI Observability help teams test and monitor generative AI applications and agents, while \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E helps teams govern the data pathways that AI systems depend on. Access history and lineage can show what data was accessed, when access occurred and how data moved from source objects to target objects, including column-level lineage in supported cases.\u003C/p\u003E\r\n\u003Cp\u003EThe \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/model-registry/overview\"\u003ESnowflake Model Registry\u003C/a\u003E supports model lifecycle management, including access control, model metadata, versioning and production promotion patterns. That matters for safety because teams need to know which model version is in use, which metrics supported promotion and how to move away from an unsafe or underperforming version.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/ISO-IEC-42001-AI-certification/\"\u003ESnowflake has achieved ISO/IEC 42001 certification\u003C/a\u003E for the artificial intelligence management system supporting Snowflake, providing external validation of governance and oversight structures for managing the AI lifecycle.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_ai-safety-is-becoming-an-enterprise-responsibility":{"id":"title-v2-f7c8182d3b","additionalClasses":"anchor-title anchor-title--ai-safety-is-becoming-an-enterprise-responsibility","type":"heading2","lines":["AI safety is becoming an enterprise responsibility"],":type":"snowflake-site/components/title-v2"},"text_ai-safety-is-becoming-an-enterprise-responsibility_0":{"id":"text-11a88c45a3","text":"\u003Cp\u003EEnterprise use of AI comes with responsibility because the actions of AI systems can create consequences for customers, partners, employees and the public. As AI systems move from answering questions to supporting decisions and taking action, safety has to be designed into the full application lifecycle.\u003C/p\u003E\n\u003Cp\u003EThat means enterprises need more than model-level assurances. They need evals that reflect real business scenarios, red-team tests that expose plausible failure modes, runtime controls that constrain unsafe behavior and audit trails that show which prompt, model version, retrieved context or tool call shaped an output.\u003C/p\u003E\n\u003Cp\u003EWhile it’s not possible to eliminate every possible error before deployment, the goal is to make AI systems reliable enough for their intended use, observable enough to investigate and correctable enough that teams can intervene when behavior moves outside acceptable bounds.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_ai-safety-is-becoming-an-enterprise-responsibility_0":{"id":"text-4674f791c4","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EAs AI systems become more autonomous, safety becomes an enterprise responsibility. Organizations need lifecycle controls — from testing and guardrails to monitoring and intervention — to help ensure AI behaves as intended in real-world environments.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular 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