AI Governance Framework
A practical, board-ready governance model for enterprise AI. Move fast without increasing unmanaged exposure.
Executive Summary
AI can create outsized value—while introducing new classes of operational, legal, security, and reputational risk. A governance framework ensures that AI initiatives move fast without increasing unmanaged exposure.
This framework is designed to be lightweight, implementable, and defensible in executive and board settings.
Core Principles
Accountability
Assign clear ownership for approvals, outcomes, and incidents.
Transparency
Document key decisions, data sources, and limitations in plain language.
Security & Privacy
Protect data, control access, and continuously monitor for leakage or abuse.
Risk-Based Controls
Apply stricter review and monitoring as impact increases (tiering).
Value Discipline
Fund initiatives with measurable outcomes and stop low-ROI efforts early.
Governance Operating Model
A clear operating model separates strategic oversight (board/executives) from day-to-day controls (council/teams). Use this as a starting point and adapt titles to match your organization.
| Role | Responsibilities |
|---|---|
| Board / Audit Committee | Oversight, risk appetite, accountability; receives quarterly AI risk and value reporting. |
| Executive Sponsor | Sets priorities, resolves conflicts, ensures funding and cross-functional alignment. |
| AI Governance Council | Approves high-impact use cases, policies, tiering rules; tracks the portfolio. |
| Risk/Compliance/Legal | Defines controls, reviews high-risk uses, ensures regulatory and contractual compliance. |
| Product / Engineering | Builds and operates AI systems; maintains documentation, monitoring, and incident response. |
| Data Governance | Data quality, lineage, and access controls; ensures proper data use and retention. |
Decision Rights by Tier
Tier 1: Low Impact
Team-level approval with standard controls.
Tier 2: Medium Impact
Governance council review, formal documentation, baseline monitoring.
Tier 3: High Impact
Executive approval, enhanced testing, legal/compliance sign-off, and ongoing reporting.
Minimum Viable Policy Pack
Policies should be short, enforceable, and aligned to your operating model. Start with this minimum set; expand as your portfolio grows.
Acceptable Use
What AI tools/models are approved, prohibited, and permitted with restrictions.
Data Use & Privacy
What data can be used, retention rules, and sensitive data handling.
Model Risk Management
Validation requirements, bias testing, and documentation expectations.
Vendor & Third-Party
Procurement requirements, security reviews, and contractual protections.
Human Oversight
Where humans must remain in the loop and escalation paths.
Incident Response
Detection, reporting, and remediation for AI-related incidents.
Control Gates Across the AI Lifecycle
Governance works when embedded into delivery. These gates define where controls apply, what evidence is required, and who approves.
Evidence Artifacts
- Model Card: Purpose, training data summary, limitations, intended users, and risks.
- Data Sheet: Sources, lineage, quality checks, retention, and access controls.
- Test Plan: Accuracy, bias, robustness, security, and red-team results.
- Approval Record: Sign-offs, tier, and required mitigations.
- Monitoring Plan: Metrics, alert thresholds, and incident runbooks.
Implementation Roadmap
Start small, enforce consistently, and iterate. This roadmap prioritizes leverage and speed.
First 30 Days
- • Name an executive sponsor and form a small governance council
- • Define tiering criteria and minimum artifacts
- • Publish a one-page acceptable-use and vendor guardrails policy
- • Create a single intake form and approval workflow
Days 31–60
- • Implement monitoring for priority systems (drift, incidents, value KPIs)
- • Establish incident response playbook and escalation paths
- • Integrate controls into delivery pipelines (gates + checklists)
- • Begin quarterly reporting to executives and the board
Days 61–90
- • Expand the policy pack and training program for teams
- • Harden vendor due diligence and contractual controls
- • Introduce periodic audits for high-impact systems
- • Iterate governance based on metrics and incidents
Need Help Implementing?
This framework is designed to be self-serve, but if you'd like an independent executive assessment and customized roadmap, we're here to help.
Or email us directly: advisory@cichocki.com