Practical AI Governance For Product Leaders

AI governance can feel like an enterprise-only exercise, yet even lean product teams need rituals that keep customer trust front and center. The goal is not bureaucracy; it is clarity on what we build, why it is safe, and how we course-correct when signals drift.

Establish A Decision Log

Create a shared document that records model choices, prompts, training data, and reviewers. When regulators or customers ask “why this model?”, the answer is already written down. We update the log during sprint reviews so it never becomes a shelf artifact.

Define Red Lines With Legal Early

Meet with legal or compliance partners before the first line of code ships. Align on non-negotiables like data residency, audit retention, and human override requirements. With bounds clarified, delivery squads can move fast while honoring governance guardrails.

Run Scenario Tests Monthly

Schedule tabletop exercises where the team simulates failures such as biased outputs or prompt injection. Capture remediation steps and update runbooks. These drills keep the team ready for production surprises and demonstrate continuous oversight to stakeholders.