什么是 AI 治理政策生成器？

AI 治理政策生成器是一个强大的工具包，旨在帮助组织应对 AI 集成的复杂性。它提供结构化模板和工作流，以建立清晰的 AI 使用边界、评估第三方供应商，并在不同司法管辖区维持监管合规。作为 Openclaw Skills 生态系统中的一项技能，它赋能团队从实验性 AI 使用过渡到受控的、企业级的环境。

通过集中化数据处理、模型选择和事件响应的文档，AI 治理政策生成器降低了与影子 AI 和数据泄露相关的风险。对于希望利用 Openclaw Skills 有效实施 ISO 42001 或 NIST 框架的 CTO 和合规官来说，它是一个基础性资源。

下载入口:https://github.com/openclaw/skills/tree/main/skills/1kalin/afrexai-ai-governance

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

AI 治理政策生成器 应用场景

• 审查员工的内部 AI 可接受使用政策。
• 将组织的 AI 使用情况映射到欧盟 AI 法案或 NIST AI RMF。
• 在采购过程中评估供应商的 AI 条款和责任条款。
• 为董事会层面的利益相关者准备季度 AI 治理报告。
• 建立跨职能的 AI 治理委员会。

1. 通过对数据层级和许可的 AI 工具进行分类，定义可接受使用政策 (AUP)。
2. 使用涵盖安全性、成本和透明度的加权计分卡评估 AI 模型和供应商。
3. 进行数据流审计，记录 PII 和机密信息的处理方式。
4. 将现有系统与欧盟 AI 法案或 ISO 42001 等监管框架进行映射。
5. 建立治理委员会，确定角色和会议频率以进行持续监督。
6. 实施针对 AI 的事件响应计划，以处理幻觉或数据泄露。

AI 治理政策生成器 配置指南

要开始使用 AI 治理政策生成器，请将这些模板集成到您的组织文档系统中。您可以通过以下步骤初始化框架：

# 为治理文档创建一个新目录
mkdir ai-governance && cd ai-governance

# 通过 Openclaw Skills 初始化政策生成器模板
openclaw-cli install governance-builder

在运行初始审计脚本之前，请确保已定义内部数据分类层级。

AI 治理政策生成器 数据架构与分类体系

该技能将治理数据组织成结构化的分类法，以确保全组织的清晰度：

AI Governance Policy Builder

Build internal AI governance policies from scratch. Covers acceptable use, model selection, data handling, vendor contracts, compliance mapping, and board reporting.

When to Use

• Writing or reviewing internal AI acceptable use policies
• Establishing AI governance committees or review boards
• Mapping AI usage to regulatory frameworks (EU AI Act, NIST, ISO 42001)
• Evaluating vendor AI terms and liability clauses
• Preparing board-level AI governance reports

Governance Policy Framework

1. Acceptable Use Policy (AUP)

Every organization running AI needs a written AUP covering:

Permitted Uses

• List approved AI tools by department and function
• Define data classification tiers (public, internal, confidential, restricted)
• Map which data tiers can enter which AI systems
• Specify approved vendors vs. shadow AI (employees using personal ChatGPT accounts)

Prohibited Uses

• Customer PII in non-SOC2 models without anonymization
• Autonomous financial decisions above $[threshold] without human review
• HR screening/scoring without bias audit documentation
• Any use violating sector regulations (HIPAA, GDPR, SOX, PCI-DSS)

Shadow AI Detection

2. AI Model Selection & Procurement

Evaluation Scorecard (100 points)

Minimum score for production deployment: 70/100

Red Flags (automatic disqualification):

• Vendor trains on your data without opt-out
• No data processing agreement (DPA) available
• Indemnification excluded for AI outputs
• No incident response SLA

3. Data Handling & Classification

AI Data Flow Audit Template

For each AI integration, document:

1. Input data: What goes in? Classification tier? PII present?
2. Processing: Where? Which model? Hosted or API? Region?
3. Output data: What comes out? Stored where? Retention period?
4. Training: Does vendor use your data for training? Opt-out confirmed?
5. Logging: Are prompts/responses logged? Where? Who has access?
6. Deletion: Can you request data deletion? Verified how?

Data Minimization Checklist

• Only send minimum necessary data to AI systems
• Strip PII before processing where possible
• Use synthetic data for testing and development
• Implement input sanitization for prompt injection prevention
• Audit output for data leakage (model regurgitating training data)

4. Regulatory Compliance Mapping

EU AI Act (effective Aug 2025, enforcement Feb 2025)

NIST AI RMF (Risk Management Framework)

• Map: Identify AI systems in use
• Measure: Quantify risks per system
• Manage: Implement controls proportional to risk
• Govern: Establish oversight structure and accountability

ISO 42001 (AI Management System)

• Useful for organizations wanting certified AI governance
• Aligns with ISO 27001 (already have it? Easier path)
• Covers: AI policy, risk assessment, objectives, competence, documentation

5. AI Governance Committee Structure

Recommended Composition

• Chair: CTO or Chief AI Officer
• Legal: 1 representative (contracts, compliance)
• Security: CISO or delegate (data protection, incident response)
• Business: 1-2 department heads (use case prioritization)
• Ethics: External advisor or designated internal role
• Finance: CFO delegate (budget, ROI tracking)

Meeting Cadence

• Monthly: Review new AI use cases, vendor changes, incidents
• Quarterly: Policy updates, compliance audit, budget review
• Annually: Full governance framework review, board report

Decision Authority

6. Vendor Contract Checklist

Before signing any AI vendor contract, confirm:

• Data processing agreement (DPA) signed
• Your data is NOT used for model training (or explicit opt-out confirmed)
• Data residency requirements met (specify regions)
• Indemnification clause covers AI-generated output liability
• SLA includes uptime, latency, and support response time
• Exit clause: data export format, deletion timeline, transition support
• Security certifications current and verified (not expired)
• Incident notification timeline specified (72h or less)
• Subprocessor list provided with change notification rights
• Insurance coverage for AI-specific risks confirmed
• Price lock or cap on increases for contract duration
• Right to audit (or audit report access)

7. Board Reporting Template

Quarterly AI Governance Report

AI GOVERNANCE REPORT — Q[X] [YEAR]

1. AI PORTFOLIO SUMMARY
   - Active AI systems: [count]
   - New deployments this quarter: [count]
   - Retired/replaced: [count]
   - Total AI spend: $[amount] (vs budget: $[amount])

2. RISK DASHBOARD
   - High-risk systems: [count] — all compliant: [Y/N]
   - Open incidents: [count] — resolved this quarter: [count]
   - Shadow AI detections: [count] — remediated: [count]
   - Compliance gaps: [list]

3. VALUE DELIVERED
   - Hours saved: [estimate]
   - Revenue attributed to AI: $[amount]
   - Cost reduction: $[amount]
   - Customer satisfaction impact: [metric]

4. KEY DECISIONS NEEDED
   - [Decision 1: context + recommendation]
   - [Decision 2: context + recommendation]

5. NEXT QUARTER PRIORITIES
   - [Priority 1]
   - [Priority 2]

8. Incident Response for AI Systems

AI-Specific Incident Categories

Post-Incident Review Template

1. What happened (factual timeline)
2. Impact (who/what affected, cost, duration)
3. Root cause (not blame — systems thinking)
4. Fixes applied (immediate + permanent)
5. Policy/process changes needed
6. Board notification required? (Y/N + rationale)

Cost of NOT Having AI Governance

90-Day Implementation Roadmap

Month 1: Foundation

• Draft acceptable use policy
• Inventory all AI systems in use (including shadow AI)
• Classify data flowing through each system
• Identify governance committee members

Month 2: Controls

• Finalize and distribute AUP
• Implement vendor evaluation scorecard for new purchases
• Set up AI incident response procedures
• Begin regulatory compliance mapping

Month 3: Operationalize

• First governance committee meeting
• Deliver first board report
• Establish monitoring for shadow AI
• Schedule quarterly policy review cycle

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