AI Governance, Ethics & Explainability

Not at all — most of my AI governance work starts exactly there. I inventory what's actually deployed, assess the privacy and data exposure of each use case, and put proportionate controls around it. You don't rip out tools people rely on; you make their use defensible.

My goal is faster, clearer risk decisions — not blanket refusals. I build lightweight review gates and pre-approved patterns so teams know what's green-lit before they build. The slowdown teams fear usually comes from late-stage surprises, which governance prevents.

I look at what data the model actually touches, where training and inference happen, what the vendor retains, and what their sub-processor chain looks like. Then I map that against your obligations under Law 25, PIPEDA, or GDPR. You get a plain-language risk rating and the specific contract terms worth pushing back on.

A use-case inventory, risk-tiering by impact, data dependency mapping, an acceptable-use and model governance policy, and review gates embedded in your product and security workflows. I tailor it to how your teams already ship software so it gets used instead of filed away.

Yes — that's the bulk of what teams ask about. My evaluations focus on privacy impact, shadow AI exposure, usage controls, and application-security considerations, so innovation doesn't outrun trust.

AI governance adds model and agent inventory, explainability guidance, bias and usage controls, and a dual focus on usage control plus application security — alongside the classic privacy reviews. The risk surface is genuinely different, so the control set has to be.

Usually a 2–3 week AI use-case inventory and risk-tiering sprint. You get a map of what's deployed, what's shadow, and what needs controls first — plus a board-ready summary. From there we can build the full framework or hand your team a roadmap to run with.