기업 AI 도입을 위한 지식
Industry deployment guides, architecture deep-dives, comparison frameworks, and Korean public-sector policy analysis.
공공기관 생성형 AI 도입의 세 가지 길
AI DLP·차단, sLLM 자체구축, 게이트웨이 방식 — N²SF 시대 공공기관의 선택지를 비용·성능·보안·정합성 관점에서 객관적으로 비교합니다.
Read →N²SF란 무엇인가 — 공공기관 보안의 새 패러다임
국가 망 보안체계(N²SF)는 망분리에서 다중계층보안(MLS)으로의 전환입니다. C/S/O 등급 체계와 「위치-주체-객체」 모델링을 정리합니다.
Read →N²SF 모델 2 완벽 해설 — 공공기관에서 ChatGPT를 쓸 수 있을까
국가정보원·NSR이 2025년 9월 발간한 「업무환경에서 생성형 AI 활용 모델 해설서」를 정보화담당관 관점에서 정리합니다.
Read →sLLM 자체구축, 정말 답일까 — 비용·성능·보안의 진짜 트레이드오프
공공기관 sLLM 자체구축의 진짜 비용(5년 28~38억), 상용 LLM 대비 성능 격차 추세, 잘못된 선택 패턴을 분석합니다.
Read →공공기관 외부 LLM 활용 도입 가이드 — 분기 로드맵
정보화담당관 관점에서 외부 LLM 도입을 분기별 5단계로 정리합니다. 시나리오 A 4개월 vs 시나리오 B 6~8개월 로드맵 비교.
Read →공공기관 생성형 AI 도입 시 가장 많이 막히는 5가지
등급 분류 부담, 시나리오와 O 등급 충돌, 솔루션 후보 부족, 예산 시점 불일치, 활용 실패 — 다섯 막힘 지점.
Read →2026 경영평가 'AI 활용 등 혁신' 가점 — 공공기관 핵심 경쟁력
2026 경영평가편람에 신설된 'AI 활용 등 혁신' 가점 1.5점이 공공기관 경쟁력에 어떤 영향을 주는지 정리합니다.
Read →How to deploy AI in a telecom NOC without exposing network data
Step-by-step deployment guide. Validated at SK Telecom and Deutsche Telekom T Challenge 2026 Top 12.
Read →How to deploy AI in a hospital without exposing PHI
HIPAA-aligned playbook for hospital CIOs and clinical informatics. Deployed at EUMC.
Read →AI on network operations data — NOC, RCA, and workflow execution
How to bring AI to NOC logs, alarm sequences, incident tickets — without exposing operational identifiers.
Read →On-premise LLM execution path
Path B architecture deep-dive: quantized model, internal GPU, vLLM, full air-gap.
Read →Sovereign AI for European enterprises — practical architecture
GDPR + EU AI Act + national data residency. Two execution paths under one governance framework.
Read →Differential privacy for enterprise LLM
Beyond field masking — DP noise, k-anonymity, and semantic tokenization for operational data.
Read →Why enterprise AI pilots stall — and how they get to production
Diagnostic for executives running an AI program. The pattern that ships to production.
Read →PII guardrails vs operational data protection
Field-level filters vs structure-preserving + DP-based encapsulation — what regulated workflows actually need.
Read →LLM Capsule vs Masking Tools
Field-level masking vs structure-preserving capsule — what each does, what each leaves on the table.
Read →LLM Capsule vs Prompt Security Gateways
Prompt-level threat defense vs data-layer enablement — where each layer fits.
Read →LLM Capsule vs Synthetic Data Platforms
Synthetic generation for training vs live capsule for production — different problems, different solutions.
Read →On-Premise vs Cloud AI Data Protection
Deployment-mode trade-offs for regulated enterprise AI workflows.
Read →PII Protection vs Enterprise Confidentiality Control
Standard PII detection vs context-aware enterprise confidentiality markers.
Read →Structure-Preserving Processing vs Flat Masking
Why tables, sequences, and cross-references matter for AI workflow quality.
Read →Secure Enterprise AI Data Workflows
What 'secure' means in enterprise AI workflows beyond field-level masking.
Read →Enterprise AI Document Processing
How regulated enterprises process documents through AI while keeping sensitive elements local.
Read →What Is an AI Data Capsule
The capsule pattern: structure-preserving encapsulation + restoration inside the trust boundary.
Read →How to Use AI on Sensitive Enterprise Data
Practical patterns for AI workflows over regulated operational data.
Read →Why Redaction Breaks Enterprise AI Workflows
Why permanent removal destroys the context AI needs to produce useful output.
Read →Structure-Preserving Document Processing
Tables, layouts, and cross-references survive the process intact.
Read →AI Data Pipeline Protection
End-to-end protection across the enterprise AI data pipeline.
Read →Restorable Workflow
Workflows where the original values can be restored locally after AI processing.
Read →Enterprise Context Control
Organization-defined markers + policy versioning + time-shifting.
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