투명성 로그
투명성 로그
starnum.com.tw는 시스템 이벤트, 품질 문제 및 개선 조치를 공개적이고 투명하게 기록할 것을 약속합니다. 이 페이지는 OpenAI Postmortem 페이지, Google Transparency Report, Anthropic Usage Policy Violation Summaries의 투명성 기준에 맞게 벤치마킹되었습니다.
현재 시스템 상태
| 서비스 | 상태 | 마지막 확인 |
|---|
| 콘텐츠 제작 시스템 | 정상 운영 | 2026-04-12 |
| 명반 분석 서비스 | 정상 운영 | 2026-04-12 |
| 지식 베이스 (KB) 인덱스 | 정상 운영 | 2026-04-12 |
| 다중 공급자 거버넌스 감사 | 정상 운영 (R15 4/4 CLEAN) | 2026-04-12 |
| Supabase 데이터베이스 | 정상 운영 | 2026-04-12 |
| Cloudflare CDN | 정상 운영 | 2026-04-12 |
사고 기록 (2026)
2026-04-10
MINOR
해결됨
INC-2026-001: 일반 언어 출력 형식 위반
심각도: 낮음 (품질 문제, 데이터 손실이나 보안 문제 아님)
영향: AI 에이전트가 명반 분석에서 비표준 형식을 사용하여 일부 출력이 형식 사양에 위반됨.
완화 조치: validate-output-format.js 자동화 형식 검증 스크립트 생성; 모든 기사의 CI/CD에 통합.
해결 시간: 2026-04-10 (당일 해결)
관련 페이지
System Card V2.0: Technical Transparency Layer
This layer publishes the technical governance evidence that can be safely disclosed: architecture, data sources, AI-use boundaries, quality gates, release integrity, and provider alignment. Source code, secrets, exploitable attack details, and private data remain out of scope.
Public architecture
Cloudflare Pages/Workers, R2/D1/KV/Pagefind, and local generation scripts form the public-site and governance publication chain. Public pages disclose behavior, state, and traceable sources, not secrets or internal permissions.
AI-use disclosure
AI-assisted workflows are used for knowledge-base retrieval, cross-checking, and error detection. Governance documents are benchmarked against OpenAI, Anthropic, and Google Gemini public frameworks. Production model usage is disclosed only when code/config evidence exists.
Quality and safety gates
Governance page audit 180/180 passing, JSON-LD errors 0, discovery-surface errors 0. Status pages report critical / 5 critical, 0 warnings as-is.
Data traceability
Knowledge base 32,724 chunks, TM 789,031 entries, AI answer-ready 7,976/7,976. Public metrics trace to data/state-machine/*, data/*audit*.json, and transparency reports.
| Governance area | OpenAI | Anthropic | Google Gemini | Starnum implementation evidence |
|---|
| Model/system-card disclosure | OpenAI models + safety docs | Claude model docs + system/model cards | Gemini model docs + safety settings | system-card, model-card, methodology, benchmark, transparency-log |
| Safety evaluation and use boundaries | Safety best practices / deployment checklist | Responsible Scaling / safety policy | Gemini safety controls / policy | AI safety, acceptable-use, ethics, risk-boundary copy, crawler policy audit |
| Data governance | Data controls / privacy controls | privacy and data handling docs | Gemini API data governance references | privacy, ai-data-governance, KB/TM source tracking, SHA-256 hashes |
| Monitoring and release | production checklist / eval discipline | system-card transparency discipline | model/version documentation discipline | deploy.js, status.html, SLA report, trust-pages-machine-audit, sitemap/hreflang audits |
- Sources: data/state-machine/model-card.json, public-bench.json, trust-pages.json, security-headers.json.
- Sources: data/trust-pages-machine-audit.json, data/discovery-surface-audit.json, data/ai-answer-readiness-audit.json.
- Sources: data/kb-machine-audit.json, data/tm/quality-audit-report.json, data/sla-report.json.
- Official benchmark docs checked: 2026-07-30; links are listed in the OpenAI / Anthropic / Google Gemini alignment table.
The V2.0 goal is not more claims; it separates implemented controls from planned controls. Production usage, benchmark alignment, status exceptions, GPG signing, and SLA breaches are disclosed from source data.