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.