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rade-nugrohorade-nugroho/Agentic-Enteprises-Skill/ai-risk-governance/SKILL.md

ai-risk-governance

Skill9 starsChanged 6 months agoMITView on GitHub

Guides AI risk management and governance—use-case risk assessment, model/system documentation, policies and guardrails, human oversight, third-party model/vendor review, and mapping to frameworks (NIST AI RMF, ISO 42001, EU AI Act concepts, internal AI policy). Use when classifying AI use cases, drafting AI acceptable-use policies, building risk registers, preparing model cards or DPIAs for AI, reviewing vendor LLMs, or aligning product teams with compliance—not for implementing RAG/agents (ai-engineer), running jailbreak tests (ai-redteam), or SOC 2/ISO evidence automation and technical control mapping (compliance-engineer), or general SOC 2 IT controls without AI scope (cybersecurity). For AI solution architecture in commercial or enterprise deployments, use applied-ai-architect-commercial-enterprise. For agent skills catalog standards and security review before publish, use ai-skill-manager. Safeguard infra/research: ml-infrastructure-engineer-safeguards, ml-research-engineer-safeguards.

Files
SKILL.md

AI Risk & Governance

When to Use

  • Classifying AI use cases by risk tier and impact
  • Drafting AI acceptable-use policies and governance frameworks
  • Building AI risk registers with likelihood/severity/mitigation tracking
  • Preparing model cards, system cards, or DPIAs for AI deployments
  • Reviewing third-party LLM vendors (data terms, fine-tuning, safety commitments)
  • Mapping AI products to frameworks (NIST AI RMF, ISO 42001, EU AI Act concepts)
  • Aligning product and engineering teams with compliance requirements
  • Designing human-in-the-loop oversight for consequential AI decisions

When NOT to Use

  • Implementing RAG pipelines, agents, or production features → ai-engineer
  • Running jailbreak tests or adversarial campaigns → ai-redteam
  • SOC 2/ISO evidence automation and technical control mapping → compliance-engineer
  • General SOC 2 IT controls without AI scope → cybersecurity
  • Commercial/enterprise AI solution architecture → applied-ai-architect-commercial-enterprise
  • Skills portfolio governance and publish gates → ai-skill-manager

Related skills

NeedSkill
Building LLM productsai-engineer
Adversarial testingai-redteam
Research and benchmarksai-researcher
Enterprise security programcybersecurity
Pipeline and data securitydevsecops
SOC 2/ISO evidence and technical controlscompliance-engineer
AI solution architecture (commercial/enterprise)applied-ai-architect-commercial-enterprise
Agent skills governanceai-skill-manager
Safeguard gateways, classifiers, rolloutml-infrastructure-engineer-safeguards
Safety classifier research and benchmarksml-research-engineer-safeguards
Privacy research for safeguardsprivacy-research-engineer-safeguards
Enterprise security risk registers (non-AI scope)security-risk-analyst
M&A/investment cyber diligence and board packscyber-diligence-governance

Core Workflows

1. Use-case intake and classification

Capture:

FieldPurpose
Purpose and usersScope and accountability
Data typesPII, special categories, IP
Automation levelHuman-in-loop vs autonomous
Impact if wrongSafety, legal, financial, reputational
External exposureCustomer-facing vs internal

Risk tier (example):

TierCriteriaControls
LowInternal, low impact, no sensitive dataStandard policy + logging
MediumCustomer-facing or internal PIIReview + eval + monitoring
HighRegulated domain, high impact, autonomous actionsGovernance board + red-team + enhanced oversight

See references/risk_classification.md for EU AI Act–oriented mapping (non-legal).

2. Risk assessment

Use structured worksheet:

  1. Identify hazards (bias, hallucination, leakage, misuse, dependency)
  2. Estimate likelihood and severity
  3. Define mitigations (technical, process, legal)
  4. Assign owner and review date
  5. Residual risk acceptance sign-off

See references/risk_assessment.md for worksheets and NIST AI RMF functions.

3. Documentation artifacts

ArtifactWhen
Model card / system cardEvery production model or vendor model
Data sheetTraining/fine-tune data described
Eval summaryPre-deploy and periodic
Incident logAI-specific harms and near-misses

See references/documentation.md for model card sections and change log.

4. Policy and oversight

  • Acceptable use policy (prohibited uses, approval paths)
  • Human oversight rules for consequential decisions
  • Escalation for policy violations and serious incidents
  • Training for builders and reviewers

See references/policy_oversight.md for governance committee cadence.

5. Vendor and third-party models

Review: data processing terms, subprocessors, retention, fine-tuning on customer data, safety commitments, breach notification, exit plan.

See references/vendor_review.md for vendor questionnaire topics.

When to load references

  • Tiering and regulation mapping → references/risk_classification.md
  • Assessments and frameworks → references/risk_assessment.md
  • Model cards → references/documentation.md
  • Policies and committees → references/policy_oversight.md
  • Vendors → references/vendor_review.md

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