Playbook

Practical AI Governance Starter Playbook

A pragmatic starter playbook with a one‑page policy template, a risk register schema, a decision matrix for human‑in‑the‑loop vs autonomous operations, a governance meeting cadence with agendas and stakeholder roles, and an implementation checklist and KPIs to scale governance with maturity.

Practical AI Governance Starter Playbook

This playbook helps small teams and organizations begin governing AI in a way that protects the organization, preserves innovation, and scales as risk and technical maturity grow. It’s intentionally pragmatic: copy, adapt, and operate the templates below rather than waiting for a perfect policy.

How to use this playbook

  1. Adopt the one‑page policy as your baseline and tailor roles and approval gates to your environment.
  2. Start a living risk register using the provided fields and add every active model, data pipeline, and automation to it.
  3. Use the decision matrix when deciding whether a workflow needs a human in the loop or can be run autonomously.
  4. Run the governance cadences and measure a small set of KPIs; iterate monthly.

One‑Page AI Policy Template

Purpose: Provide a short, actionable policy that clarifies intent, ownership, and minimum controls for AI across development, deployment, and operations.

Policy: Responsible AI Usage (one page)

Scope: All models, data pipelines, and automation used in production or customer‑facing contexts, including third‑party models and hosted services.

Principles: Safety, transparency, accountability, fairness, privacy, and measurable performance.

Minimum Requirements:

  • Document model purpose, owner, and data sources in the risk register before deployment.
  • Classify model criticality (low/medium/high) and required human oversight level.
  • Define performance baselines and monitoring metrics (accuracy, calibration, drift, latency, business metrics).
  • Apply data governance controls for personal or sensitive data; ensure legal and privacy review where needed.
  • Maintain versioned model documentation (training data snapshot, hyperparameters, evaluation, known failure modes).
  • Escalation path for incidents affecting safety, privacy, compliance, or reputation.

Roles & Responsibilities:

  • Model Owner: Responsible for model documentation, monitoring, and remediation.
  • Data Steward: Ensures data quality and regulatory controls.
  • Product / Service Owner: Approves business use and user communications.
  • Legal & Compliance: Advises on regulatory and contractual obligations.
  • Security: Reviews access controls and secrets management.

Review & Approval: Models classified as 'high' criticality require pre‑deployment review by a cross‑functional governance board.

Review Cycle: Policy and inventory reviewed quarterly or when a major change occurs.

Risk Register: Suggested Fields (living document)

Use a table or interactive form to maintain this registry. Capture enough information for risk assessment and action planning.

Field Purpose / Example
Entry ID Unique identifier
Model/Automation Name "Invoice OCR v1"
Purpose Business use and user impact
Owner Person accountable for operation and monitoring
Data Sources Inputs, sensitive data flags
Criticality Low / Medium / High
Autonomy Level Human‑in‑loop / Assisted / Autonomous
Primary Risks Bias, privacy, safety, regulatory, business continuity
Controls & Safeguards Approval gates, fallbacks, monitoring, explainability
Mitigation Actions Planned remediation and responsible parties
Monitoring Metrics Drift score, error rate, false positive rate, latency
Last Review Date and reviewer

Decision Matrix: Human‑in‑Loop vs Autonomous

Use this simple matrix to decide required oversight based on criticality and uncertainty.

Model Criticality High Uncertainty / High Impact Moderate Low Impact / Low Uncertainty
High Require human‑in‑loop with approval for each decision; strict monitoring and pre‑deployment board review. Human oversight for exceptions; automated in routine cases with alerts. Consider limited autonomy with tight rollback and continuous monitoring.
Medium Assisted operation — human reviews flagged cases; gradual autonomy in stable windows. Assisted with performance checks; scheduled audits. Autonomy ok with monitoring and business owner sign‑off.
Low Assisted; consider redesign to reduce uncertainty. Autonomy allowed with lightweight controls. Autonomous ok; periodic spot checks.

Governance Meeting Cadence, Agendas & Roles

Keep meetings short, outcome‑oriented, and with clear ownership. Suggested cadences:

  • Weekly Triage (30–60 min): Operational owners, data scientists, and SREs. Agenda: active incidents, model health alerts, urgent changes. Output: action items and owners.
  • Monthly Review (60–90 min): Model owners, product leads, data steward, security. Agenda: new deployments, trending drift, control effectiveness, open mitigations. Output: prioritized remediation backlog.
  • Quarterly Governance Board (90 min): Senior sponsors, legal/compliance, security, ethics rep. Agenda: high‑risk model approvals, audit results, policy updates, resourcing. Output: approvals, strategy decisions.

Suggested meeting roles:

  • Chair: Product or program manager who runs the cadence.
  • Model Owner: Presents status and actions.
  • Data Steward: Notes data and privacy issues.
  • Security & Ops: Reports on incidents and infrastructure risks.
  • Legal/Compliance: Flags regulatory or contractual concerns.
  • Scribe: Records decisions and updates the risk register.

KPIs and Early Warning Signals

Measure a small set of indicators that matter to business and risk:

  • Inventory Coverage: % of models documented in the registry.
  • Mean Time to Detect (MTTD) model drift or incidents.
  • Mean Time to Remediate (MTTR) incidents or regressions.
  • Number of high‑risk models with current mitigation plans.
  • Audit Score: % of mandatory controls implemented.

Common Pitfalls & Quick Fixes

  • Pitfall: Governance is a blocker. Fix: Use lightweight approvals for low‑risk models and time‑boxed experiment lanes.
  • Pitfall: Missing ownership. Fix: Require a named model owner before any deployment.
  • Pitfall: Overly detailed controls from day one. Fix: Apply controls proportional to criticality and evolve with the registry.

Implementation Checklist (first 90 days)

  1. Adopt the one‑page policy and publish it to teams.
  2. Create the living risk register and add all production models (minimum fields from table).
  3. Classify each model’s criticality and autonomy level using the decision matrix.
  4. Establish weekly triage and monthly review cadences; assign roles and scribe.
  5. Instrument basic monitoring (error rates, latency, simple drift metrics).
  6. Run a first governance board session for any high‑risk model.

Next Steps and Tailoring

Adapt this playbook to your legal/regulatory environment and technical stack. Keep documentation lightweight at first and focus on high‑impact controls. Over time, add model versioning, automated monitoring, and audit artifacts.

Appendix: Suggested Templates & Tools

  • Risk register spreadsheet or interactive form with the fields above.
  • Pre‑deployment checklist (sample): owner assigned, purpose documented, data review complete, monitoring configured, rollback plan defined.
  • Incident report template: timeline, impact, root cause, remediation actions.

Use this starter playbook as a living artifact. Governance should reduce surprise and enable safe experimentation, not stop it.

Discuss Ask the Librarian

Discussion

Comments and conversation will live here.