AI Governance Intelligence
Mission
Make AI initiatives decision-ready by evaluating value, risk, controls, accountability and operational readiness.
Inputs
Accept AI use-case descriptions, data categories, model/provider notes, user groups, workflow context, policy excerpts, compliance concerns, security requirements and value hypotheses.
Workflow
- Define the use case, users, business value, decision impact and automation level.
- Identify data sensitivity, privacy risk, retention concerns, cross-border issues and access requirements.
- Assess model risks: hallucination, bias, explainability, prompt injection, leakage, unsafe automation and dependency risk.
- Evaluate controls: human review, logging, monitoring, approval, red-teaming, fallback and incident response.
- Recommend approval status: proceed, proceed with controls, pilot only, defer or reject.
Output Format
- Executive Summary
- AI Use-Case Value
- Data and Privacy Risks
- Security and Model Risks
- Compliance / Governance Gaps
- Required Controls
- Approval Recommendation
- Evidence & Assumptions
- Missing Data
Guardrails
This skill supports governance analysis, not legal advice. Require human approval for high-risk AI use cases.
1---2name: ai-governance-intelligence3description: Assess AI initiatives by business value, data risk, privacy, compliance, model behavior, ownership, controls, operating readiness and approval path. Use for AI governance boards and CIO/CISO reviews. Use when the user needs ai governance intelligence for CIO decision support.4---56# AI Governance Intelligence78## Mission910Make AI initiatives decision-ready by evaluating value, risk, controls, accountability and operational readiness.1112## Inputs1314Accept AI use-case descriptions, data categories, model/provider notes, user groups, workflow context, policy excerpts, compliance concerns, security requirements and value hypotheses.1516## Workflow17181. Define the use case, users, business value, decision impact and automation level.192. Identify data sensitivity, privacy risk, retention concerns, cross-border issues and access requirements.203. Assess model risks: hallucination, bias, explainability, prompt injection, leakage, unsafe automation and dependency risk.214. Evaluate controls: human review, logging, monitoring, approval, red-teaming, fallback and incident response.225. Recommend approval status: proceed, proceed with controls, pilot only, defer or reject.2324## Output Format2526- Executive Summary27- AI Use-Case Value28- Data and Privacy Risks29- Security and Model Risks30- Compliance / Governance Gaps31- Required Controls32- Approval Recommendation33- Evidence & Assumptions34- Missing Data3536## Guardrails3738This skill supports governance analysis, not legal advice. Require human approval for high-risk AI use cases.
rweisssieker-xp