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data-anchor=\"resources\"\u003EResources\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"experiencefragment":{"id":"experiencefragment-415c25a3bd","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/share-icons/share-icons-no-title/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"container_949147658":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-1c4f5124e1",":type":"snowflake-site/components/container",":items":{"container_949147658":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 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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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"section_0":{"columnClassNames":{"text_0":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"intro",":type":"snowflake-site/components/container",":items":{"text_0":{"id":"text-6e89578d5a","text":"\u003Cp\u003EWhat makes \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/\"\u003EAI security\u003C/a\u003E challenging is not simply that AI introduces new threats — although it does. AI also changes the conditions under which familiar applications operate. A search interface that once simply returned data may now interpret it. Software that once supported decisions may now actively shape them. Tools that once stopped at a recommendation may now take action on that recommendation.\u003C/p\u003E\u003Cp\u003EAI security threats are also more consequential, because they sit closer to enterprise data, user intent and downstream action than most traditional threats. This changes the security conversation, because the risk has expanded to how the system behaves once it has access.\u003C/p\u003E\u003Cp\u003ETo manage this expanded risk, organizations are increasingly putting AI security systems in place. These systems combine technical controls, monitoring, validation and \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/\"\u003Egovernance\u003C/a\u003E to protect AI applications, their data dependencies and the workflows around them.\u003C/p\u003E\u003Cp\u003E\u003Cem\u003ERead \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-security/agents/\"\u003EAI Agent Security Explained\u003C/a\u003E to learn how to protect AI agents and the systems they access.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_1":{"columnClassNames":{"youtube":"aem-GridColumn aem-GridColumn--default--12","text_0":"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":"why-organizations-need-dedicated-ai-security-systems",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-07b73f7c71","type":"heading2","lines":["Why organizations need dedicated AI security systems"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-3ae369eadd","text":"\u003Cp\u003EAI changes where risk enters an environment and how fast it can propagate. For example, a misconfigured SaaS app might expose a set of records, but an AI agent with broad data access, external tool integrations and execution authority can expand the blast radius and compound downstream effects.\u003C/p\u003E\r\n\u003Cp\u003EThe pressure to move fast is also a factor. According to its AI Readiness Index 2025, Cisco found a \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.cisco.com/c/m/en_us/solutions/ai/readiness-index/realizing-the-value-of-ai.html\"\u003E54-point gap\u003C/a\u003E between planned agentic AI deployment and actual security readiness. Organizations are not waiting for perfect controls. They are building and deploying now, which means security has to catch up in production.\u003C/p\u003E\r\n\u003Cp\u003EMaking the challenge harder is that the AI risk profile is qualitatively different from conventional application security. Adversarial inputs can cause a model to produce incorrect or unsafe outputs — often with no visible sign the input has been tampered with. Poisoned training data can corrupt model behavior before a system is ever deployed. Prompt injection can redirect a model at runtime, hijacking its instructions mid-task. And drift can quietly degrade accuracy after launch, with no alerts and no obvious failure point.\u003C/p\u003E\r\n\u003Cp\u003EThis is why security teams need controls purpose-built for models, prompts, pipelines and governed data access — and why Gartner® has projected that “by 2028, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.gartner.com/en/newsroom/press-releases/2026-03-17-gartner-predicts-ai-applications-will-drive-50-percent-of-cybersecurity-incident-response-efforts-by-2028\"\u003Emore than half\u003C/a\u003E of enterprises will rely on dedicated AI security platforms to secure third-party AI service usage and protect custom-build AI applications.”\u003Csup\u003E1\u003C/sup\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ELearn how Horizon Catalog protects sensitive data, enables tag-based controls, and provides lineage and quality observability to enable secure, trusted AI.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"youtube":{"id":"embed-26b6f3151d","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"}},":itemsOrder":["title_v2","text_0","youtube"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"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","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","text_5":"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":"core-components-of-an-ai-security-system",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-0a4986957e","type":"heading2","lines":["Core components of an AI security system"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-2b2284c911","text":"\u003Cp\u003EIn most enterprise environments, an AI security system includes a set of controls that protect data, validate models, harden the surrounding pipeline, monitor behavior in production and enforce policy across the lifecycle. Each component is important on its own, but the real value comes from how they work together.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-80d1a6e4a9","type":"heading3","lines":["Data protection and integrity controls"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-0254e73797","text":"\u003Cp\u003EAI systems handle different classes of data differently, and the controls should reflect that. Training data shapes model behavior over time, inference data may contain live business or customer inputs, and retrieval-augmented generation (\u003Ca href=\"https://www.snowflake.com/en/fundamentals/rag/\"\u003ERAG\u003C/a\u003E) pipelines pull governed content into prompts at runtime. Classification, encryption in transit and at rest, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/tokenization/\"\u003Edata tokenization\u003C/a\u003E where appropriate and clear controls on what can enter prompts all belong here.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-43c50cbde4","type":"heading3","lines":["Model security and validation"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-3ae5a27611","text":"\u003Cp\u003EA model includes weights, architecture choices, fine-tuning artifacts, dependencies and the surrounding validation process. External model registries and open source packages can accelerate development, but they also introduce AI supply chain risk. OWASP explicitly calls out \u003Ca href=\"https://genai.owasp.org/llmrisk/llm032025-supply-chain/\" target=\"_blank\"\u003Esupply chain vulnerabilities\u003C/a\u003E and \u003Ca href=\"https://genai.owasp.org/llmrisk2023-24/llm03-training-data-poisoning/\" target=\"_blank\"\u003Etraining data poisoning\u003C/a\u003E as material risks for large language model (LLM) applications. This is why model validation should include adversarial testing, bias evaluation, provenance checks and controlled release processes before deployment.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-367a20f6e4","type":"heading3","lines":["Pipeline and infrastructure hardening"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-2640262703","text":"\u003Cp\u003EMost real-world AI risk lives in the seams between components. Data ingestion, feature pipelines, training jobs, model registries, containers, APIs and inference services all create exposure. Hardening here means treating the ML pipeline as production infrastructure — secure images, signed artifacts, API gateway protections, workload isolation, network segmentation and identity controls for services as well as people.\u003C/p\u003E\u003Cp\u003E\u003Cem\u003ERead \u003Ca href=\"https://www.snowflake.com/en/fundamentals/ai-governance/compliance/\"\u003EAI Governance Compliance in Practice\u003C/a\u003E to learn how to align AI systems with global regulation.\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-7f78b799c1","type":"heading3","lines":["Monitoring, observability and drift detection"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-954af9d57c","text":"\u003Cp\u003EAn AI security system has to observe what the model is doing after deployment. This includes logging model access, prompt and response activity where appropriate, policy violations, unusual tool calls, data access patterns, performance degradation and model drift. \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\" target=\"_blank\"\u003ENIST’s AI Risk Management Framework\u003C/a\u003E reinforces that AI risk management is ongoing rather than onetime, flowing through govern, map, measure and manage activities.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-3f22c3846e","type":"heading3","lines":["Governance and policy enforcement"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-0b3cb8056b","text":"\u003Cp\u003EAI workflows need role-based access control (\u003Ca href=\"https://www.snowflake.com/en/fundamentals/rbac/\"\u003ERBAC\u003C/a\u003E), environment-specific policies, approval paths for model changes and consistent enforcement across development, staging and production. This is where AI security begins to overlap with broader governance. \u003Ca href=\"https://www.iso.org/standard/42001\" target=\"_blank\"\u003EISO/IEC 42001\u003C/a\u003E is useful here because it frames AI management as an organizational system of policies, objectives and continual improvement rather than a narrow technical checklist.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_3":{"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","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","text_5":"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":"how-ai-security-systems-address-common-threats",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-125e366500","type":"heading2","lines":["How AI security systems address common threats"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-4e89e17bbb","text":"\u003Cp\u003EThe value of AI security systems become clear when you look at the kinds of failures they’re designed to contain. Some threats target AI models directly, while others target the data, the prompt path or the connected applications around it.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-9fbc8a1606","type":"heading3","lines":["Adversarial attacks and input manipulation"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-cd9b3fd4d5","text":"\u003Cp\u003EAdversarial attacks use crafted inputs to push a model toward the wrong output without making the manipulation obvious to a human observer. In vision systems, this can mean adversarial manipulations that trigger misclassification. In language systems, it can mean carefully shaped inputs that alter reasoning or behavior. Defensive measures include adversarial training, input validation, guardrails and model hardening, but this is still an active research area.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-018f666225","type":"heading3","lines":["Data poisoning and training set corruption"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-eeebe9a6d8","text":"\u003Cp\u003EPoisoning happens when malicious or low-integrity samples enter the training or fine-tuning data and shift model behavior in ways the organization did not intend. This can be overt, such as deliberate manipulation of labels, or more subtle, such as tainted external corpora entering downstream pipelines without \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lineage/lineage-vs-provenance/\"\u003Eprovenance\u003C/a\u003E checks.\u003C/p\u003E\r\n\u003Cp\u003EThe defense pattern is usually a combination of data lineage, provenance tracking, anomaly detection during training and tighter filtering on sources. In 2026, Zscaler reported \u003Ca href=\"https://ir.zscaler.com/news-releases/news-release-details/zscaler-2026-ai-threat-report-91-year-over-year-surge-ai\" target=\"_blank\"\u003E410 million DLP policy violations\u003C/a\u003E tied to ChatGPT alone, which shows how much sensitive and potentially contaminating data may already be moving through AI channels.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-e9cfeda620","type":"heading3","lines":["Model theft and intellectual property exposure"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-c6a78bfb65","text":"\u003Cp\u003EModel extraction attacks aim to reconstruct the behavior of a proprietary model through repeated API interactions. Even when weights are never exposed directly, inference endpoints can leak enough signal to make extraction economically worthwhile. The usual countermeasures include rate limiting, stronger authentication, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/anomaly-detection/\"\u003Eanomaly detection\u003C/a\u003E around query patterns, output shaping and contractual as well as technical controls around access. In practice, this is one reason inference APIs should be treated as sensitive assets, not just utility endpoints.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-1acc017cc4","type":"heading3","lines":["Prompt injection and output manipulation"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-acd588f189","text":"\u003Cp\u003E\u003Ca href=\"https://genai.owasp.org/llmrisk/llm01-prompt-injection/\" target=\"_blank\"\u003EPrompt injection\u003C/a\u003E is one of the clearest examples of why AI security needs dedicated controls. Prompt injection involves manipulating LLMs through crafted inputs that can lead to unauthorized access, data breaches and compromised decision-making. It’s so dangerous because insecure output handling can create downstream exploits.\u003C/p\u003E\r\n\u003Cp\u003EIn LLM-based systems, both direct and indirect prompt injection must be addressed, particularly when a model can retrieve content, call tools or pass outputs into other systems. Defenses typically combine prompt isolation, input sanitization, output validation and runtime guardrails.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_4":{"id":"title-v2-04ed666334","type":"heading3","lines":["Excessive agency and ungoverned action"],":type":"snowflake-site/components/title-v2"},"text_5":{"id":"text-14e7be6d0b","text":"\u003Cp\u003EAn agent with broad tool access, elevated permissions or insufficient human oversight creates disproportionate exposure — not because it is necessarily compromised, but because the blast radius of any failure, whether from an attack, a faulty instruction or a model error, scales with the authority the agent holds.\u003C/p\u003E\r\n\u003Cp\u003EOWASP identifies \u003Ca href=\"https://genai.owasp.org/llmrisk/llm062025-excessive-agency/\" target=\"_blank\"\u003Eexcessive agency\u003C/a\u003E as a distinct risk category for LLM applications, distinct from prompt injection or data exposure. The relevant controls are capability scoping (granting agents only the tools and permissions required for a specific task), reversible design (preferring actions that can be undone over those that cannot), and human-in-the-loop checkpoints for consequential or irreversible decisions.\u003C/p\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_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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_4":{"columnClassNames":{"text_0":"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":"ai-security-frameworks-and-standards",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-6a46fed627","type":"heading2","lines":["AI security frameworks and standards"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-a3d1580d86","text":"\u003Cp\u003EFrameworks give security teams a shared structure for evaluating, governing, and communicating AI risk. No single framework answers every question. Some operate at the organizational level, establishing governance and accountability. Others focus on specific threat surfaces, like LLM applications, or carry regulatory weight for organizations operating in certain markets. Most enterprise teams draw from more than one.\u003C/p\u003E\r\n\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EFramework\u003C/th\u003E\r\n\u003Cth\u003EFocus\u003C/th\u003E\r\n\u003Cth\u003EScope\u003C/th\u003E\r\n\u003Cth\u003EBest For\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003ENIST AI RMF 1.0\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ERisk management\u003C/td\u003E\r\n\u003Ctd\u003EAll AI systems\u003C/td\u003E\r\n\u003Ctd\u003EU.S. organizations, voluntary adoption\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EISO/IEC 42001\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAI management system\u003C/td\u003E\r\n\u003Ctd\u003EOrganization-wide\u003C/td\u003E\r\n\u003Ctd\u003EGlobal compliance, certification\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EOWASP Top 10 for LLMs\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EThreat catalog\u003C/td\u003E\r\n\u003Ctd\u003ELLM applications\u003C/td\u003E\r\n\u003Ctd\u003EDeveloper teams, AppSec\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EEU AI Act\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003ERegulation\u003C/td\u003E\r\n\u003Ctd\u003EEU market AI systems\u003C/td\u003E\r\n\u003Ctd\u003EOrganizations selling into the EU\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EGartner AI TRiSM\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EPlatform architecture\u003C/td\u003E\r\n\u003Ctd\u003EEnterprise AI security\u003C/td\u003E\r\n\u003Ctd\u003ETool evaluation, vendor selection\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cb\u003EMITRE ATLAS\u003C/b\u003E\u003C/td\u003E\r\n\u003Ctd\u003EAdversarial ML threat taxonomy\u003C/td\u003E\r\n\u003Ctd\u003EAI/ML systems and their attack surfaces\u003C/td\u003E\r\n\u003Ctd\u003ESecurity teams threat modeling AI, red teams, organizations already using \u003Ca href=\"https://attack.mitre.org/\" target=\"_blank\"\u003EMITRE ATT&CK\u003C/a\u003E\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_5":{"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":"implementing-an-ai-security-system-a-practical-approach",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-68b8388098","type":"heading2","lines":["Implementing an AI security system: a practical approach"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-3bc625a595","text":"\u003Cp\u003EZscaler reported that most enterprise AI systems it tested could be compromised \u003Ca href=\"https://www.zscaler.com/press/zscaler-2026-ai-threat-report-83-year-over-year-surge-ai-activity-creates-growing-oversight\" target=\"_blank\"\u003Ein 16 minutes\u003C/a\u003E, with critical flaws found in 100% of systems analyzed. The weakness often sits in the distance between governance as written in policies and governance as enforced in production. Reducing exposure means identifying where AI is running, controlling the data and tools it can reach, and putting monitoring and response in place for the moments when behavior changes or controls fail.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_0":{"id":"title-v2-8c806c725d","type":"heading3","lines":["Assess your AI security posture"],":type":"snowflake-site/components/title-v2"},"text_1":{"id":"text-4a0a7c1ab2","text":"\u003Cp\u003EStart with inventory. Security teams need to know which models are in use, where they run, which data sources they depend on, which endpoints expose them and which tools or agents they can call. AI security posture management (\u003Ca href=\"https://www.crowdstrike.com/en-us/cybersecurity-101/cloud-security/ai-security-posture-management-ai-spm/\" target=\"_blank\"\u003EAI-SPM\u003C/a\u003E) is useful for building continuous visibility into AI assets and relationships. You cannot secure what you cannot enumerate.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_1":{"id":"title-v2-01e831bbb1","type":"heading3","lines":["Define policies and access controls"],":type":"snowflake-site/components/title-v2"},"text_2":{"id":"text-71dcefa4de","text":"\u003Cp\u003EThe next step is to define policy around data movement, model access and operational authority. This usually means classifying data used for training and inference differently, applying \u003Ca href=\"https://csrc.nist.gov/glossary/term/least_privilege\" target=\"_blank\"\u003Eleast-privilege access\u003C/a\u003E to model development and deployment workflows, and defining which agents or services can invoke which tools. Identity is central, especially as AI expands the number of nonhuman actors in the environment.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_2":{"id":"title-v2-8bc800d3bf","type":"heading3","lines":["Deploy monitoring and incident response"],":type":"snowflake-site/components/title-v2"},"text_3":{"id":"text-779703e040","text":"\u003Cp\u003EMonitoring has to move beyond uptime and error rates. Teams need real-time alerting for anomalous model behavior, suspicious prompt patterns, unusual retrieval activity, data exfiltration attempts and policy violations.\u003C/p\u003E\u003Cp\u003EThey also need incident response playbooks that assume the failure may involve a model, a prompt chain, a poisoned data source or an agent with excessive permissions. In mature environments, these signals should connect into existing security information and event management and security orchestration, automation and response workflows so AI incidents are handled inside the same operational muscle memory as other security events.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"h3_title_3":{"id":"title-v2-2b86d79f89","type":"heading3","lines":["Integrate with existing security infrastructure"],":type":"snowflake-site/components/title-v2"},"text_4":{"id":"text-2c57fa327c","text":"\u003Cp\u003EAI security should extend existing security architecture. Zero trust, network segmentation, identity enforcement, secrets management, data loss prevention, and audit logging remain essential — and they apply directly to AI systems. AI agents need service identities and scoped access, not open-ended permissions. The data they touch needs the same classification and loss-prevention controls applied elsewhere. Every action they take should be logged.\u003C/p\u003E\u003Cp\u003EWhat changes is the nature of what you're securing. The challenge is no longer just protecting systems and data at rest or in transit, but also governing systems that can interpret context, generate outputs and take action inside live workflows. That is why AI security has to carry familiar controls forward while adding new ones around retrieval, tool use, output handling and runtime oversight.\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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_6":{"columnClassNames":{"text_0":"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":"ai-security-systems-across-industries",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-8d7af9c05f","type":"heading2","lines":["AI security systems across industries"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-963ba65a44","text":"\u003Cp\u003EThe control pattern is broadly similar across sectors, but the operational emphasis changes based on the industry.\u003C/p\u003E\r\n\u003Cp\u003EIn financial services, teams must preserve explainability, auditability and controlled change in workflows tied to fraud detection, credit risk and market behavior. \u003Ca href=\"https://www.sec.gov/newsroom/speeches-statements/gensler-remarks-fsoc-121423\" target=\"_blank\"\u003ESEC commentary\u003C/a\u003E on AI in financial contexts has pointed to issues around explainability, bias and accuracy, while \u003Ca href=\"https://www.finra.org/rules-guidance/key-topics/fintech/report/artificial-intelligence-in-the-securities-industry/key-challenges\" target=\"_blank\"\u003EFINRA\u003C/a\u003E has said firms using AI-based applications may need to update model risk management frameworks to address AI-specific challenges. Together, these concerns make monitoring, documentation and audit trails especially important.\u003C/p\u003E\r\n\u003Cp\u003EIn healthcare, the data is highly regulated and the tolerance for silent degradation is lower. \u003Ca href=\"https://www.hipaajournal.com/when-ai-technology-and-hipaa-collide/\" target=\"_blank\"\u003EHIPAA\u003C/a\u003E constraints shape what can be used in training and inference, while \u003Ca href=\"https://www.hhs.gov/guidance/sites/default/files/hhs-guidance-documents/FDA/Guidance-Predetermined-Change-Control-AIML.pdf\" target=\"_blank\"\u003EFDA guidance\u003C/a\u003E for AI-enabled device software functions emphasizes planned modifications, validation methods and ongoing assessment of safety and effectiveness. In other words, model drift is not just a quality issue when a clinical or diagnostic workflow is involved. It can become a patient safety issue.\u003C/p\u003E\r\n\u003Cp\u003EIn the public sector, the supply chain and national security angles are often more pronounced. CISA and the U.K. NCSC’s \u003Ca href=\"https://www.cisa.gov/news-events/alerts/2023/11/26/cisa-and-uk-ncsc-unveil-joint-guidelines-secure-ai-system-development\" target=\"_blank\"\u003Ejoint guidelines\u003C/a\u003E on secure AI system development, along with CISA’s more recent \u003Ca href=\"https://www.cisa.gov/news-events/alerts/2025/05/22/new-best-practices-guide-securing-ai-data-released\" target=\"_blank\"\u003Ebest-practices\u003C/a\u003E work on securing AI data, reflect the same pattern: AI systems need secure development, controlled data handling and stronger resilience across the lifecycle. This matters especially when agencies depend on third-party models, embedded AI features or software supply chains they do not fully control.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_7":{"columnClassNames":{"text_0":"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":"operationalizing-ai-security-at-scale",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-2625230d54","type":"heading2","lines":["Operationalizing AI security at scale"],":type":"snowflake-site/components/title-v2"},"text_0":{"id":"text-8a6171d146","text":"\u003Cp\u003EThe practical case for AI security systems is straightforward. Once AI is connected to enterprise data, embedded in business workflows, or allowed to invoke tools, security can no longer stop at the model or the infrastructure around it. Organizations need a way to govern what these systems can access, observe how they behave in production, and respond when outputs, permissions or runtime behavior create risk.\u003C/p\u003E\r\n\u003Cp\u003EThis doesn’t mean starting over with an entirely new security architecture. In most environments, the right approach is to extend existing controls — identity, segmentation, logging, data protection and policy enforcement — while adding the oversight needed for retrieval, prompt handling, tool use and runtime decision-making. The foundation is familiar, but the system being governed is less predictable and more dependent on context.\u003C/p\u003E\r\n\u003Cp\u003E\u003Csup\u003E1. Gartner Press Release, \u003Ca href=\"https://www.gartner.com/en/newsroom/press-releases/2026-03-17-gartner-predicts-ai-applications-will-drive-50-percent-of-cybersecurity-incident-response-efforts-by-2028\" target=\"_blank\"\u003EGartner Predicts AI Applications Will Drive 50% of Cybersecurity Incident Response Efforts by 2028,\u003C/a\u003E March 17, 2026. GARTNER is a trademark of Gartner Inc. and/or its affiliates.\u003C/sup\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"section_8":{"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-security-systems-faqs",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-5907441476","type":"heading2","lines":["AI security systems FAQs"],":type":"snowflake-site/components/title-v2"},"simple_snowflake_acc":{"id":"simple-snowflake-accordion-39a174982a","showDivider":false,"accordionItemsList":[{"title":"What is an AI security system?","richText":"\u003Cp\u003EAn AI security system is the integrated set of controls, tools and processes used to protect AI models, training data, inference pipelines and AI-generated outputs across the lifecycle. It covers data protection, model validation, runtime monitoring, policy enforcement and incident response.\u003C/p\u003E"},{"title":"How does AI security differ from traditional cybersecurity?","richText":"\u003Cp\u003ETraditional cybersecurity protects deterministic systems and familiar infrastructure layers. AI security has to handle those layers too, but it also has to address probabilistic model behavior, adversarial inputs, data poisoning, prompt injection and model drift. Those risks depend on data, prompts and model behavior in ways older control sets do not fully cover.\u003C/p\u003E"},{"title":"What frameworks govern AI security?","richText":"\u003Cp\u003EThe most commonly referenced frameworks include NIST AI RMF 1.0, ISO/IEC 42001, OWASP Top 10 for LLM Applications and the EU AI Act. They do different jobs: risk management, management-system governance, threat modeling and regulation.\u003C/p\u003E"},{"title":"How do you evaluate AI security tools?","richText":"\u003Cp\u003ELook for continuous asset discovery, AI-SPM capabilities, model and dependency scanning, prompt injection testing, supply chain analysis, policy enforcement, runtime monitoring and integration with existing SIEM and SOAR workflows. The right tool should fit into operational security, not sit beside it as a disconnected console.\u003C/p\u003E"},{"title":"What role does zero trust play in AI security?","richText":"\u003Cp\u003EZero trust principles still apply — verify explicitly, grant least-privilege access and assume breach. 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