AI Governance for Law Firms: A Practical Starting Checklist

For most firms, especially smaller ones, 'AI governance' doesn't need to start as a written policy document. It can start as a short, specific set of questions the firm has actually answered before adopting a tool - and that gets revisited as usage grows.

Where does the tool's output actually come from - is it grounded in identifiable sources, or generated freely? Who at the firm is responsible for reviewing AI-assisted output before it's relied upon or sent to a client? What happens to the data you put into the tool - is it used to train the vendor's models, and does the vendor say so clearly? What's the fallback if the tool is wrong, unavailable, or produces something that shouldn't have gone out?

None of these require sophisticated tooling to answer - they require the firm to have actually had the conversation and agreed on an answer, rather than assuming AI output is automatically safe to use.

Whatever else a firm's AI governance approach includes, the one non-negotiable piece is a human review step before AI-assisted output - drafts, research summaries, client communications - is relied upon or sent externally. This isn't about distrust of the tool; it's the same standard that should apply to a junior associate's first draft.

As usage scales past a couple of advocates, it becomes worth writing the informal answers down - who reviews what, what tools are approved, what data categories are off-limits for AI processing - so the standard doesn't depend on institutional memory.