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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf"}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"section_0":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what-makes-data-in-ai-systems-unique",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"text":{"id":"text-ead1473f92","text":"\u003Cp\u003EAI data security focuses on protecting the data that trains, grounds and is exposed through AI systems. Because data in AI pipelines shapes model behavior and can be surfaced later through outputs, controls must go beyond storage security to include classification, masking, least-privilege access, lineage, monitoring and lifecycle-specific governance.\u003C/p\u003E\r\n\u003Cp\u003EWhen security researchers disclosed \u003Ca href=\"https://nvd.nist.gov/vuln/detail/cve-2025-32711\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EEchoLeak\u003C/a\u003E, a zero-click exploit in Microsoft 365 Copilot, the vulnerability was particularly concerning because of its potential impact. The incident showed how quickly enterprise data — email, documents and other business context already inside the system's reach — can become part of the attack path once AI systems can retrieve, interpret and act on it.\u003C/p\u003E\r\n\u003Cp\u003EAI data security sits within the broader \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\" rel=\"noopener noreferrer\"\u003EAI security\u003C/a\u003E landscape, but focuses specifically on the data sets, prompts, retrieval corpora, embeddings and access paths that shape what an AI system learns and what it can reveal.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2":{"id":"title-v2-b3a3bad1c0","type":"heading2","lines":["What makes data in AI systems unique"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-57c11874eb","text":"\u003Cp\u003EIn a conventional application, data is primarily stored, queried and returned. While it may also feed downstream analytics or decision systems, its influence is typically bounded and context-specific. In an AI system, the same data asset may also be used to tune \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-models/\"\u003Emodel\u003C/a\u003E behavior, populate a retrieval index, ground an agent’s decisions or supply live context at inference time — which means a compromise at the data layer can persist far beyond a single transaction and show up later as degraded outputs, unauthorized disclosure or altered behavior.\u003C/p\u003E\r\n\u003Cp\u003EData in AI pipelines can influence the model’s weights, steer downstream generations and shape what an agent is allowed to see and act on. Traditional perimeter controls have their place, but they do not fully address attacks that originate inside the pipeline itself, such as poisoned training examples, manipulated retrieved documents or over-privileged service identities moving through internal data systems.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text","title_v2","text_0"]},"section_1":{"columnClassNames":{"h3_title_3":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types-of-data-in-ai-systems",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-6add295f55","type":"heading2","lines":["Types of data in AI systems"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-1c83ee3f57","text":"\u003Cp\u003EAI systems rely on several distinct categories of data, and each one creates a different security risk.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-3568b4db59","type":"heading3","lines":["Training and fine-tuning data sets"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-79cc235eaa","text":"\u003Cp\u003EThese are the raw and curated data sets used to train a foundation model or fine-tune a pre-trained one. They can include structured records, documents, images, logs and code. Because they directly influence what a model learns, compromise here can cause the model to absorb corrupted, biased or malicious patterns without producing an obvious error at the moment of ingestion.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-0cb971179d","type":"heading3","lines":["Inference inputs and context"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-bbf9f6d493","text":"\u003Cp\u003EThis is the live data passed to the model at runtime, including user prompts, system prompts, attached files, retrieved snippets and application context. Depending on how the system is built, those inputs may contain PII, credentials, internal documents or regulated records. Inference-time context is one of the most direct paths to leakage or \u003Ca href=\"https://csrc.nist.gov/glossary/term/exfiltration\" target=\"_blank\"\u003Eexfiltration\u003C/a\u003E if controls are weak.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-56217e0e8a","type":"heading3","lines":["RAG retrieval corpora"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-2043028763","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG\u003C/a\u003E corpora are the document collections, tables and knowledge stores that a retrieval system searches at runtime to supply context to a model. They deserve to be treated as sensitive data stores in their own right, because the risk is not only that confidential documents may be indexed, but that users may surface them through crafted prompts. Also, attackers can poison retrieved content with fewer resources than they can poison a full training set.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-353a3e28e1","type":"heading3","lines":["Model weights and embeddings"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-bd57786156","text":"\u003Cp\u003EModel weights are the learned numerical parameters produced through training. Embeddings are vector representations of data used for similarity search and retrieval. Both are abstractions rather than source records, but they still matter from a security standpoint because embeddings and trained models can retain statistical information about the data they were built from. Additionally, under some conditions they can be used to reconstruct approximations of training data or mount further attacks.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3","h3_title_3","text_4"]},"section_2":{"columnClassNames":{"h3_title_3":"aem-GridColumn aem-GridColumn--default--12","h3_title_4":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_5":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_6":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","text_5":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12","text_7":"aem-GridColumn aem-GridColumn--default--12","text_6":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai-data-security-threats",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5444d76e21","type":"heading2","lines":["AI data security threats"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-bfe00d5d19","text":"\u003Cp\u003EThe threats below are specific to the data layer of AI systems — attacks and failure modes that target the data feeding, shaping or exposed by the model.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-e9744843e6","type":"heading3","lines":["Training data poisoning"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-2bb3cebb70","text":"\u003Cp\u003ETraining data poisoning is the deliberate insertion of manipulated, biased or malicious samples into training or fine-tuning data so the model learns the wrong thing. \u003Ca href=\"https://genai.owasp.org/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EOWASP’s 2025 GenAI project\u003C/a\u003E defines data and model poisoning as manipulation of pre-training, fine-tuning or embedding data that can introduce vulnerabilities, backdoors or biases, and it notes that these attacks can degrade performance or create harmful outputs.\u003C/p\u003E\r\n\u003Cp\u003EIn practice, the effect depends on where the poisoned data enters, how much of it is present and how it interacts with clean data, which is why it is more accurate to say poisoning can alter model behavior than to suggest it always will. Supply-chain exposure matters here too — third-party data sets, scraped corpora and externally sourced documents widen the attack surface before training even begins.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-c327b3baa2","type":"heading3","lines":["Membership inference attacks"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-36fde3eccb","text":"\u003Cp\u003EA membership inference attack tries to determine whether a particular record was included in a model’s training data. An attacker may not be able to recover the original record, but proving that a person’s data was used can still create privacy, contractual or regulatory problems. Guidance from the National Institute of Standards and Technology (\u003Ca href=\"https://csrc.nist.gov/pubs/ai/100/2/e2025/final\"\u003ENIST)\u003C/a\u003E treats membership inference as part of the privacy-breach side of the attack landscape, which is one reason data minimization and careful provenance matter before organizations ever start training.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-e5971ca31b","type":"heading3","lines":["Model inversion attacks"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-afdb1adb28","text":"\u003Cp\u003EModel inversion attacks query a model in ways intended to reconstruct representative inputs corresponding to a given output or class. The outcome can range from weak signals to more revealing approximations depending on model type, access level and how much information the system exposes. NIST groups model inversion within the adversarial ML taxonomy because it represents a meaningful privacy-breach class even when exact recovery is not possible.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-610ee3e611","type":"heading3","lines":["Data leakage through model outputs"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-d420fa2c2e","text":"\u003Cp\u003EModels can disclose sensitive data in more than one way. A base or fine-tuned model may reproduce verbatim or near-verbatim fragments from training data, while a RAG application may surface a sensitive document that was retrievable but never meant to be exposed to the requesting user. This is not just a theoretical concern — a study presented at the USENIX Security Symposium showed that \u003Ca href=\"https://www.usenix.org/system/files/sec21-carlini-extracting.pdf\" target=\"_blank\"\u003Eadversaries could recover memorized sequences, including PII and code\u003C/a\u003E, by querying a language model.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-7e5c48c5a6","type":"heading3","lines":["Prompt injection and data exfiltration"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-4b2aacaeec","text":"\u003Cp\u003EPrompt injection becomes a data-security issue when adversarial instructions push a model or agent to reveal information, ignore prior constraints or take actions against policy. \u003Ca href=\"https://genai.owasp.org/llmrisk/llm01-prompt-injection/\"\u003EOWASP’s 2025 prompt injection guidance\u003C/a\u003E distinguishes between direct prompt injection from the user and indirect prompt injection embedded in retrieved or external content, which is especially relevant for enterprise RAG systems. Once retrieved content can carry instructions, a document store stops being a passive knowledge source and starts behaving like part of the control plane.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_5":{"id":"title-v2-636909a24c","type":"heading3","lines":["Shadow AI and unsanctioned data ingestion"],":type":"snowflake-site/components/title-v2"},"text_6":{"id":"text-cf89a62e0c","text":"\u003Cp\u003EShadow AI refers to employees using unsanctioned AI tools and pasting in source code, internal documents, financial data, credentials or regulated records outside approved controls. It is a data-layer problem because it can involve the movement of sensitive information into systems with unknown retention, unclear access boundaries or vendor-side training policies.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_6":{"id":"title-v2-015ed26b58","type":"heading3","lines":["Agentic AI and over-privileged data access"],":type":"snowflake-site/components/title-v2"},"text_7":{"id":"text-fa2a0bfe36","text":"\u003Cp\u003EAgentic systems raise the stakes because they call tools, traverse systems and act across workflows. If an agent has broad read access to knowledge bases, data warehouses, ticketing systems and SaaS applications, then a successful prompt injection or compromised tool chain can turn that access into machine-speed exfiltration. The security issue is less “agents are risky” in the abstract and more that service identities, retrieval permissions and API scopes often expand faster than governance keeps up. \u003Ca href=\"https://cpl.thalesgroup.com/data-threat-report\" target=\"_blank\"\u003EThales’ 2026 Data Threat Report\u003C/a\u003E frames agentic AI as an insider-style data risk because it increasingly operates with privileged access to valuable information.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3","h3_title_3","text_4","h3_title_4","text_5","h3_title_5","text_6","h3_title_6","text_7"]},"section_3":{"columnClassNames":{"youtube":"aem-GridColumn aem-GridColumn--default--12","h3_title_3":"aem-GridColumn aem-GridColumn--default--12","h3_title_4":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_5":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_6":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","text_5":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12","text_7":"aem-GridColumn aem-GridColumn--default--12","text_6":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"data-layer-security-controls",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-69362076d4","type":"heading2","lines":["Data-layer security controls"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-ed1d20d43a","text":"\u003Cp\u003EAI data security cannot rely on policy statements alone. Organizations reduce risk more effectively when they govern the data layer — the mechanisms that determine what data is discovered, how it is classified, who can access it, what gets masked and how its movement is recorded across the AI lifecycle.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EWatch a demo showing practical governance workflows in Snowflake:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube":{"id":"embed-636d070122","youtubeVideoId":"42GWL5x3Xpg","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"h3_title_0":{"id":"title-v2-6c50df31b2","type":"heading3","lines":["Sensitive data classification and discovery"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-0be92388d8","text":"\u003Cp\u003EThe first step is knowing what data is actually feeding AI pipelines. Sensitive data classification and discovery identify PII, regulated data, confidential documents and high-value business records so controls can be attached before those assets are copied into training sets, embedded into vector indexes or exposed to inference workflows. Automated classification tools (such as \u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E) surface sensitive data before it flows into AI pipelines.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-21483b8c86","type":"heading3","lines":["Encryption and tokenization"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-fc20f124a5","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-encryption/\"\u003EEncryption\u003C/a\u003E renders data unreadable without a key. It protects data at rest and in transit, which remains foundational for AI pipelines just as it is elsewhere. \u003Ca href=\"https://www.snowflake.com/en/fundamentals/tokenization/\"\u003ETokenization\u003C/a\u003E allows organizations to replace sensitive values with non-sensitive substitutes so patterns can still be processed without exposing real identifiers or raw confidential values everywhere in the pipeline.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-52cef83ccd","type":"heading3","lines":["Role-based access control"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-09da9b5050","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/rbac/\"\u003ERole-based access control\u003C/a\u003E (RBAC) keeps AI jobs, RAG ingestion processes and agent workflows from inheriting blanket access they do not need. In practice, this means training jobs should run under purpose-specific roles, ingestion pipelines should only read approved sources, and retrieval services should only access the corpora relevant to their function. The principle of \u003Ca href=\"https://csrc.nist.gov/glossary/term/least_privilege\"\u003Eleast privilege\u003C/a\u003E is especially important in AI because over-privileged service accounts can expose far more data than any individual end user could have reached manually.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-f47cb94ca2","type":"heading3","lines":["Dynamic data masking"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-969bff19e5","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003EDynamic masking\u003C/a\u003E lets teams transform or suppress sensitive values at query time without changing the underlying stored data. This is useful for inference-time AI because a model often does not need raw identifiers to do its job. If a pipeline can answer a support question, summarize activity or classify a document with masked or tokenized fields, then raw PII never needs to enter the context window in the first place.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-9c4111b118","type":"heading3","lines":["Differential privacy"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-ee2ab3c0fd","text":"\u003Cp\u003EDifferential privacy is a mathematical framework that introduces calibrated statistical noise into training processes or query results, making it provably difficult to determine whether any individual record was included in a data set. Unlike masking or encryption, which protect data before it reaches a model, differential privacy addresses what the trained model itself can reveal — giving it formal guarantees against membership inference and related privacy-breach attacks that other controls do not provide.\u003C/p\u003E\u003Cp\u003EDifferential privacy comes with the trade-off of reduced model accuracy at higher privacy budgets, which means it is usually recommended only when individual record exposure carries significant regulatory or reputational risk, and the sensitivity of the training data justifies the accuracy cost.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_5":{"id":"title-v2-75a34b896d","type":"heading3","lines":["Data lineage and provenance tracking"],":type":"snowflake-site/components/title-v2"},"text_6":{"id":"text-bf077e2045","text":"\u003Cp\u003ELineage records where data came from, how it moved and what transformations it passed through. Provenance adds the contextual history that helps teams assess whether a data set is suitable for a particular use. In AI, the combination of \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lineage/lineage-vs-provenance/\"\u003Elineage and provenance\u003C/a\u003E helps teams trace which documents were embedded into a retrieval index, which batch introduced poisoned samples, which downstream model or feature used a disputed source and which owner approved the asset at each stage.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_6":{"id":"title-v2-bfa64f8614","type":"heading3","lines":["Audit logging and continuous monitoring"],":type":"snowflake-site/components/title-v2"},"text_7":{"id":"text-d1cffcd64b","text":"\u003Cp\u003EPersistent logs and monitoring are what make data-layer attacks visible. Query history, retrieval logs, schema changes, access events and model interaction patterns can reveal the difference between normal use and suspicious behavior, such as repeated probing with small prompt variations, unusual volumes of retrieval requests or an agent suddenly enumerating data outside its ordinary pattern. Monitoring does not replace preventive controls, but without it, many privacy and exfiltration attempts are invisible until a model output or downstream decision goes wrong.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","youtube","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3","h3_title_3","text_4","h3_title_4","text_5","h3_title_5","text_6","h3_title_6","text_7"]},"section_4":{"columnClassNames":{"h3_title_3":"aem-GridColumn aem-GridColumn--default--12","text_1":"aem-GridColumn aem-GridColumn--default--12","text_0":"aem-GridColumn aem-GridColumn--default--12","text_3":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","h3_title_0":"aem-GridColumn aem-GridColumn--default--12","text_2":"aem-GridColumn aem-GridColumn--default--12","h3_title_1":"aem-GridColumn aem-GridColumn--default--12","h3_title_2":"aem-GridColumn aem-GridColumn--default--12","text_4":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"securing-data-across-the-ai-development-lifecycle",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-25ba48b456","type":"heading2","lines":["Securing data across the AI development lifecycle"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-31198154a8","text":"\u003Cp\u003EThe most practical way to apply AI data security is to match controls to the moments where data is created, ingested, exposed and reused.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-8fd56d3ee0","type":"heading3","lines":["Training data governance and curation"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-cd8585e2b4","text":"\u003Cp\u003ESecurity starts well before model training begins — vetting data sources rather than assuming public or third-party data sets are safe, documenting provenance so teams know where the data came from and under what terms it was collected, minimizing the scope of data to what the use case actually requires and scanning for sensitive content before it is copied into a training environment.\u003C/p\u003E\u003Cp\u003EVersioning is important here too. If a batch is later found to contain corrupted or noncompliant content, teams need to know which run used it and what downstream assets inherited the problem. This is familiar \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-stewardship/\"\u003Edata stewardship\u003C/a\u003E work, but in AI it has to happen earlier. Once the model has learned from the data, rollback becomes much harder.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-52f0df852a","type":"heading3","lines":["Inference-time data protection"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-623eb8021a","text":"\u003Cp\u003EInference-time protection is about controlling what enters the context window and what the model is allowed to reach at runtime. Sensitive fields should be masked or removed before prompts are assembled, live data sources should enforce access policies before retrieval occurs, and production data should be segmented from development, testing and evaluation environments so convenience does not become exposure. This is also where teams should be especially careful with agents — when a model can call tools, it matters what data and actions the tool layer is authorized to expose in response.\u003C/p\u003E\u003Cp\u003E\u003Cem\u003ETo learn more about protecting AI agents, read \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/agents/\"\u003EAI Agent Security Explained\u003C/a\u003E.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-3a62fa8eaa","type":"heading3","lines":["Output monitoring and guardrails"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-0858be00a9","text":"\u003Cp\u003EOutput controls catch cases where the upstream protections were not enough. Teams can scan generated responses for PII patterns, credentials, policy violations or suspicious verbatim fragments before returning them to the user. Ideally, pattern-based detection will be combined with classification and policy logic to identify both obvious secrets and context-dependent disclosures. It’s important to note that output monitoring is a detective control — it can reduce harm, but it is downstream of the original exposure, which is why it works best as a backstop rather than the primary defense.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-5eb1a5cb21","type":"heading3","lines":["RAG data governance"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-4132d2b4c1","text":"\u003Cp\u003ERAG governance should look to the governance of any other sensitive data store. Teams need to decide which documents are eligible for ingestion, classify them before indexing, enforce retrieval-time authorization so users only see content they are allowed to see and keep audit records of what was retrieved, by whom and under what identity. Because retrieved documents are susceptible to indirect prompt injection, ingestion scanning should assess not just sensitivity, but whether documents contain instructions, markup or embedded content that could alter downstream model behavior.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0","h3_title_0","text_1","h3_title_1","text_2","h3_title_2","text_3","h3_title_3","text_4"]},"section_5":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai-data-security-best-practices",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-f324f11002","type":"heading2","lines":["AI data security best practices"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-d9f9230185","text":"\u003Cp\u003EAI systems put pressure on data practices that may have been sufficient in analytics or application environments, but now leave vulnerabilities. The best practices below help teams adapt those controls to the ways AI systems ingest, reuse and expose data.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_0":{"id":"text-332f353982","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EStart with a clear inventory of AI-relevant data assets:\u003C/b\u003E Teams need a reliable map of the data sets, document stores, vector indexes, service accounts and pipelines feeding AI workflows before controls can be applied in a disciplined way.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETreat data minimization as a design principle:\u003C/b\u003E Models and agents should only be trained on, retrieve from and access the data strictly required for the use case, because every additional source expands the attack surface for poisoning, leakage and misuse.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECreate governance policies specific to AI ingestion and retrieval:\u003C/b\u003E A table approved for analytics use should not be automatically approved for fine-tuning, embedding or agent retrieval, especially when context windows, retrieval layers and automated actions create new disclosure paths.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EBuild security checks into MLOps and data engineering workflows:\u003C/b\u003E Sensitive-data scans, lineage validation, approved-source checks and policy enforcement are more reliable when they run every time a data set is refreshed, a corpus is reindexed or a model is updated. The objective is consistent enforcement where the pipeline actually changes.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ETest continuously for leakage and retrieval abuse:\u003C/b\u003E Adversarial probing, retrieval abuse testing and, for higher-risk use cases, simulations aimed at detecting memorization or privacy leakage can help surface problems that would not appear as obvious system failures.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAlign controls with the obligations attached to the data:\u003C/b\u003E Security and privacy are not the same thing, but in AI systems they are closely related. Retention, provenance, access control, minimization and deletion are much harder to defend if the organization cannot show what data entered the system, where it went and who could reach it.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ci\u003ESee \u003Ca href=\"https://www.snowflake.com/en/resources/white-paper/snowflake-ai-security-framework/\"\u003ESnowflake’s AI Security Framework\u003C/a\u003E to learn more about AI security threats, explore examples and find mitigation strategies.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","text_0"]},"section_6":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","simple_snowflake_acc":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"ai-data-security-faqs",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-a4f77a3334","type":"heading2","lines":["AI Data Security FAQs"],":type":"snowflake-site/components/title-v2"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-bf44028efb","showDivider":false,"accordionItemsList":[{"title":"What types of data are most at risk in AI systems?","richText":"\u003Cp\u003ETraining data sets that contain PII, proprietary records, code or regulated content are high-value targets because they shape what the model learns. RAG corpora are also vulnerable, since sensitive documents can be surfaced through retrieval abuse or prompt manipulation, and inference inputs can leak confidential context if they reach the model unsanitized.\u003C/p\u003E"},{"title":"How does data poisoning differ from a traditional cyberattack?","richText":"\u003Cp\u003EA traditional attack often aims to steal, encrypt or destroy data directly. Data poisoning is different because it targets the model’s learned behavior through the data pipeline, so the system may continue operating while outputs become biased, degraded or quietly manipulated over time.\u003C/p\u003E"},{"title":"Can a model leak sensitive training data in its outputs?","richText":"\u003Cp\u003EYes, published research has shown that language models can reproduce memorized training sequences under certain conditions, and in enterprise applications a RAG layer can also expose sensitive retrieved documents if retrieval and access controls are weak. Mitigations include data minimization, stronger curation, output filtering and privacy-aware training techniques where appropriate.\u003C/p\u003E"},{"title":"How do I know if my RAG corpus has been compromised?","richText":"\u003Cp\u003EWarning signs include outputs that cite documents users should not be able to access, retrieval logs that show unusual document access patterns or behavior that changes after new content is ingested. Preventive controls are still more dependable than detection alone. Restrict ingestion, classify content before indexing and enforce authorization at retrieval time.\u003C/p\u003E"},{"title":"What is the difference between AI data security and AI data privacy?","richText":"\u003Cp\u003EAI data security is about protecting data from unauthorized access, tampering and leakage through technical and operational controls. AI data privacy is about how personal data is collected, used, retained and governed in relation to individual rights. The two overlap often, because weak security controls make privacy obligations much harder to meet.\u003C/p\u003E"}],":type":"snowflake-site/components/simple-snowflake-accordion","appliedCssClassNames":"snowflake-responsive-component-bottom-padding-small"}},":itemsOrder":["title_v2","simple_snowflake_acc"]},"section_7":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"resources",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-e7e87d1d21","type":"heading2","lines":["AI Data Security 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