AI Governance as Engineering Work

AI governance is often described as policy, oversight, or compliance. Those pieces matter, but governance also has to become engineering work.

A useful governance process should connect risks to artifacts: model behavior, data quality, system boundaries, logs, prompts, tests, permissions, and human review paths. Without those artifacts, governance becomes a document rather than an operating practice.

My current interest is in risk mapping that gives teams practical ways to ask better questions: what could fail, what evidence would reveal it, and what controls can reduce the risk before deployment?