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.
This is a generic, board-ready starting framework. It is not certified compliance with any of the standards below.
Use it as a starting point; customize policies, tiering thresholds, and control gates for your regulatory context, risk appetite, and operating model. For a regulator-anchored adaptation, see the ISO 42001 / NIST AI RMF crosswalk or work with us.
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.
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, and tiering rules. Tracks the AI 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 Click any tier for detail
AI Risk Categories
AI introduces new failure modes alongside the existing operational risks. Categorizing them gives the board a shared vocabulary for what we control, what we accept, and what we transfer.
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.
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.
Click any gate for required artifacts and approvals.
Evidence Artifacts
Use-case intake form
Captures business owner, expected users, data classes, decision impact, regulatory context. Drives tier assignment.
Model card
Datasheet describing model purpose, training data, known limitations, performance profile, retraining triggers.
Risk register entry
Per-use-case risk profile, mitigations, residual risk, sign-off chain. Required for Tier 2+ and audited.
Deployment runbook
Step-by-step launch plan with rollback criteria, named ops owner, monitoring thresholds, and incident escalation.
Monitoring dashboard
Live drift detection, accuracy thresholds, complaint rate, bias re-audit calendar. Tied to retirement criteria.
Board reporting pack
Quarterly portfolio view: tiers in flight, control coverage, incidents, regulatory mapping refresh.
Implementation Roadmap
Three phases from framework approval to first board reporting cycle. Designed so each phase ships visible artifacts before the next begins.
Foundation
- Form Governance Council and identify Executive Sponsor
- Approve tiering framework and policy pack outline
- Inventory current AI initiatives across the org
- Identify 1–2 priority use cases to govern first
Build & Pilot
- Publish initial six-policy pack with named owners
- Run Gates 1–4 on priority use cases
- Stand up monitoring dashboard and incident playbook
- First model card published; bias check live
Operate & Report
- Deploy first governed use case under Gates 5–6
- First quarterly Board reporting pack delivered
- Tabletop incident response with named owners
- 90-day retrospective; expand to next 3 use cases
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
AI Governance Maturity Model
A five-level ladder for executives to honestly locate where the organization stands today — and where the next quarter takes it. Click any level for what defines it, what artifacts exist, and the realistic time-to-next-level.
Companion materials
ISO 42001 vs. NIST AI RMF
How U.S. enterprises should think about ISO/IEC 42001 alongside NIST AI RMF — what overlaps, what diverges.
90-Day AI Governance Roadmap
Phased gates, deliverables, and evidence the board can defend — framework to shipped controls in 90 days.
Advisory engagements
Where the framework runs into your context — tiering, control gates, evidence binders — we operationalize.
