Technical AI Governance Leader
Credentials
- Chief AI Officer — GrayCloudAi
- NIST AI standards contributor — Generative AI working group
- Author — Constitutional Democracy in the Algorithmic Age (Springer)
- US Congressional Record — recognised for AI & blockchain leadership
I architect enterprise AI governance — the technical controls that make constitutional protections, regulatory compliance and accountability properties of the system rather than promises in a policy document. The work sits between engineering and legal, which is where most governance programs fail: the lawyers write requirements the engineers can't implement, and the engineers ship systems the lawyers can't defend.
Twenty-five years of it, across Fortune 500 companies and startups, on systems making millions of decisions a day. The frameworks I've built have reduced compliance violations without slowing delivery — mostly by catching problems at design time, when they're cheap, instead of at audit, when they aren't.
Leadership & Impact
Senior technical roles where governance was mission-critical rather than an afterthought — regulated industries where an algorithmic failure carries eight-figure liability and a regulator's phone call.
Chief AI Officer — built a 38-control governance framework achieving EU AI Act compliance for enterprise AI products in global markets.
SVP Technology Strategy — led cross-functional teams building privacy-preserving ML systems (patent pending) for healthcare AI training at scale.
VP AI Governance — designed algorithmic accountability frameworks that reduced compliance issues at Airbnb.
Policy — member of the NIST Generative AI working group, contributing to federal regulatory frameworks.
Enterprise Implementations
These are production systems, not reference architectures — built for financial services and healthcare AI, where the framework has to satisfy a regulator and a revenue target at the same time.
38-control governance framework — EU AI Act compliance, deployed across a multi-billion dollar AI product portfolio.
Privacy-preserving training architecture — patent-pending distributed learning that lets institutions train on data they can't pool.
Algorithmic impact assessment protocol — board-ready framework that cut audit findings without degrading model performance.
Speaking
I speak to boards, C-suites and engineering teams about where AI innovation meets constitutional accountability — translating governance requirements into technical decisions someone can actually action on Monday.
Here I walk through the patent-pending distributed training architecture — turning idle hospital computers into a secure training network, so institutions can build models on data that legally can't leave the building. An example of protections engineered into the infrastructure rather than bolted on after.
Speaking topics
- Building board-ready AI governance programs
- EU AI Act technical compliance for enterprise
- Constitutional protections by design
- Algorithmic accountability for the C-suite
Advisory & Board Positions
I work with boards, C-suites and GC offices carrying real regulatory exposure on AI. That's either an advisory board seat with technical governance depth, or hands-on work with the teams building the thing. If you have a deadline, an audit, or a model you couldn't currently explain to your board, that's the conversation worth having.
Advisory services Schedule a consultation