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내용"],":type":"snowflake-site/components/title-v2"},"text_800689963":{"id":"text-b7c164eee0","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E거버넌스 안의 AI:\u003C/b\u003E AML, 부정 탐지 시스템 등 미션 크리티컬 데이터를 다루는 환경에서, 3년간 쌓아온 Snowflake RBAC 거버넌스 위에 Cortex Code 기반 AI 실행 계층을 얹어 별도 인프라 구축이나 데이터 복제 없이 안전하게 전사 AI 자동화를 시작했습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E측정된 효과:\u003C/b\u003E Slack·Google Drive·Confluence·Jira에 흩어진 업무 지식을 LLM Wiki, RAG, Graph DB로 연결하고 Harness·Skill·Evaluation 체계로 표준화해, 휴먼 에러 90% 감소, 작업 준비 시간 80% 단축, 서비스 중단 시간 50% 단축이라는 성과를 달성했습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E개인의 경험을 조직의 표준으로:\u003C/b\u003E 개발자 중심의 인프라 자동화를 넘어 CoWork와 n8n을 통해 비개발자인 구매 담당자까지 라이선스 자동화를 직접 구현하며, AI 역량을 조직 전체로 확산시켰습니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"}},":itemsOrder":["title_v2","text_800689963"],"appliedCssClassNames":"ui-background-05 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AI가 가장 잘하는 일은 새로운 것을 창조하는 것이 아니라 좋은 사례를 복제하는 것이었습니다.\u003C/p\u003E\r\n\u003Cp\u003E조직 내부에는 더 근본적인 문제가 있었습니다. 엔지니어들이 업무 시간에 가장 많이 하는 일은 분석, 설계, 구축, 유지보수, 장애 처리, 운영처럼 반복적이고 루틴한 업무였는데, 같은 업무를 두고도 실수 없이 처리하는 사람과 매번 비슷한 실수를 반복하는 사람의 편차가 컸습니다. 이런 편차는 개인의 기술적 경험 차이, 담당 워크로드에 대한 이해도, 도메인 특성을 고려하는 정도에서 비롯되었습니다.\u003C/p\u003E\r\n\u003Cp\u003E이 편차를 줄이기 위해 조직은 체크리스트와 프로세스를 계속 추가했고, 그 결과 업무 흐름은 초안 작성, 1차 리뷰, 작업 기획, 재검토, 세부 절차 수립, PoC, 승인, 작업 실행, Live QA, 포스트모텀에 이르는 길고 복잡한 단계로 늘어났습니다.\u003C/p\u003E\r\n\u003Cp\u003E문제는 세상이 빠르게 변하는 동안 조직은 완성도를 지키려 할수록 더 느려졌다는 점입니다. 사람과 사람이 순차적으로 연결되는 업무 구조 속에서 엔지니어들은 기술을 연구하고 발전시켜야 할 시간에 문서 작업과 승인 대기에 쫓겼고, 일의 재미보다 스트레스가 커져갔습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_ns_2":{"id":"title-v2-b038aac9ac","type":"heading4","lines":["Snowflake를 선택한 배경: 거버넌스 안에서 바로 시작하는 AI"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_ns_2":{"id":"text-d7a3deeb47","text":"\u003Cp\u003E앤서스랩은 문제 해결의 본질을 지식을 새로 찾는 것이 아니라, 조직 내 이미 존재하고 입증된 성공 사례를 찾아 재사용하는 구조를 만드는 것이라고 정의했습니다. 이를 위해 흩어져 있는 업무 대화와 문서를 체계적으로 수집하고 지식 기반으로 축적해 나가면서, 도메인 지식이 반영된 원하는 수준의 결과물이 나오기 시작했습니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 AI 활용 범위를 확장하는 과정에서 Shadow AI로 인한 데이터 유출 위험과 기술 경계 파괴에 따른 보안 취약점이 부각되었습니다. 이에 앤서스랩은 별도의 AI 게이트웨이나 복잡한 신규 인프라를 구축하는 대신, 이미 전사 데이터 플랫폼으로 사용해 온 Snowflake의 견고한 거버넌스 및 RBAC(역할 기반 접근 제어) 환경 위에서 AI를 즉시 가동하는 길을 택했습니다.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake CoCo(Cortex Code)를 선택한 이유도 명확했습니다.\u003C/p\u003E\r\n\u003Cp\u003E첫째, 데이터가 이미 Snowflake에 올라가 있고 접근 권한만 있다면 Cortex를 통해 자연어로 '몇 년 치 핸드 데이터가 어디에 있는지 알려줘'라고 묻는 것만으로 데이터가 어느 테이블의 어떤 컬럼에 어떤 의미로 있는지 즉시 확인할 수 있었습니다. 많은 조직이 성장하면서 놓치기 쉬운 데이터 카탈로그, 스키마 정의, 글로서리(용어집) 문제를 별도로 구축하지 않고도 해결한 것입니다.\u003C/p\u003E\r\n\u003Cp\u003E둘째, 데이터를 외부로 옮기거나 별도 인프라를 구축하지 않고도 안전하게 AI를 쓸 수 있었습니다.\u003C/p\u003E\r\n\u003Cp\u003E셋째, CoCo는 Snowsight 안에서 바로 사용할 수 있을 뿐 아니라 별도의 CoCo CLI로도 쓸 수 있고, 이미 개발자들이 쓰던 Claude Code나 VS Code에는 플러그인·확장 프로그램만 설치하면 바로 연동되어 비개발자와 개발자 모두의 진입장벽을 낮췄습니다.\u003C/p\u003E\r\n\u003Cp\u003E넷째, Snowflake는 데이터·검색·모델·에이전트·평가·모니터링을 하나의 플랫폼에서 운영할 수 있는 통합된 AI 생태계를 제공했습니다. Claude, GPT, DeepSeek 등 다양한 AI 모델을 상황에 맞게 선택해 쓸 수 있고, 추가 인프라나 별도 도구 연결 없이 실행부터 평가, 모니터링까지 Snowflake 안에서 처리할 수 있었습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_ns_3":{"id":"title-v2-d67868aac7","type":"heading4","lines":["기록을 지식으로, 지식을 실행 표준으로"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_ns_3":{"id":"text-a522f7d714","text":"\u003Cp\u003E앤서스랩이 AI를 통해 이루고자 한 궁극적 목표는 '더 빠른 사람'을 만드는 것이 아니라, '최고의 성공 결과를 반복하는 조직', 즉 엔지니어 업무 결과의 상향 평준화였습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_ns_4":{"id":"title-v2-cb9ff57c16","type":"heading5","lines":["파편화된 기록을 업무를 복제하는 지식 기반으로: LLM Wiki에서 RAG, Graph DB까지"],":type":"snowflake-site/components/title-v2"},"text_ns_4":{"id":"text-2d7dce6798","text":"\u003Cp\u003E앤서스랩은 먼저 조직 곳곳에 흩어져 있던 업무 기록을 모으는 것부터 시작했습니다. 가장 많이 소통하는 채널인 Slack의 업무 대화와 장애 처리 대화, Google Drive에 쌓인 기획서·작업 절차서·결과 보고서, 그리고 개발 부서가 쓰는 Confluence와 Jira의 정보까지 API와 MCP를 통해 모두 수집했습니다.\u003C/p\u003E\r\n\u003Cp\u003E이렇게 모은 데이터는 처음에는 오픈소스 도구인 Obsidian을 활용해 LLM Wiki 형태로 정리했습니다. 이 안에서 RAG(검색 증강 생성)를 구성하자 원하는 데이터가 나오기 시작했지만, 정확히 일치하는 과거 사례(팩트)가 없는 비슷한 상황에서는 여전히 데이터를 찾기 어려운 한계가 있었습니다. 이를 보완하기 위해 임베딩 기반의 Vector DB를 도입했는데, 이번에는 검색되는 데이터의 양이 늘고 컨텍스트가 커지면서 오히려 LLM이 엉뚱한 답을 내놓는 문제가 발생했습니다. 앤서스랩은 지식과 지식을 서로 연결해야 한다는 결론에 도달했고, 최종적으로 Graph DB를 도입해 지식 간의 관계를 연결함으로써 검색 정확도를 끌어올렸습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"image":{"id":"image-36d10585ac","height":"112","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--aa768ef1-66e0-4fef-b5be-e84a1d41cadd/image2.png?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"819",":type":"snowflake-site/components/image"},"title_ns_5":{"id":"title-v2-b68ed3d975","type":"heading5","lines":["Harness · Skill · Evaluation: 반복 가능한 실행 시스템"],":type":"snowflake-site/components/title-v2"},"text_ns_5":{"id":"text-c70b2206f8","text":"\u003Cp\u003E지식 기반이 갖춰지자 앤서스랩은 이를 실제 업무 표준으로 바꾸는 작업에 들어갔습니다.\u003C/p\u003E\r\n\u003Cp\u003E최신 문서와 업무 이력, 시스템 제약을 컨텍스트로 연결하고, 작업별 역할과 도구, 검증 기준, 승인 경계를 하네스(Harness)로 구성한 뒤, 반복되는 기술과 절차를 팀이 공유할 수 있는 실행 단위인 스킬(Skill)로 배포했습니다. 그리고 실행 결과와 실패 사례를 다시 데이터로 반영해 지속적으로 개선하는 평가(Evaluation) 루프를 만들었습니다. 처음에는 이 평가 작업을 사람이 직접 했지만, 이후에는 에이전트에게 맡겨 요청이 들어올 때마다 자동으로 평가와 개선이 반복되도록 구성했습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"image_1753401542":{"id":"image-438286dea9","height":"295","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--71bf26ef-106e-4403-bda4-3b9484b2d2d2/image1.png?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"1999",":type":"snowflake-site/components/image"},"text_248339630":{"id":"text-87efdd6f26","text":"\u003Cp\u003E이 결과 '작업 계획서를 만들어줘', '이 설계를 검토해줘', '이런 설계가 필요한데 초안을 작성해줘'라고 요청하면, 조직이 중요하게 여겨온 도메인 지식까지 반영된 워크로드 기반의 설계 문서가 곧바로 만들어지는 수준에 도달했습니다. 한 번 잘한 일을 항상 잘하는 방식으로 바꾼 것입니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"title_ns_6":{"id":"title-v2-3e2d711b23","type":"heading5","lines":["대표 적용 사례: 복잡하고 반복적인 고위험 인프라 업무의 자동화"],":type":"snowflake-site/components/title-v2"},"text_ns_6":{"id":"text-593f253462","text":"\u003Cp\u003E이렇게 만들어진 Harness는 하나의 기능이 복잡해지면 모듈 단위로 세분화하고, 검증된 로직은 새로운 업무의 템플릿으로 재사용하는 방식으로 확장되었습니다. 그 결과 지금은 구축, 데이터 마이그레이션, 그리고 여러 DevOps·SRE 업무가 모두 개별 Harness 안에서 동작하고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E일하는 방식이 달라졌습니다. 배경과 목적, 할 일을 주면 AI가 계획부터 PoC까지 완결하고, 생성과 비판, 수정의 셀프 리뷰와 체크리스트 기반 사전 검증을 거칩니다. 사람은 승인과 최종 판단에 집중합니다. 특히 복잡하고 반복되는 고위험 업무에서 효과가 컸습니다. 대표적인 다섯 가지 사례는 다음과 같습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003E계정·VPC 간 리소스 이관:\u003C/b\u003E 개별 VPC나 레거시 계정에 있던 서비스를 통합 VPC나 전용 계정으로 옮기는 작업입니다. Kubernetes 환경에서는 엔드포인트가 바뀌고 ALB를 재구성할 때마다 매핑 작업이 발생하는데, 리스트업이 누락되면 장애로 이어지기 쉬웠던 이 과정을 이제는 AI가 꼼꼼하게 채워줍니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E정기점검 작업 오케스트레이션:\u003C/b\u003E 월 1회 점검 창에 순단이 필요한 작업들을 몰아넣고 담당자별로 나누는 작업입니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAurora/RDS 일괄 업그레이드·패치:\u003C/b\u003E 환경 단위로 Aurora MySQL 버전과 인스턴스 세대를 맞추는 작업으로, 대상과 배경 정보만 입력하면 반복되는 루프 안에서 작업 계획과 절차서가 자동으로 만들어집니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E레거시·유휴(Idle) 리소스 정리와 비용 절감:\u003C/b\u003E 히스토리가 끊겨 정리하지 못했던 인스턴스와 리소스를 사람이 아닌 AI가 지속적인 로그 분석을 통해 찾아내고 정리합니다. 삭제 전에는 사용 내역을 3중으로 검증하고 대체 경로를 먼저 구성한 뒤에야 리소스를 지우는 절차를 거쳐, AI 자동화가 실수로 운영 중인 리소스를 삭제할 위험을 구조적으로 차단했습니다.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003E데이터·AI 파이프라인 및 봇 개발:\u003C/b\u003E 지식과 지표, 커뮤니티 신호를 수집해 Slack과 Snowflake로 흘려보내는 파이프라인과 봇을 개발하는 작업입니다.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_ns_7":{"id":"title-v2-4ac65661d7","type":"heading5","lines":["개발자의 AI 역량을 모든 직원에게"],":type":"snowflake-site/components/title-v2"},"text_ns_7":{"id":"text-eadf4cb200","text":"\u003Cp\u003E앤서스랩은 개발 조직의 AI 역량을 구매 및 IT 어드민 등 비개발 직군으로 성공적으로 확산시켰습니다. Snowflake CoCo에서 정의된 스킬과 플러그인을 자율형 에이전트 환경인 Snowflake CoWork와 공유함으로써, 비개발자들도 직접 업무 자동화를 구현하기 시작했습니다.\u003C/p\u003E\r\n\u003Cp\u003E가장 대표적인 사례가 소프트웨어 라이선스 관리입니다. 기존에는 발급된 라이선스의 10~20%가 상시 유휴 상태였음에도, 회수 시도 때마다 '곧 사용할 것'이라는 답변이 돌아오면서 소모적인 실랑이가 반복되곤 했습니다. 담당자는 신청을 막기보다 발급을 더 빠르게 해주는 대신, 회수를 철저히 자동화하는 방향으로 정책을 바꾸기로 했습니다.\u003C/p\u003E\r\n\u003Cp\u003E코딩 경험이 없던 라이선스 담당자는 CoWork의 Skill과 Harness를 활용해 자동화 로직을 직접 구현했습니다. n8n 워크플로우 구조를 AI에게 공유하면, AI가 구성 방법과 필드 입력값을 상세히 가이드해 주었기 때문에 가능했습니다.\u003C/p\u003E\r\n\u003Cp\u003E그 결과 \u003Cb\u003E① 데이터 수집/자동화\u003C/b\u003E(API 자동화와 RPA·파일 드랍) → \u003Cb\u003E② match_key 기준 정규화 및 중복 제거 → ③ 30일 이상 미접속 계정 필터링 → ④ 회수/발급 액션 판단 → ⑤ Slack DM 및 웹훅 회수 안내 → ⑥ Google Sheets 저장 \u003C/b\u003E및 대시보드 반영까지 전 단계가 자동화됐습니다.\u003C/p\u003E\r\n\u003Cp\u003E이제는 Figma 등 고가 라이선스는 7일 미사용 시 자동으로 View 권한으로 전환되고 재신청 시 즉시 재발급되며, 유휴 라이선스 비율은 1% 미만으로 관리되고 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"image_1378962619":{"id":"image-fdca2e9bee","height":"1038","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3bbca2f2-f046-4603-aab5-076cb19db323/image3.png?preferwebp=true&quality=85","lazyEnabled":true,"isLcpImage":false,"width":"1982",":type":"snowflake-site/components/image"},"title_ns_8":{"id":"title-v2-5c0e719671","type":"heading4","lines":["도입 효과: 일관된 품질, 더 적은 장애, 더 높은 가용성"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_ns_8":{"id":"text-7e4c806c08","text":"\u003Cp\u003E\u003Cb\u003E① 휴먼 에러(작업 실수) 90% 감소:\u003C/b\u003E 사전 검증 단계와 작업 직후 라이브 QA 절차를 AI 실행 흐름 내에 내장하여 휴먼 에러를 90% 줄였습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E② 작업 준비 시간 80% 단축:\u003C/b\u003E 과거의 성공 패턴과 축적된 도메인 지식을 신규 작업 계획 수립에 즉시 재배포함으로써 작업 준비 시간을 80% 단축했습니다.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E③ 서비스 중단 시간 50% 단축: \u003C/b\u003E사전 영향도 분석과 롤백 준비를 통해 서비스 중단 시간은 50% 감소했습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"title_ns_9":{"id":"title-v2-b1b2ba9af0","type":"heading4","lines":["절감한 시간을 다음 품질 개선에 재투자"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"blue-black-text-color snowflake-responsive-component-top-padding-extra-small"},"text_ns_9":{"id":"text-5af72fb64b","text":"\u003Cp\u003E앤서스랩은 AI가 만들어준 시간을 다시 조직의 다음 개선에 투자하고 있습니다. 반복 업무의 리드타임이 줄어든 만큼 엔지니어는 고난도 판단이 필요한 일에 집중하고, 그동안 히스토리와 스킬 부족, 러닝커브 문제로 미뤄뒀던 구조적 취약점(Weak Point)을 AI를 활용해 다시 설계하고 분석하는 작업을 진행하고 있습니다. 또한 장애 탐지, 분석, 대응까지 자동화 범위를 넓힌 AI 운영 에이전트(AIOps)를 지속적으로 개발하며, 이 모든 개발 과정 역시 기존 Harness 안에서 이루어지고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003EAI와 조직의 데이터, 그리고 반복 가능한 실행 시스템을 결합해 한 사람의 경험을 조직의 지속 가능한 경쟁력으로 바꾸는 일, 앤서스랩이 이루고 있습니다.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"quote_item_ns":{"id":"quote-item-f631fd6456","alignment":"left","layout":"simple","quoteSourceTitle":"앤서스랩(NSUSLAB) Head of TechOps Jeff Bang","showQuoteIcon":true,"quote":"최고의 엔지니어를 복제할 수는 없습니다. 하지만 최고의 엔지니어링 방식은 복제할 수 있습니다. 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