자주 묻는 질문 FAQ
FAQ v2.0Version 2.0 · · Governance 2.0 public evidence surface
Governance 2.0 Overview
This page is part of the starnum public Governance 2.0 surface and uses the same evidence layer as the system card, data governance, transparency report, use policy, and security policy.
Governance Summary
This page answers common user questions and points each governance-sensitive topic toward the relevant public policy surface.
Scope
Privacy, chart IDs, AI assistance, non-advice boundaries, retention, accessibility, security, and evidence artifacts.
Implementation Status
Version 2.0 keeps FAQ content aligned with the same canonical footer and machine-audited governance sections.
현재 데이터 수명주기(코드 구현 기준)
이 절은 현재 검증 가능한 규칙입니다. 이전 문구와 충돌하면 이 절과 실제 구현을 우선합니다.
- 저장 항목에는 생년월일, 성별, 선택 입력한 출생 시각·장소 메타데이터, 명반 구조, 해석 결과, 언어, 요금제 상태, 접근／제거 토큰이 포함됩니다. 출생 정보는 개인정보이며 익명화되었다고 표현하지 않습니다.
- 주 데이터는 Cloudflare R2, 조회 인덱스와 상태는 D1에 저장합니다. KV는 기한이 있는 복구 백업입니다. Supabase는 기본 비활성화된 이전 장애용 예비 경로이며 일상적인 주 경로가 아닙니다.
- 프런트 조회 기간은 무료 6개월, 일반 1년, 고급·특별 요금제는 무기한입니다. 무료／일반 만료 후 R2 명반 내용은 삭제하고 최소 소비자 인덱스와 주문／감사 기록만 보존합니다.
- 사용자가 제거하면 소프트 삭제 상태를 기록하여 공개 조회·목록·덮어쓰기를 즉시 차단합니다. 보호된 기록과 감사 메타데이터는 계속 보관됩니다. 이는 물리적 삭제가 아니라 프런트 접근 제거입니다.
- AI 생성 또는 번역이 실제로 활성화된 경우에만 작업에 필요한 명반 구조나 문장을 설정된 공급자에게 전송할 수 있습니다. 벤치마크 표의 공급자명은 운영 사용을 뜻하지 않습니다.
src/chart-storage.js · src/api-handler.js
자미두수, 생명수비학, 감정 정확도, 서비스 안내에 대한 완전한 답변.
기본
자미두수 명반 분석에는 어떤 정보가 필요한가요?
생명수(Life Path Number)는 어떻게 계산하나요?
감정은 어떤 학파를 따르나요?
명리 분석으로 미래를 예언할 수 있나요?
정확도와 방법론
백화문 감정의 정확도는 어떻게 보장되나요?
글은 어떻게 만들어지며 AI 는 어떤 역할을 하나요?
일부 유년 글에 연도가 표기되어 있는 이유는?
프라이버시
제 개인정보는 어떻게 처리되나요?
서비스
이 감정이 심리 상담이나 의료 진단을 대체할 수 있나요?
피드백이나 오류 제보는 어디로 보내나요?
Current Machine Audit Snapshot
This block uses only traceable local audit data. No unsupported metrics or model claims are added.
- data/state-machine/i18n-parity.json: 8,036 parent URLs, 7,976 articles.
- data/kb-machine-audit.json: 3,238 source files, 0 missing coverage, 0 orphan chunks.
- data/discovery-surface-audit.json: 0 errors, 0 warnings.
- data/sla-report.json: critical / 5 critical, 0 warnings.
Content Maintenance And Update Decision
This block makes governance-page content machine-checkable: every page must disclose its source artifacts, related pages, and the gate that reports update needs.
Update Decision
This is not static copy. When source artifacts, related policies, public metrics, or generators change, AI Ops reports evidence and an AI agent decides whether the page needs edits.
Human Boundary
Systems detect, report, and preserve machine-readable evidence. Codex/Claude agents perform final judgment and repair.
Verification Command
node scripts/verify-trust-pages.js --check
data/state-machine/trust-pages.jsondata/public-claim-registry.jsondata/sla-report.json- Related governance pages: Privacy Policy · Acceptable Use Policy · Security Policy · methodology
- Update flow:
npm run update:trust-pages→npm run test:trust
Verifiable Evidence Layer
This block is not a narrative claim. Each core assertion has a claim id, source JSON, hash, and a repeatable verification command. Public pages disclose governance evidence without exposing source code, secrets, private data, or exploitable attack details.
| Claim ID | Verifiable value | Status | Owner | Source and verification |
|---|---|---|---|---|
| claim.public-url-manifest.indexable-count Public URL and canonical inventory |
38,965 indexable URLs | verified | sitewide | node scripts/generate-public-evidence-manifest.js --dry |
| claim.trust-pages.audit-pass-rate Trust page machine audit |
180/180 pass | verified | sitewide | node scripts/verify-trust-pages.js --check |
| claim.discovery-surface.zero-errors AI discovery surface audit |
{"errors":0,"warnings":0} | verified | sitewide | node scripts/verify-discovery-surface.js |
| claim.structured-data.jsonld-errors JSON-LD / structured data audit |
{"structured_data_invalid_files":0,"breadcrumb_count":28274,"faq_count":27506,"dataset_count":30,"article_count":27406} | verified | sitewide | node scripts/site-machine-audit.js |
| claim.status.sla-state Status page SLA source |
critical / 5 critical, 0 warnings | verified | sitewide | node scripts/generate-status-page.js |
| claim.provider-alignment.openai-anthropic-gemini OpenAI / Anthropic / Google Gemini benchmark alignment |
benchmark alignment only unless code/config evidence exists | verified | sitewide | node scripts/verify-public-evidence.js --check |
| claim.transparency-report.sha256 Transparency report SHA-256 anchor |
{"report":"transparency/report-2026-Q3.json","sha256":"47b09e2ca4e8b8fe9dffdfaccef3b11212de9ee3a8a14badca8044e2481203c5"} | verified | sitewide | node scripts/update-transparency-current-data.js |
| claim.release-integrity.gpg-signing GPG signing status |
GPG signing configured locally; GitHub verification pending | github_verification_pending | sitewide | gpg --list-secret-keys --keyid-format=long && git log -1 --show-signature |
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.
Release Integrity And GPG
GPG signing configured locally. signingkey=0934DFA0EDA6363A. GitHub verification pending until the public key upload and Verified badge are confirmed.
OpenAI / Anthropic / Google Gemini Alignment
The governance surface is benchmarked against the three public frameworks: model docs, system/model cards, safety evaluation, data governance, and use policies. This is benchmark alignment, not a claim that every provider is active in production inference. Official docs checked: 2026-07-30
| Provider | Governance focus | Starnum disclosure | Official source |
|---|---|---|---|
| OpenAI | Model documentation, latest model notes, safety best practices, and data controls. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://platform.openai.com/docs/models |
| Anthropic | Claude model documentation, system/model cards, Responsible Scaling, and safety policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://docs.anthropic.com/en/docs/about-claude/models |
| Google Gemini | Gemini API model documentation, safety settings, data governance, and platform policy. | No verifiable production model setting was found in the production code scan; providers are listed as governance benchmarks. | https://ai.google.dev/gemini-api/docs/models |