AI Governance
Practical, board-defensible AI governance — guardrails, oversight, and risk controls that let you ship AI without flying blind.
Most companies are not short on AI ambition. They're short on anyone accountable for what happens when an AI system gets something wrong in production — and by most measures, almost nobody at the top is actually watching.
Governance hasn't caught up with adoption. Only 17% of organizations say their board directly oversees AI governance, and just 28% say their CEO takes direct responsibility for it. Separately, IBM's 2025 Cost of a Data Breach study found that AI adoption is outpacing AI governance industry-wide — most organizations that have deployed AI still lack a policy to manage its risk, and the ones without one pay more when something goes wrong.
Sources: McKinsey, "The State of AI" (2025) ↗ · IBM Cost of a Data Breach Report 2025, via HIPAA Journal ↗
What Our AI Governance Practice Covers
- Practical guardrails for production AI systems: human-in-the-loop checkpoints, escalation paths, and audit trails that hold up under real scrutiny.
- Board and executive briefings that translate AI risk into decisions someone can actually sign off on.
- Vendor and model evaluation — including bias, data handling, and liability exposure — before procurement, not after.
- Governance frameworks scoped to your regulatory reality, including HIPAA-aware controls for healthcare deployments — see our Healthcare IT practice.
- Incident response planning specific to AI failure modes, not retrofitted from a generic security playbook.
Why Tristella
This practice is anchored by John Moore, who spent 30 years in technology leadership, including roles as VP of Engineering and CTO, and now writes a weekly newsletter on practical, no-hype AI governance for executives. The frameworks we build come from someone who has had to defend a technology decision to a board, not just write about one.
How Engagements Work
| Engagement | What's Included | Fee |
|---|---|---|
| AI Readiness Assessment | Assess data, tooling, and team readiness for AI deployment, including risk and dependency mapping (2–3 weeks) | $5,000–$10,000 |
| AI Strategy Workshop | Half-day facilitated session: use case identification, prioritization, and roadmap framing | $3,000–$5,500 |
| Fractional CTO Retainer | AI governance delivered as part of an ongoing fractional CTO engagement | $10,000–$20,000/mo |
Most clients fold AI governance into a broader Fractional CTO retainer once the initial framework is in place. Nonprofits and mission-driven organizations receive a 10–15% discount on all engagements.
If you've already deployed AI and are retrofitting governance after the fact, say so when you get in touch — that's a different (and more urgent) conversation than starting from scratch.