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-risk-management
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title: Risk Management
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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: [Project Risk Management, Software Risk Management]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.88
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verification_status: applied
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tags: [project-management, sdlc, governance, security]
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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: none
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framework: PMI/ISO 31000
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---
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# Risk Management
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## 매 한 줄
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> **"매 uncertain event 를 매 identify → assess → respond → monitor 의 cycle 로 관리"**. ISO 31000 (2018) + PMBOK 7e (2021) + NIST RMF (SP 800-37r2) 의 공통 골격. 매 software 맥락에서는 매 schedule risk, technical debt, supply-chain (CVE), AI hallucination, model drift 까지 포괄. 매 2026 추가 트렌드: LLM agent autonomy risk, prompt injection, SBOM 의무화 (US EO 14028).
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## 매 핵심
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### 매 4-step cycle
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1. **Identify**: brainstorming, checklist, threat modeling (STRIDE, LINDDUN), pre-mortem.
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2. **Assess**: probability × impact = risk score. Qualitative (matrix) 또는 quantitative (Monte Carlo, EMV).
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3. **Respond**: avoid / transfer / mitigate / accept (PMBOK).
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4. **Monitor**: risk register, KRI dashboard, retro.
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### 매 software-specific 영역
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- **Schedule/budget**: estimation bias, scope creep, dependency.
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- **Technical debt**: SonarQube, CodeScene 의 quantification.
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- **Security**: CVE, supply-chain (Log4Shell, xz-utils 2024), SBOM (SPDX/CycloneDX).
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- **AI**: hallucination, prompt injection, training-data leak, model drift, agent autonomy.
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- **Operational**: SLO breach, incident, on-call burnout.
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### 매 응용
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1. Pre-mortem (Klein): "프로젝트 실패했다고 가정하고 원인 작성".
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2. Risk-adjusted backlog: high-risk story 를 sprint 1 에 배치.
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3. Chaos engineering: 매 failure 를 사전 주입해 hypothesis 검증.
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4. Agent guardrail: tool-call allowlist, human-in-the-loop checkpoint.
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## 💻 패턴
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### Risk register (YAML)
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```yaml
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- id: R-001
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title: PostgreSQL 17 upgrade fails on JSONB index
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category: technical
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probability: 0.3 # 0..1
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impact: 4 # 1..5
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score: 1.2 # P × I
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owner: data-platform
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response: mitigate
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mitigation:
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- run upgrade on staging mirror
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- keep pg17→pg16 logical replication for 2 weeks
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trigger: production migration window
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status: open
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review_date: 2026-06-01
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```
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### Probability × Impact matrix
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```typescript
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type Level = 1 | 2 | 3 | 4 | 5;
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type Risk = { p: Level; i: Level };
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const score = (r: Risk) => r.p * r.i;
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const tier = (s: number) =>
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s >= 16 ? 'critical'
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: s >= 9 ? 'high'
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: s >= 4 ? 'medium'
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: 'low';
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console.log(tier(score({ p: 4, i: 5 }))); // critical
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```
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### Monte Carlo schedule risk (Python)
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```python
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import numpy as np
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# task durations: triangular(min, mode, max) days
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tasks = [(2, 3, 7), (5, 8, 14), (1, 2, 4), (3, 5, 10)]
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N = 100_000
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samples = np.array([
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[np.random.triangular(*t) for t in tasks]
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for _ in range(N)
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])
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totals = samples.sum(axis=1)
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print(f"P50={np.percentile(totals,50):.1f}d, P90={np.percentile(totals,90):.1f}d")
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```
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### Threat modeling — STRIDE checklist
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```text
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S Spoofing — auth, mTLS, signed JWT
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T Tampering — integrity hash, append-only log
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R Repudiation — audit log + WORM storage
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I Info disclosure— TLS, encryption-at-rest, PII redaction
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D Denial — rate limit, autoscale, circuit breaker
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E Elev privilege — least-priv IAM, RBAC, no sudo prod
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```
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### LLM agent risk gate (Claude Opus 4.7)
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```typescript
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import Anthropic from '@anthropic-ai/sdk';
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const TOOL_ALLOWLIST = new Set(['read_file', 'list_dir', 'web_fetch']);
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const HIGH_RISK = new Set(['delete_file', 'execute_shell', 'send_email']);
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async function gate(toolName: string, args: unknown) {
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if (HIGH_RISK.has(toolName)) {
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const ok = await humanApproval({ tool: toolName, args });
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if (!ok) throw new Error(`tool ${toolName} rejected by human gate`);
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}
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if (!TOOL_ALLOWLIST.has(toolName) && !HIGH_RISK.has(toolName)) {
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throw new Error(`tool ${toolName} not in allowlist`);
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}
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}
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```
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### SBOM generation (Syft)
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```bash
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# 매 CI step — SPDX SBOM 생성 + CVE scan
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syft packages dir:. -o spdx-json > sbom.spdx.json
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grype sbom:sbom.spdx.json --fail-on high
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```
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### Chaos experiment (Litmus / k8s)
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```yaml
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apiVersion: litmuschaos.io/v1alpha1
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kind: ChaosEngine
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metadata: { name: pod-kill }
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spec:
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appinfo: { applabel: 'app=checkout' }
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chaosServiceAccount: litmus
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experiments:
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- name: pod-delete
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spec:
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components:
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env:
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- { name: TOTAL_CHAOS_DURATION, value: '60' }
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- { name: CHAOS_INTERVAL, value: '10' }
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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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| Startup, light process | Risk register (markdown/YAML) + weekly review |
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| Regulated (SOC2/ISO27001) | NIST RMF + control mapping |
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| Schedule heavy | Monte Carlo + critical path |
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| Security-sensitive | Threat model (STRIDE) per feature |
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| LLM agent system | Tool allowlist + human gate + audit log |
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| Live ops | KRI dashboard + chaos engineering |
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**기본값**: 매 risk register + weekly triage + threat model per epic.
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## 🔗 Graph
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- 부모: [[Project Management]] · [[SDLC]] · [[Governance]]
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- 변형: [[Threat Modeling]] · [[Chaos Engineering]] · [[FMEA]]
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- 응용: [[SBOM]]
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- Adjacent: [[SARA (Software Architecture Review and Assessment)]] · [[Resource-Management]]
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## 🤖 LLM 활용
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**언제**: 매 risk register 초안, 매 STRIDE checklist 생성, 매 incident retro 의 root cause 분류.
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**언제 X**: 매 quantitative 신뢰 — LLM 의 probability 추정은 calibrated 아님. 실측 또는 expert estimate 우선.
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## ❌ 안티패턴
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- **Risk register as graveyard**: 매 등록 후 매 review 없음.
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- **Probability theater**: 매 0.37 같은 false-precision — qualitative 5-tier 충분.
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- **Mitigation without trigger**: 매 언제 발동인지 불명.
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- **Hero culture**: 매 risk 무시하고 매 incident 시 영웅적 fix — burnout.
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- **Agent without allowlist**: 매 prompt injection → arbitrary tool call.
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- **Single-vendor lock**: 매 supply-chain risk 미평가.
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## 🧪 검증 / 중복
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- Verified: ISO 31000:2018, PMBOK 7e (2021), NIST SP 800-37r2 RMF, OWASP Threat Modeling.
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- 신뢰도 A.
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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 — full RM cycle + STRIDE + LLM agent gate |
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