# Send AI Governance Policy Builder to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
## Suggested prompts
### New install

```text
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
```
### Upgrade existing

```text
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
```
## Machine-readable fields
```json
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    "name": "AI Governance Policy Builder",
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      "expiresAt": "2026-05-01T08:04:24.924Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-ai-governance",
      "contentType": "application/zip",
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        "redirectLocation": null,
        "bodySnippet": null,
        "slug": "afrexai-ai-governance"
      },
      "scope": "item",
      "summary": "Item download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/afrexai-ai-governance"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/afrexai-ai-governance",
    "downloadUrl": "https://openagent3.xyz/downloads/afrexai-ai-governance",
    "agentUrl": "https://openagent3.xyz/skills/afrexai-ai-governance/agent",
    "manifestUrl": "https://openagent3.xyz/skills/afrexai-ai-governance/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/afrexai-ai-governance/agent.md"
  }
}
```
## Documentation

### 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

### 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

SignalRisk LevelActionAPI calls to unknown AI endpointsHIGHBlock + investigateBrowser extensions with AI featuresMEDIUMAudit + approve/denyPersonal accounts on company devicesMEDIUMPolicy reminder + monitorExported data to AI training setsCRITICALImmediate review

### 2. AI Model Selection & Procurement

Evaluation Scorecard (100 points)

CriteriaWeightWhat to CheckData residency & sovereignty20Where is data processed? Stored? Can you choose region?Security certifications20SOC2 Type II, ISO 27001, HIPAA BAA, FedRAMPModel transparency15Training data provenance, bias testing, version controlContract terms15Data usage rights, indemnification, SLA, exit clausesPerformance & cost15Latency, accuracy benchmarks, token pricing, rate limitsIntegration & support15API stability, documentation quality, support SLA

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:

Input data: What goes in? Classification tier? PII present?
Processing: Where? Which model? Hosted or API? Region?
Output data: What comes out? Stored where? Retention period?
Training: Does vendor use your data for training? Opt-out confirmed?
Logging: Are prompts/responses logged? Where? Who has access?
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)

Risk CategoryExamplesRequirementsUnacceptableSocial scoring, real-time biometric ID (most cases)BannedHigh-riskHR screening, credit scoring, medical devicesConformity assessment, human oversight, transparencyLimitedChatbots, deepfakesTransparency obligations (disclose AI use)MinimalSpam filters, game AINo requirements

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

DecisionAuthority LevelNew AI tool (< $5K/year)Department head + security reviewNew AI tool (> $5K/year)Governance committee approvalCustomer-facing AICommittee + legal + CEO sign-offAI incident responseSecurity lead (immediate) → Committee (48h review)

### 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

CategoryExampleResponse TimeData breach via AIModel leaks PII in outputImmediate — invoke security IR planHallucination causing harmWrong medical/legal/financial advice acted on4h — document, notify affected partiesBias detectedDiscriminatory output in hiring/lending24h — suspend system, audit, remediatePrompt injectionAttacker manipulates AI behaviorImmediate — block vector, patchCost overrunRunaway API calls4h — rate limit, investigate, capVendor incidentProvider breach or outagePer vendor SLA — activate backup

Post-Incident Review Template

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

### Cost of NOT Having AI Governance

Company SizeAnnual Risk Without Governance15-50 employees$50K-$200K (shadow AI waste, compliance fines)50-200 employees$200K-$800K (data incidents, vendor lock-in, redundant tools)200-1000 employees$800K-$3M (regulatory penalties, IP exposure, audit failures)1000+ employees$3M-$15M+ (class action, regulatory enforcement, reputational damage)

### 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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## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: 1kalin
- Version: 1.1.0
## Source health
- Status: healthy
- Item download looks usable.
- Yavira can redirect you to the upstream package for this item.
- Health scope: item
- Reason: direct_download_ok
- Checked at: 2026-04-24T08:04:24.924Z
- Expires at: 2026-05-01T08:04:24.924Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/afrexai-ai-governance)
- [Send to Agent page](https://openagent3.xyz/skills/afrexai-ai-governance/agent)
- [JSON manifest](https://openagent3.xyz/skills/afrexai-ai-governance/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/afrexai-ai-governance/agent.md)
- [Download page](https://openagent3.xyz/downloads/afrexai-ai-governance)