Section 07 · Adoption
Stage adoption. Don’t ship perfection.
AI governance initiatives commonly stall when they attempt to implement everything at once, over-engineer controls before risk tiering exists, or lack executive sponsorship and political alignment. The framework is designed for staged adoption. The goal is structured, scalable control maturity, not immediate perfection.
Phased rollout
Five phases. Phase 0 is the most commonly skipped, and the most commonly fatal. Phase 90+ is the most commonly underspecified.
Sponsor Alignment
The phase that determines whether the rest happens. Funding, executive endorsement, and political alignment land before day 0. If they don't, the program stalls in Phase 1.
Objectives
- Identify and confirm executive sponsor
- Secure budget for monitoring and registry tooling
- Align Security, Risk, Platform, and Product on scope
- Frame the initiative as enablement, not restriction
- Identify pilot ML/AI systems for Phase 1
Deliverable
Signed sponsor mandate and identified pilot scope. Phase 1 has air cover before kickoff.
Foundation
Visibility before friction. The point of Phase 1 is to know what AI exists and roughly what it's worth governing.
Objectives
- Define governance ownership (Security / Risk / Platform)
- Publish risk tiering model
- Establish centralized AI system registry
- Pilot tier classification across 3–5 active systems
Deliverable
Enterprise AI inventory with assigned provisional risk tiers.
Operational Embedding
Move from registry to workflow. Tier assignment becomes a step in development intake, and threat modeling becomes a template, not a meeting.
Objectives
- Integrate tier assignment into development intake
- Introduce threat modeling template for Tier 2+ systems
- Define escalation triggers
- Align AI incidents with existing IR playbook
- Begin baseline monitoring metrics for Tier 3 systems
Deliverable
First controlled AI deployments under the framework.
Governance Formalization
Make the workflow durable. Tier 3–4 review, vendor checklist, dashboarding, and tabletop exercises convert governance from initiative to practice.
Objectives
- Implement structured Tier 3–4 review workflow
- Integrate vendor AI review checklist
- Launch monitoring dashboard (even if minimal)
- Define executive reporting cadence
- Conduct tabletop AI incident simulation
Deliverable
AI Secure-by-Design operational baseline achieved.
Continuous Improvement
Most rollouts ship Phase 3 then drift. The framework decays unless monitoring posture and metrics close the loop. Phase 90+ is where governance becomes operational practice.
Objectives
- Quarterly tier registry review
- Annual threat model refresh for Tier 3–4
- Continuous adversarial simulation cadence
- Vendor recertification cycle
- Adoption metrics reviewed at executive cadence
Deliverable
Governance posture monitored as a first-class operational metric.
Executive sponsorship
Successful AI governance requires visible executive backing. Recommended stakeholders:
Sponsor responsibilities: endorse the tiered approach, approve escalation structure, accept documented risk decisions, support resource allocation for monitoring infrastructure.
Anti-patterns to avoid
Most AI governance failures repeat one of these four. Frame the initiative around enabling safe innovation, protecting model integrity, reducing audit friction, increasing customer trust, and supporting AI scaling, not around restriction.
Implementing everything at once
Tries to ship full controls across all systems on day one. Burns political capital before the registry is even populated.
Over-engineering before tiering exists
Designs Tier 4 controls in detail before knowing which systems are Tier 4. Inverts the engineering.
Lacking executive sponsorship
Governance without visible executive backing becomes symbolic. Phase 0 exists for this reason.
Framing as restriction
If product teams hear 'compliance,' they hear 'slowdown.' Frame as enabling safe innovation.
Maturity roadmap
Progressive milestones for long-term scaling without immediate overhaul.
Level 1
Inventory + tiering + basic logging
Level 2
Threat modeling + structured monitoring
Level 3
Red teaming + executive dashboard
Level 4
Continuous adversarial simulation + automated risk scoring
Adoption metrics
Metrics turn governance into measurable performance. Track at quarterly cadence.
- % of AI systems tier-classified
- % Tier 3–4 systems with completed threat models
- Time from drift detection to remediation
- AI-related incidents per quarter
- Vendor AI review coverage
- Risk exceptions open vs. resolved