refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-policy-surveillance
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title: Policy Surveillance
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Legal Mapping, Policy Tracking, Regulatory Monitoring]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [public-policy, governance, compliance, legal-tech]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: legal-mapping
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---
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# Policy Surveillance
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## 매 한 줄
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> **"매 you can't evaluate what you can't measure — start by mapping the law."**. 매 Burris (Temple) 가 정립한 **Policy Surveillance** = 매 systematic, scientific tracking of laws/policies as data 의 개념. 매 2026 AI governance (EU AI Act enforcement, Korea AI Basic Act, US state AI laws) 시대에 매 polyjurisdictional compliance 의 핵심 도구.
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## 매 핵심
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### 매 정의 vs adjacent
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- **Policy Surveillance**: 매 ongoing, systematic, scientific 매 monitoring of policies as 매 quantifiable data.
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- **vs Legal Research**: 매 case-driven, episodic.
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- **vs Compliance Audit**: 매 organization-internal, point-in-time.
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- **vs Regulatory Tracking**: 매 news-driven, qualitative.
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### 매 5단계 method (Burris)
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1. 매 frame the question — what behavior does the law target?
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2. 매 define jurisdictional + temporal scope.
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3. 매 collect primary sources (statutes, regs).
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4. 매 code into structured variables (binary, ordinal, categorical).
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5. 매 publish + maintain — 매 LawAtlas-style open data.
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### 매 응용
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1. AI Act compliance: 매 27 EU 회원국 + 미국 50주의 AI law variation 추적.
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2. Public health: 매 LawAtlas COVID closure tracking, opioid policies.
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3. Privacy: 매 GDPR vs CPRA vs PIPL 의 cross-walk.
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## 💻 패턴
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### Pattern 1: Coding scheme YAML
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```yaml
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# 매 ai_law_codes.yaml
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variables:
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- id: requires_impact_assessment
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type: binary
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question: "매 Does law require AI impact assessment?"
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- id: penalty_max
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type: numeric
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unit: USD
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- id: covered_systems
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type: categorical
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values: [foundation_models, biometric, hiring, healthcare, all_high_risk]
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jurisdictions: [EU, US-CA, US-CO, KR, UK, CN]
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effective_dates: required
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```
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### Pattern 2: Cross-walk matrix
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```python
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import pandas as pd
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def crosswalk(jurisdictions, variables, codes_df):
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matrix = codes_df.pivot(index="jurisdiction",
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columns="variable",
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values="value")
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matrix.to_csv("crosswalk.csv")
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return matrix
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```
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### Pattern 3: Diff over time
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```python
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def policy_diff(snapshot_old, snapshot_new):
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changes = []
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for jur in snapshot_new.index:
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for var in snapshot_new.columns:
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if snapshot_old.at[jur, var] != snapshot_new.at[jur, var]:
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changes.append({
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"jurisdiction": jur, "variable": var,
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"from": snapshot_old.at[jur, var],
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"to": snapshot_new.at[jur, var],
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})
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return changes
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```
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### Pattern 4: LLM-assisted coding (with human verification)
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```python
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import anthropic
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client = anthropic.Anthropic()
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def code_statute(statute_text, scheme):
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2048,
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system=f"Code the statute against this scheme: {scheme}. Return JSON.",
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messages=[{"role": "user", "content": statute_text}],
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)
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# 매 ALWAYS human-verify legal coding
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return {"draft": resp.content[0].text, "needs_review": True}
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```
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### Pattern 5: Effective-date timeline
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```python
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def timeline_view(codes_df):
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return codes_df.sort_values("effective_date")[
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["jurisdiction", "variable", "value", "effective_date"]
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]
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```
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### Pattern 6: Citation chain (provenance)
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```python
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def store_with_provenance(code, value, statute_section, source_url, retrieved_at):
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return {
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"code": code, "value": value,
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"citation": {"section": statute_section, "url": source_url, "retrieved": retrieved_at},
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}
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| 매 single-org compliance | Standard compliance audit |
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| 매 multi-jurisdiction policy comparison | Policy Surveillance |
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| 매 academic causal inference (does law X cause outcome Y?) | Policy Surveillance + econometrics |
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| 매 real-time regulatory news | News tracker (NOT surveillance) |
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| 매 AI Act multi-state US tracking | Policy Surveillance + LLM-draft + lawyer review |
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**기본값**: 매 LawAtlas-style codebook + git versioning + LLM-draft + human verification.
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## 🔗 Graph
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- 응용: [[AI 거버넌스 정책(AI Usage Policy)|AI Governance]] · [[GDPR Compliance]]
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- Adjacent: [[EU AI Act]]
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## 🤖 LLM 활용
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**언제**: 매 first-pass coding of large statute corpus, 매 cross-walk drafting, 매 diff summarization.
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**언제 X**: 매 final legal coding without human lawyer — 매 hallucination risk too high for compliance use.
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## ❌ 안티패턴
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- **No version control**: 매 statutes 가 amend 되는데 snapshot 없으면 매 useless for trend analysis.
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- **Coding without scheme**: 매 ad-hoc tags — 매 inter-coder reliability ~0.
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- **LLM-only coding**: 매 hallucinated citations — 매 catastrophic for legal use.
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- **Single jurisdiction silo**: 매 policy surveillance 의 가치 = comparison.
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## 🧪 검증 / 중복
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- Verified (Burris et al., Temple Center for Public Health Law Research; LawAtlas.org).
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- 신뢰도 A (academic + practitioner standard).
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Burris method, AI Act 응용, LLM augmentation |
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