Responsible AI: Governance, Risk, and Oversight

Board Brief · 2026

Goal: Align on policy stack, oversight cadence, and funding for training/controls.
Sources: NIST AI Risk Management Framework (RMF); ISO/IEC 42001 crosswalks; AI literacy guides.

Why This Matters (Now)

Regulatory/contractual: ISO 42001 momentum; public-sector expectations for responsible AI.
Operational risk: model errors, safety gaps, mis/disinformation exposure.
Reputation: partners and patrons expect trustworthy AI.

Standards Spine: NIST AI RMF ↔ ISO 42001

NIST RMF cycle: GOVERN → MAP → MEASURE → MANAGE → MONITOR
ISO 42001 alignment: Context/Leadership (4–7) → Operation (8) → Performance/Improvement (9–10)

Use RMF as our operating model; stay 42001-ready if certification is needed.

Who Uses These, and When?

Public sector and regulated firms (finance/health/critical infra) to show responsible AI.
Tech vendors and clouds to reassure customers with a standard playbook.
Auditors and consultants (Big Four and boutiques) to structure gap assessments and readiness.
Applied at: pre-launch (intake/testing/approval), vendor reviews, ongoing monitoring, and audit/certification moments.

Policy Stack (Board Approval)

Acceptable AI Use
Data Governance
Model Risk Management
AI Incident Response
Third-Party AI Review

Procedures: Lifecycle Controls

Intake + risk assessment before build/buy.
Testing/validation: robustness, fairness, security; defined sign-off gates.
Deployment gates: go/no-go with mapped controls.
Monitoring & drift checks; periodic reviews; decommission path.

Procedures: Safety & Mis/Disinformation

Detection playbook for misleading/harmful outputs.
Escalation and communications templates.
User guardrails/disclaimers where appropriate.

Training & Literacy

Leaders

30-minute briefing: obligations, risk appetite, oversight asks.

Practitioners

2-hour workshop: intake, testing standards, documentation.

All Staff

Quarterly micro-learnings and refreshers.

Sources: Microsoft_AI_Literacy_Starting_Guide_2025.pdf; Yale_AI_Literacy_Framework_2025.pdf; AI_Literacy_Framework_ParadoxLearning_2024.pdf; AI_Literacy_Framework_DigitalPromise_2024.pdf.

Oversight Model

Board: sets risk appetite; receives quarterly AI risk report.
Risk/Compliance: owns controls, assessments, reporting.
Product/IT/Data: executes gates, monitoring, documentation.
Internal Audit: independent checks against RMF/42001 controls.

Metrics Snapshot

Metric	Target (example)
% AI systems risk-assessed	100% before launch
Validation coverage (robustness/fairness/security)	100% of in-scope systems
Training completion	≥ 95% of required audiences
Third-party AI reviews completed	All new vendors/models pre-contract
Incidents/exceptions	Tracked with remediation cycle time
Use cases in monitoring	Top 5 live use cases monitored

Roadmap (0–3–6 Months)

0–30 days: approve policies; pilot intake form; launch training v1.
30–90 days: assess top 3 AI uses; apply validation checklist; start monitoring reports.
90–180 days: third-party AI review playbook; ISO 42001 readiness check.

Spotlight: High-Impact AI Use Cases

Content Safety & Moderation (UGC)Safety: Screen reviews/comments/uploads for harm/misinfo; balance false positives/negatives.
Personalized Reading PathwaysEquity: Build multi-book/media plans; check fairness across demographics; explain picks.
Multilingual Reference Co-PilotQuality: Assist librarians; enforce citations/“don’t answer” rules; reduce hallucinations.
Accessibility EnhancerAccess: Convert to audio/large-print/summaries; validate fidelity; protect PII.
Sensitive-Topic ShieldRisk: Flag health/legal/children topics for human review; track escalation SLAs.

All use cases follow intake, testing, deployment gates, and monitoring per the RMF/ISO controls.

Decisions / Board Asks

Approve policy stack and oversight cadence.
Approve budget for training and validation/tooling.
Endorse third-party AI review requirement for new vendors/models.