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윤리 주요 주제:\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-c490bcc20d","additionalClasses":"related-topics ","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/\"\u003E데이터 거버넌스 정책\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/templates/\"\u003E정책 템플릿\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/standards/\"\u003E거버넌스 표준\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/audit/\"\u003E데이터 거버넌스 감사\u003C/a\u003E\u003C/li\u003E\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/data-governance/data-privacy/\"\u003E데이터 프라이버시\u003C/a\u003E\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_549182334":{"id":"flexible-column-container-b2d9ed4132","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text":{"id":"text-4f711cad28","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003E데이터 윤리의 정의\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E데이터 윤리는 분석, AI, 비즈니스 운영 전반에서 데이터가 수집, 사용, 공유, 거버넌스되는 방식에 도덕적 원칙을 적용하는 실천입니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_policy-overview_0":{"id":"text-71e34034ad","text":"\u003Cp\u003E데이터 윤리는 데이터 활용에 관한 결정이 파이프라인, 모델, 애플리케이션, 공유 데이터 제품에 고정되기 전에 조직이 방어 가능한 선택을 내릴 수 있도록 돕습니다.\u003C/p\u003E\n\u003Cp\u003E조직이 데이터 윤리를 중요하게 여기는 이유는 데이터 활용이 이제 신뢰, 리스크, 의사 결정의 방향을 좌우하기 때문입니다. 고객, 직원, 규제 기관, 비즈니스 파트너는 데이터가 설명 가능하고, 비례적이며, 원래 목적에 부합하는 방식으로 사용되기를 점점 더 기대하고 있습니다. \u003C/p\u003E\n\u003Cp\u003EAI와 ML 시스템이 데이터 기반 결정을 자동화된 결과로 전환하는 범위가 확대되면서, 데이터 계층의 윤리적 공백은 빠르게 확산될 수 있습니다. 특히 학습 데이터에 편향이 반영되어 있거나, 민감한 속성이 검토 없이 파이프라인에 유입되거나, 데이터가 승인된 목적을 넘어 재사용될 때 그 위험은 더욱 커집니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-a-data-governance-policy":{"id":"title-v2-61d1d0b22a","additionalClasses":"anchor-title anchor-title--what-is-a-data-ethics","type":"heading2","lines":["데이터 윤리란?"],":type":"snowflake-site/components/title-v2"},"text_what-is-a-data-governance-policy_0":{"id":"text-68e0161ec0","text":"\u003Cp\u003E데이터 윤리는 데이터의 수집, 저장, 사용, 공유 방식에 관한 결정에 도덕적 원칙을 적용하는 것입니다. 데이터 윤리는 거버넌스 프로그램에 가치 계층을 부여합니다. 이를 통해 팀은 데이터가 분석, AI, 비즈니스 워크플로우를 이동할 때 법적으로 요구되는 사항뿐 아니라 무엇이 적절하고 비례적이며 책임 있는지 판단할 수 있습니다. 윤리적 약속을 거버넌스 정책과 플랫폼 제어로 전환하면, 조직은 위해 발생 가능성을 낮추면서 데이터를 더 확신 있게 활용할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E실무에서 데이터 윤리는 거버넌스 정책을 작성하기 전부터 시작됩니다. 조직은 먼저 데이터로 무엇을 할지, 무엇을 하지 않을지 정의해야 합니다. 여기에는 동의가 필요한 활용 방식, 최소화해야 할 민감한 속성, 검토 없이 AI 학습에 재사용해서는 안 되는 데이터 세트, 데이터 제품이 고객, 직원 또는 환자에게 영향을 미칠 때 남겨야 할 감사 추적이 포함됩니다. 이러한 약속은 이후 수집, 액세스, 보존, 공유, 마스킹, 검토에 관한 규칙과 같은 거버넌스 제어로 구현되어야 합니다.\u003C/p\u003E\r\n\u003Cp\u003E마지막 단계는 집행입니다. 민감한 인구통계학적 속성을 제한해야 한다는 정책은 데이터 플랫폼이 해당 속성을 식별하고, 태그를 적용하며, 마스킹 또는 행 수준 액세스 정책을 지원하고, 다운스트림 사용 및 액세스 활동에 대한 가시성을 제공할 때 더 효과적으로 작동합니다.\u003C/p\u003E\r\n\u003Cp\u003E데이터 윤리는 개인정보 보호, 규정 준수, 거버넌스와 관련되어 있지만 서로 동일한 개념은 아닙니다. \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E개인정보 보호\u003C/b\u003E는 주로 개인 데이터를 무단 액세스나 오용으로부터 보호하는 데 초점을 둡니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E규정 준수\u003C/b\u003E는 법적 의무를 정의합니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E거버넌스\u003C/b\u003E는 데이터의 전체 수명 주기에서 데이터를 관리하는 역할, 정책, 기술적 제어를 제공합니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E데이터 윤리\u003C/b\u003E는 어떤 데이터를 수집해야 하는지, 누가 액세스해야 하는지, 얼마나 오래 보존해야 하는지, 새로운 활용에 추가적인 사람의 검토가 필요한 시점은 언제인지 등 이러한 메커니즘 뒤의 선택을 이끄는 기준을 제공합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EAI는 데이터 결정이 가져오는 윤리적 결과를 훨씬 더 가시적으로 만들었습니다. 편향된 학습 데이터 세트는 채용 추천, 크레딧 결정, 헬스케어 트리아지 워크플로우에 대규모로 영향을 미칠 수 있습니다. 한 가지 목적을 위해 수집된 고객 속성이 자동화된 의사 결정의 입력이 될 수 있습니다. 모델 파이프라인은 기술적으로 허용되지만 윤리적으로 정당화하기 어려운 방식으로 데이터를 재사용할 수 있습니다. 데이터 윤리는 이러한 결정이 시스템에 내재되어 검토하기 어려워지기 전에 조직이 이를 점검하도록 돕습니다.\u003C/p\u003E\r\n\u003Cp\u003EEU 인공지능법은 특정 고위험 AI 시스템에 대해 \u003Ca href=\"https://snowflake.com/ko/data-governance/\"\u003E데이터 거버넌스\u003C/a\u003E 요구 사항을 포함합니다. 여기에는 학습, 검증, 테스트 데이터 세트, 데이터 수집 프로세스, 데이터 준비, 잠재적 편향, 개인 데이터 수집의 원래 목적과 관련된 관행이 포함됩니다. \u003Ca href=\"https://www.nist.gov/itl/ai-risk-management-framework\"\u003ENIST의 AI 리스크 관리 프레임워크\u003C/a\u003E 또한 거버넌스, 매핑, 측정, 관리와 같은 기능을 통해 \u003Ca href=\"https://www.snowflake.com/ko/fundamentals/ai-governance/\"\u003EAI 거버넌스\u003C/a\u003E를 조직의 리스크 관리 관행과 연결합니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003E데이터 클라우드 팟캐스트에서 ADP의 최고 데이터 책임자(CDO) Jack Berkowitz가 \u003Ca href=\"https://www.snowflake.com/en/podcast/jack-berkowitz/\"\u003E데이터 공유와 알고리즘에 윤리를 적용하는 방법을 논의하는 내용을 들어보세요\u003C/a\u003E.\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-a-data-governance-policy-matters":{"id":"title-v2-c3ef0454c1","additionalClasses":"anchor-title anchor-title--principles-data-ethics","type":"heading2","lines":["데이터 윤리의 원칙"],":type":"snowflake-site/components/title-v2"},"text_why-a-data-governance-policy-matters_0":{"id":"text-c2e06ac17b","text":"\u003Cp\u003E데이터 윤리 프로그램은 조직, 산업, 규제 환경에 따라 다르지만, 대부분 공통된 원칙 위에 구축됩니다. 이러한 원칙은 데이터 팀, 스튜어드, 법무팀, 비즈니스 리더가 데이터 활용에 관한 결정을 파이프라인, 모델, 애플리케이션에 내재화하기 전에 일관되게 내릴 수 있도록 돕습니다.\u003C/p\u003E\r\n\u003Ch3\u003E책임 있는 데이터 활용\u003C/h3\u003E\r\n\u003Cp\u003E책임 있는 데이터 활용은 위해를 최소화하고, 권리를 존중하며, 명확히 정의된 목적에 부합하는 방식으로 데이터를 수집, 저장, 분석하겠다는 운영상의 약속입니다. 이를 통해 윤리적 의도는 파이프라인 수준에서 실행 가능한 결정으로 전환됩니다. 어떤 데이터가 워크플로우에 들어오는지, 어떤 필드를 보존할지, 어떤 팀이 액세스할 수 있는지, 어떤 다운스트림 활용을 허용할지가 그 결정에 포함됩니다.\u003C/p\u003E\r\n\u003Cp\u003E책임 있는 데이터 활용의 중심에는 일반적으로 네 가지 의무가 있습니다:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E적법한 수집: \u003C/b\u003E데이터는 유효한 법적 근거, 명시적 동의 또는 승인된 기타 정당화 사유에 따라 수집되어야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E목적 제한: \u003C/b\u003E데이터는 명시되었거나 승인되었거나 합리적으로 예상 가능한 목적에 한해서만 사용되어야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E비례성: \u003C/b\u003E팀은 특정 과업에 필요한 데이터만 수집하고 보존해야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E피해 최소화: \u003C/b\u003E조직은 데이터 제품, 분석 워크플로우 또는 AI 시스템을 배포하기 전에 예측 가능한 다운스트림 피해를 평가해야 합니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003E데이터 존엄성\u003C/h3\u003E\r\n\u003Cp\u003E데이터 존엄성은 개인이 자신에 관한 데이터가 어떻게 사용되는지 이해하고 그 사용 방식에 영향을 미칠 수 있어야 한다는 원칙입니다. 이는 데이터를 단순히 추출, 결합, 재사용할 자산이 아니라 개인의 자율성과 맥락에 연결된 것으로 봅니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 개인정보 보호와 겹치는 부분이 있지만, 동일한 개념은 아닙니다. 개인정보 보호는 개인 데이터를 무단 액세스나 오용으로부터 보호하는 데 중점을 둡니다. 데이터 존엄성은 승인된 사용이라 하더라도 해당 데이터가 나타내는 사람을 존중하는지 묻습니다. 예를 들어 환자 기록이 안전하게 저장되고 승인된 사용자만 열람할 수 있더라도, 충분한 인지나 동의 없이 해당 기록을 상업용 AI 시스템 학습에 사용하는 것은 여전히 존엄성 측면의 우려를 낳을 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E실무적으로 데이터 존엄성은 동의 설계, 목적 명시, 데이터 최소화, 데이터 주체의 권리에 영향을 미칩니다. 동의는 약관 속에 묻혀 있는 형식적 절차가 아니라 실질적 의미를 가져야 합니다. 목적 설명은 향후 사용을 안내할 수 있을 만큼 구체적이어야 합니다. 데이터 수집은 과업 수행에 필요한 범위로 제한되어야 합니다. 또한 개인은 자신의 데이터가 어떻게 사용되는지 이해하고, 이의를 제기하거나 영향을 미칠 수 있는 적절한 방법을 가져야 합니다. 특히 그 사용이 서비스, 기회 또는 의사결정에 대한 액세스에 영향을 줄 때 더욱 중요합니다.\u003C/p\u003E\r\n\u003Ch3\u003E투명성과 개방형 데이터 거버넌스\u003C/h3\u003E\r\n\u003Cp\u003E투명성은 조직 안팎의 이해관계자가 데이터 사용 방식을 이해할 수 있게 합니다. 여기에는 계보 기록, 데이터 프로비넌스, 카탈로그 메타데이터, 모델 설명서, 거버넌스 승인, 감사 로그가 포함될 수 있습니다. 투명성의 목표는 검토자, 스튜어드, 규제기관, 파트너 또는 데이터 소비자에게 필요한 정보를 적절히 보이게 하는 것입니다.\u003C/p\u003E\r\n\u003Cp\u003E개방형 데이터 거버넌스는 공개적으로 액세스할 수 있거나 연구, 책임성 또는 협업을 위해 공유되는 데이터에 투명성의 원칙을 적용합니다. 개방형 데이터는 신뢰, 학술 연구, 민주적 책임성을 뒷받침할 수 있지만, 제한 없는 개방성은 PII, 독점적 비즈니스 로직 또는 민감한 공공 부문 정보를 노출할 수도 있습니다. 윤리적인 개방형 데이터 프로그램은 접근성과 리스크 통제 간의 균형을 맞춥니다. \u003Ca href=\"https://snowflake.com/en/data-governance/frameworks/fair-principles/\"\u003EFAIR 원칙\u003C/a\u003E, 즉 검색 가능성, 접근 가능성, 상호 운용성, 재사용 가능성은 책임 있는 개방형 데이터 관행을 이끄는 기준으로 자주 활용됩니다. \u003C/p\u003E\r\n\u003Cp\u003E실무에서는 \u003Ca href=\"https://snowflake.com/ko/data-governance/data-catalog/\"\u003E데이터 카탈로그\u003C/a\u003E를 사용해 민감한 필드를 노출하지 않으면서 풍부한 메타데이터를 게시하고, 감독을 위해 규제기관이 거버넌스 감사 로그에 액세스할 수 있도록 하며, 명확한 프로비넌스 설명서가 포함된 버전 관리 데이터 세트를 유지하는 것이 포함됩니다. 이를 통해 사용자는 데이터가 시간이 지나며 어떻게 생성, 변환, 업데이트되었는지 이해할 수 있습니다.\u003C/p\u003E\r\n\u003Ch3\u003E공정성\u003C/h3\u003E\r\n\u003Cp\u003E공정성은 데이터 기반 시스템이 적절하고 정당화 가능하며 보호 대상 또는 취약 집단에 체계적 피해를 주지 않는 결과를 만들어내는지에 초점을 둡니다. 분석과 AI에서 공정성은 데이터와 그 데이터를 사용하는 시스템 모두에 달려 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E예를 들어 과거 인재채용 데이터로 학습된 채용 모델은 학습 데이터가 이전의 배제나 기회에 대한 불균등한 액세스를 반영할 경우, 해로운 과거 패턴을 재현할 수 있습니다. 또는 헬스케어 트리아지 모델은 기초 데이터가 특정 집단을 충분히 대표하지 못할 경우, 집단별로 서로 다른 성능을 보일 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E공정성은 하나의 기술 설정으로 해결되지 않습니다. 공정성의 정의는 서로 충돌할 수 있습니다. 해당 예로는 다음을 들 수 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E인구통계학적 동등성\u003C/b\u003E은 결과가 집단 간에 균등하게 분포되는지를 묻습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E균등화 승산\u003C/b\u003E은 집단 간 오류율이 유사한지에 초점을 둡니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E개인 공정성\u003C/b\u003E은 유사한 개인이 유사한 대우를 받는지를 묻습니다. \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E팀은 의사결정 맥락에 맞는 공정성 기준을 선택하고, 그 선택이 왜 적절한지 문서화해야 합니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_core-components-of-a-data-governance-policy":{"id":"title-v2-0622f4be10","additionalClasses":"anchor-title anchor-title--data-ethics-risks-in-ai-and-analytics","type":"heading2","lines":["AI와 분석의 데이터 윤리 리스크"],":type":"snowflake-site/components/title-v2"},"text_core-components-of-a-data-governance-policy_0":{"id":"text-4ff742ba33","text":"\u003Cp\u003E윤리적 리스크는 데이터가 하나의 맥락에서 다른 맥락으로 이동할 때 자주 나타납니다. 이러한 리스크는 데이터 선택이 결과를 대규모로 좌우할 수 있는 AI와 ML에서 특히 두드러집니다.\u003C/p\u003E\n\u003Ch3\u003E데이터 편향\u003C/h3\u003E\n\u003Cp\u003E데이터 편향은 데이터 세트에 존재하는 체계적 오류로, 분석 또는 모델 결과를 특정 방향으로 왜곡할 수 있습니다. 편향은 샘플링 공백, 역사적 불평등, 측정 오류, 라벨링 관행 또는 새로운 용도를 염두에 두지 않고 설계된 비즈니스 프로세스에서 비롯될 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E따라서 편향은 단순한 ML 문제가 아니라 거버넌스 문제입니다. 데이터 사이언티스트가 모델을 학습시키는 시점에는 편향과 관련된 많은 결정이 이미 데이터에 내재되어 있을 수 있습니다. 어떤 집단이 포함되었는지, 어떤 필드가 수집되었는지, 어떤 라벨이 적용되었는지, 어떤 기록이 제외되었는지, 어떤 역사적 결과가 ground truth로 간주되었는지가 이미 결정되어 있는 경우가 많습니다.\u003C/p\u003E\n\u003Cp\u003E데이터 윤리를 실천하려면 수명 주기 초기에 검토가 이뤄져야 합니다. 팀은 데이터의 출처, 수집 목적, 알려진 대표성의 공백, 레이블이나 결과에 깔린 가정을 이해해야 합니다. AI 환경에서는 이러한 접근이 학습 데이터 품질, 대표성, 편향 완화에 대한 규제 및 리스크 관리 기대치와 맞닿아 있습니다.\u003C/p\u003E\n\u003Ch3\u003E알고리즘 공정성\u003C/h3\u003E\n\u003Cp\u003E알고리즘 공정성은 데이터 세트 자체보다 모델 출력에 초점을 둡니다. 즉, 시스템이 해당 데이터를 사용해 의사결정을 내리거나 지원할 때 실제로 어떻게 작동하는지를 묻습니다.\u003C/p\u003E\n\u003Cp\u003E실무자는 보통 여러 점검 지점에서 공정성을 평가합니다.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E학습 전에는 관련 집단이 충분히 대표되고 있는지 파악하기 위해 데이터 구성을 감사할 수 있습니다. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E모델 평가 단계에서는 코호트별로 출력에 차별적 영향이 나타나는지 테스트할 수 있습니다. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E프로덕션 단계에서는 드리프트, 변화하는 오류율, 예상치 못한 격차를 감지하기 위해 결과를 모니터링할 수 있습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E민감 필드를 제거한다고 해서 리스크가 반드시 사라지는 것은 아닙니다. 다른 변수가 대리 변수로 작동할 수 있기 때문입니다. 예를 들어 모델이 인종, 성별, 장애 여부, 소득을 직접 사용하지 않더라도 위치, 교육 이력, 구매 행동, 고용 패턴은 여전히 보호 속성과 상관관계를 가질 수 있습니다. 따라서 윤리적 AI 거버넌스에는 데이터 수준의 제어와 출력 수준의 모니터링이 모두 필요합니다.\u003C/p\u003E\n\u003Cp\u003E알고리즘 공정성에 관한 결정도 설명서 형태로 문서화해야 합니다. 팀이 인구통계학적 동등성 대신 equalized odds를 선택했다면, 그 선택에는 의사결정 컨텍스트, 수용 가능한 절충점, 조직이 줄이고자 하는 피해에 대한 가정이 반영됩니다. 데이터 윤리는 이러한 선택이 기술 워크플로우 안에 숨겨지지 않도록 합니다.\u003C/p\u003E\n\u003Cp\u003ESnowflake의 수석 데이터 전략가(Principal Data Strategist) Jennifer Belissent는 책임 있는 AI가 데이터 파운데이션에 어떻게 의존하는지 다음과 같이 설명합니다. “새로운 AI 환경에서의 성공은 반짝이는 새 도구 하나에만 달려 있지 않습니다. 그 도구가 구축될 기반에도 달려 있습니다. AI와 생성형 AI를 성공적이고 책임 있게 활용하기 위한 기반은 데이터 보안, 데이터 다양성, 조직적 성숙도 위에 세워져야 합니다.”\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"quote_data_governanc":{"id":"quote-item-2813fa9c42","alignment":"left","layout":"simple","quoteSourceName":"Jennifer Belissent","quoteSourceTitle":"Principal Data Strategist, Snowflake","showQuoteIcon":true,"quote":"The foundation for the successful and responsible use of AI and gen AI must be based on data security, data diversity and organizational maturity.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"text_966706563":{"id":"text-4e4fb19583","text":"\u003Ch3\u003E편향되거나 존엄성을 침해하는 모델 출력\u003C/h3\u003E\n\u003Cp\u003E충분한 검토 없이 민감 데이터가 AI 워크플로우에 들어가면 편향 리스크와 존엄성 침해 리스크가 맞물릴 수 있습니다. 모델은 특정 집단에 불리한 출력을 생성하거나, 최소화했어야 할 정보를 노출하거나, 수집 당시의 본래 목적과 맞지 않는 방식으로 개인 데이터를 사용할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E바로 이 때문에 거버넌스 제어가 중요합니다. 행 수준 액세스 정책, 마스킹 정책, 오브젝트 태그는 어떤 인구통계학적, 건강, 재무 또는 행동 속성이 모델 학습 파이프라인에 도달할지 통제하는 데 도움이 됩니다. 리니지를 활용하면 민감 필드가 거버넌스가 적용된 소스에서 파생 테이블, 피처 세트 또는 애플리케이션으로 이동했는지 추적할 수 있습니다. 액세스 이력은 누가 데이터 세트를 쿼리했는지, 그리고 언제 쿼리했는지를 보여줄 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E제어만으로 모든 윤리적 질문이 해결되지는 않지만, 검토와 집행, 책임성이 작동할 수 있는 조건을 마련합니다. 이러한 제어가 없으면 데이터 윤리는 각 인계 지점에서 개인의 판단에 의존하게 됩니다. 반대로 제어가 갖춰지면 윤리적 약속을 반복 가능한 규칙으로 전환할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-26441004fa","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003E자주 하는 실수\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E조직은 민감 데이터 태깅과 분류 정의에 투자하지만, 정작 그 태그를 액세스 제어, 마스킹, 보존, 검토 워크플로우와 연결하는 단계까지 나아가지 못하는 경우가 많습니다. 그 결과 데이터에는 올바른 레이블이 붙어 있어도 실제 처리는 여전히 잘못될 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_how-legal-privacy-and-usage-requirements-shape-governance-policy":{"id":"title-v2-6eee12a930","additionalClasses":"anchor-title anchor-title--how-organizations-operationalize-data-ethics","type":"heading2","lines":["조직이 데이터 윤리를 운영에 내재화하는 방법"],":type":"snowflake-site/components/title-v2"},"text_303662111":{"id":"text-94f4c5c5bc","text":"\u003Cp\u003E데이터 윤리는 데이터가 수집, 쿼리, 공유, 재사용되는 워크플로우까지 닿아야 합니다. 실제로 조직은 서로 연결된 몇 가지 실천 방식을 통해 이를 운영화합니다.\u003C/p\u003E\n\u003Ch3\u003E가치 약속의 문서화\u003C/h3\u003E\n\u003Cp\u003E조직은 보통 데이터로 무엇을 할 것인지, 무엇을 하지 않을 것인지 정의하는 데서 출발합니다. 이러한 약속은 의사결정을 이끌 만큼 구체적이어야 합니다. 조직이 데이터를 책임 있게 사용한다는 일반적 선언보다, 민감 데이터 수집을 최소화하고 검토 없는 2차 사용을 피하며 자동화된 의사결정 시스템의 공정성 선택을 문서화하겠다는 명확한 약속이 훨씬 유용합니다.\u003C/p\u003E\n\u003Cp\u003E이러한 약속에는 책임 주체도 필요합니다. 데이터 스튜어드십은 지정된 담당자에게 도메인, 정의, 품질, 액세스, 정책 준수에 대한 책임을 부여합니다. 법무, 규정 준수, 보안, 비즈니스 팀이 약속을 정의하는 데 기여할 수 있지만, 스튜어드는 이를 실제 테이블, 필드, 파이프라인, 데이터 제품에 적용하도록 돕습니다.\u003C/p\u003E\n\u003Ch3\u003E약속을 거버넌스 정책에 반영하기\u003C/h3\u003E\n\u003Cp\u003E약속이 정의되면 조직은 무엇을 반드시 수행해야 하는지 명시하는 거버넌스 정책을 마련해야 합니다. 예를 들어 데이터 최소화 약속은 정해진 기간이 지나면 기록을 삭제하거나 아카이브하는 보존 정책으로 구체화될 수 있습니다. 존엄성에 대한 약속은 개인 데이터의 새로운 사용에 대해 동의를 검토하는 프로세스로 이어질 수 있습니다. 공정성에 대한 약속은 모델 배포 전에 학습 데이터 구성 감사를 요구할 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E정책은 데이터 수명 주기와 연결되어야 합니다. 수집 정책은 어떤 데이터를 어떤 법적 또는 윤리적 근거로 수집할 수 있는지 정의합니다. 액세스 정책은 민감 필드를 사용할 수 있는 주체를 정의합니다. 보존 정책은 데이터가 사용 가능한 상태로 유지될 수 있는 기간을 정의합니다. 공유 정책은 데이터를 게시, 교환하거나 파트너에게 제공할 수 있는 시점을 정의합니다. 검토 정책은 새로운 사용 방식에 승인이 필요한 시점을 정의합니다.\u003C/p\u003E\n\u003Ch3\u003E플랫폼 통제를 통한 정책 시행\u003C/h3\u003E\n\u003Cp\u003E윤리 정책을 수동 검토에만 의존해 운영하면 지속하기 어렵습니다. 플랫폼 통제는 데이터가 저장, 쿼리, 공유, 사용되는 지점에서 정책을 시행하는 데 도움이 됩니다.\u003C/p\u003E\n\u003Cp\u003E마스킹 정책은 민감한 열의 노출을 줄일 수 있습니다. 행 수준 액세스 정책은 특정 사용자나 역할이 볼 수 있는 레코드를 제한할 수 있습니다. 오브젝트 태그는 민감 데이터, 승인된 사용 목적, 도메인 소유권, 보존 요건 또는 분류(Classification) 상태를 표시할 수 있습니다. 데이터 분류(Classification)는 잠재적 민감 데이터를 식별해 일관된 거버넌스 적용을 지원합니다. \u003C/p\u003E\n\u003Cp\u003E예를 들어, 정책은 민감한 인구통계학적 속성이 모델 학습에 광범위하게 사용되어서는 안 된다고 명시할 수 있습니다. 플랫폼 통제는 이러한 속성을 마스킹하고, 승인된 역할로 액세스를 제한하며, 해당 데이터가 어떻게 관리되는지를 보여주는 메타데이터를 보존할 수 있습니다.\u003C/p\u003E\n\u003Ch3\u003E리니지와 감사 추적으로 책임 있는 사용 입증\u003C/h3\u003E\n\u003Cp\u003E조직에는 증거도 필요합니다. 데이터 리니지 (혹은 데이터 계보) 데이터의 출처, 변경 과정, 그리고 어떤 다운스트림 자산이 이에 의존하는지를 보여줍니다. 감사 추적은 누가, 언제, 어떤 맥락에서 데이터에 액세스했는지를 보여줍니다. \u003C/p\u003E\n\u003Cp\u003E이 두 요소를 함께 활용하면 데이터가 명시된 목적에 따라 사용되었고, 거버넌스 대상 필드가 승인되지 않은 워크플로우로 눈에 띄지 않게 이동하지 않았음을 입증할 수 있습니다. 이러한 증거는 내부 책임성과 외부 감독을 모두 뒷받침합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-ad4d2f77db","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003E핵심 요약\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E데이터 윤리를 운영에 정착시키는 가장 신뢰할 수 있는 방법은 분류(Classification)와 의도를 집행 및 검토와 연결하는 것입니다. 민감 데이터에 태그를 지정하고, 해당 태그를 액세스, 마스킹, 보존 정책과 연결하며, 계보(Lineage)와 감사 추적으로 데이터가 분석(Analytics) 및 AI 워크플로우로 이동하는 과정에서 실제로 어떻게 사용되는지 검증해야 합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_how-legal-privacy-and-usage-requirements-shape-governance-policy_0":{"id":"text-00d009f7c3","text":"\u003Ch3\u003E맥락 변화에 따른 데이터 활용 검토\u003C/h3\u003E\n\u003Cp\u003E데이터는 이동하고, 비즈니스 요구는 바뀌며, AI 시스템은 새로운 형태의 재사용을 만들어냅니다. 수집 단계에서는 위험이 낮았던 데이터도 결합되거나, 재학습에 사용되거나, 대규모로 프로덕션에 투입되면 민감해질 수 있습니다.\u003C/p\u003E\n\u003Cp\u003E지속적인 검토는 조직이 정책을 맥락에 맞게 유지하도록 돕습니다. 여기에는 정기적인 액세스 검토, 보존 검토, 데이터 제품 자격 인증, 공정성 감사, 계보(Lineage) 검토, 새로운 사용 사례에 대한 승인 워크플로우가 포함될 수 있습니다. 윤리적 질문에는 규칙만으로 완전히 포착할 수 없는 맥락이 수반되는 경우가 많기 때문에, 사람의 감독은 여전히 중요합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2":{"id":"card-v2-09f34cafdb","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003EMerkle은 민감한 마케팅 데이터, 거버넌스, 협업(Collaboration) 워크플로우를 Snowflake로 통합해 데이터 개발 주기를 64% 개선하고 최대 20%의 비용 절감을 달성했습니다(2025년 3월 기준). 또한 이 팀은 Snowflake Cortex의 Document AI로 RFP 응답 솔루션을 구축해 최소 25명의 팀원이 수행하던 데이터 입력 부담을 줄였습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"title":{"id":"title","type":"heading4","lines":["고객 사례: Merkle "],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/en/customers/all-customers/case-study/merkle/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"전체 활용 사례 읽기"},"image":{"id":"image","alt":"merkle customer logo","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--37461af2-131c-4f09-a134-dc5d00f88e7d/merkle-switzerland-ag%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card","layoutStyle":"horizontal",":type":"snowflake-site/components/card-v2"},"title_operational-policy-areas":{"id":"title-v2-ec7ed7c357","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-data-ethics","type":"heading2","lines":["Snowflake가 데이터 윤리를 지원하는 방식"],":type":"snowflake-site/components/title-v2"},"text_operational-policy-areas_0":{"id":"text-4038453ea1","text":"\u003Cp\u003ESnowflake는 데이터 환경 내에서 거버넌스 정책, 메타데이터, 통제를 연결함으로써 조직이 데이터 윤리를 운영화할 수 있도록 지원합니다. \u003C/p\u003E\r\n\u003Ch3\u003E거버넌스 컨텍스트의 중앙화\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/ko/product/features/horizon/\"\u003ESnowflake Horizon 카탈로그\u003C/a\u003E는 AI 데이터 클라우드 전반에서 팀이 데이터, 앱, 모델을 발견하고 이해하며 거버넌스할 수 있도록 지원합니다. Horizon 카탈로그는 분류, 오브젝트 태그, 정책, 소유권, 계보(Lineage)와 같은 메타데이터를 표면화하여, 데이터가 분석(Analytics), AI 또는 데이터 공유 워크플로우에 사용되기 전에 데이터 스튜어드와 소비자가 더 풍부한 컨텍스트를 확보할 수 있게 합니다.\u003C/p\u003E\r\n\u003Ch3\u003E책임 있는 데이터 활용 시행\u003C/h3\u003E\r\n\u003Cp\u003ESnowflake는 책임 있는 AI 약속과 거버넌스 통제를 결합하여, 팀이 윤리 원칙을 데이터 계층에서 직접 적용할 수 있도록 지원합니다. 동적 데이터 마스킹은 민감한 열의 불필요한 노출을 줄이고, 행 액세스 정책은 사용자나 역할이 볼 수 있는 레코드를 제한할 수 있습니다. Object Tagging과 Classification은 관리 대상 데이터를 식별하고, 데이터 제품 및 파이프라인 전반에 걸쳐 통제를 보다 일관되게 적용할 수 있도록 돕습니다.\u003C/p\u003E\r\n\u003Ch3\u003E데이터 활동 감사 및 검토\u003C/h3\u003E\r\n\u003Cp\u003E책임 있는 데이터 활용에도 증거가 필요합니다. Access History 뷰, Object Tagging, 계보와 같은 Snowflake 기능은 누가 데이터에 액세스했는지, 관리 대상 데이터가 어디로 이동했는지, 어떤 다운스트림 자산이 해당 데이터에 의존하는지를 팀이 확인할 수 있도록 돕습니다. 이 감사 추적은 스튜어드십 검토, 규정 준수 워크플로우, 데이터가 승인된 목적에 맞게 사용되었는지에 대한 조사를 지원할 수 있습니다.\u003C/p\u003E\r\n\u003Ch3\u003E책임 있는 AI 워크플로우 지원\u003C/h3\u003E\r\n\u003Cp\u003EAI 사용 사례에서는 데이터가 모델이나 애플리케이션에 들어가기 전에 거버넌스 컨텍스트가 중요합니다. Snowflake는 데이터가 저장되고 처리되는 동일한 환경에서 AI 워크플로우에 데이터 거버넌스 통제를 적용할 수 있도록 지원하며, Cortex Guard는 Snowflake Cortex AI로 구축된 LLM 기반 애플리케이션의 콘텐츠 안전성을 지원합니다. 이러한 기능을 함께 활용하면 팀은 책임 있는 AI 관행을 그 기반이 되는 거버넌스된 데이터 파운데이션과 연결할 수 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_building-deploying-and-maintaining-your-policy":{"id":"title-v2-457f4245f4","additionalClasses":"anchor-title anchor-title--data-ethics-depends-on-operational-governance","type":"heading2","lines":["데이터 윤리의 기반은 운영형 거버넌스"],":type":"snowflake-site/components/title-v2"},"text_building-deploying-and-maintaining-your-policy_0":{"id":"text-bf974ac875","text":"\u003Cp\u003E데이터 윤리는 어떤 데이터를 수집하고, 어떻게 분류하며, 누가 액세스할 수 있는지, 어디로 이동하는지, 새로운 활용에 검토가 필요한 시점은 언제인지 결정하는 실무적 판단의 기준이 되어야 합니다. 데이터 스튜어드가 계보를 추적하고, 태그를 연결하고, 마스킹 정책을 적용하며, 액세스를 검토하고, 목적을 문서화할 수 있다면 조직은 별도의 승인 프로세스가 아니라 일상 운영 안에 윤리적 데이터 활용 관행을 더 자연스럽게 내재화할 수 있습니다. \u003C/p\u003E\n\u003Cp\u003E그 결과 책임 있는 분석, AI 개발, 데이터 공유를 위한 기반이 한층 강화되고, 조직은 더 큰 신뢰와 책임성을 바탕으로 데이터를 활용할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_1679185601":{"id":"text-c908f51006","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cb\u003E핵심 요약\u003C/b\u003E\u003C/p\u003E\n\u003Cp\u003E데이터 윤리는 공정성, 투명성, 책임 있는 사용과 같은 원칙을 데이터 수명 주기 전반에서 집행 가능한 거버넌스 제어로 전환할 때 실행 가능한 체계가 됩니다. 정책을 태그 지정, 액세스 제어, 계보, 감사 가능성과 결합하면 조직은 리스크를 줄이고 신뢰를 강화하며, 더 책임 있는 AI와 분석을 대규모로 지원할 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["text","text_policy-overview_0","title_what-is-a-data-governance-policy","text_what-is-a-data-governance-policy_0","title_why-a-data-governance-policy-matters","text_why-a-data-governance-policy-matters_0","title_core-components-of-a-data-governance-policy","text_core-components-of-a-data-governance-policy_0","quote_data_governanc","text_966706563","text_copy","title_how-legal-privacy-and-usage-requirements-shape-governance-policy","text_303662111","text_copy_copy","text_how-legal-privacy-and-usage-requirements-shape-governance-policy_0","card_v2","title_operational-policy-areas","text_operational-policy-areas_0","title_building-deploying-and-maintaining-your-policy","text_building-deploying-and-maintaining-your-policy_0","text_1679185601"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"container":{"additionalClasses":"sticky-sidebar","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-2594758170","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text_943981956_copy_":{"id":"text-bba64d5a9f","additionalClasses":"eyebrow-text","text":"\u003Cp\u003E이 가이드의 주요 내용\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-9d10de5bea","additionalClasses":"page-toc","text":"\u003Cul\u003E\n\u003Cli data-anchor=\"what-is-a-data-ethics\"\u003E데이터 윤리란 무엇인가\u003C/li\u003E\n\u003Cli data-anchor=\"principles-data-ethics\"\u003E데이터 윤리의 원칙\u003C/li\u003E\n\u003Cli data-anchor=\"data-ethics-risks-in-ai-and-analytics\"\u003EAI와 분석의 데이터 윤리 리스크\u003C/li\u003E\n\u003Cli data-anchor=\"how-organizations-operationalize-data-ethics\"\u003E조직이 데이터 윤리를 운영화하는 방법\u003C/li\u003E\n\u003Cli data-anchor=\"how-snowflake-supports-data-ethics\"\u003ESnowflake가 데이터 윤리를 지원하는 방식\u003C/li\u003E\n\u003Cli data-anchor=\"data-ethics-depends-on-operational-governance\"\u003E데이터 윤리의 기반은 운영형 거버넌스\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"flexible_column_cont_132267907":{"id":"flexible-column-container-0ea002c2c2","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2_copy":{"id":"title-v2-ccb2ff80d4","additionalClasses":"hub-faq__headline","type":"heading2","lines":["자주 묻는 질문"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-2216c19949","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003E데이터 윤리에 대한 자주 묻는 질문에 Snowflake 전문가가 답합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-d5e8930fbc","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"데이터 윤리와 개인정보 보호는 어떻게 다른가요?","richText":"\u003Cp\u003E개인정보 보호는 주로 \u003Ca href=\"https://gdpr.eu/what-is-gdpr/\" target=\"_blank\"\u003EGDPR\u003C/a\u003E 또는 \u003Ca href=\"https://oag.ca.gov/privacy/ccpa\" target=\"_blank\"\u003E캘리포니아 소비자 개인정보 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