The Rule of AI: Making Legal AI Governed, Consistent, and Defensible

Legal AI's real risk isn't a wrong answer. It's an inconsistent one: the same facts producing a different answer six months apart, with no paper trail to catch it the way a human lawyer would leave behind. Most legal AI stacks have a model, but not a rules layer underneath it, and that gap is where the exposure actually lives.

An LLM will always give you a confident answer. A rules engine gives you a defensible one. The teams getting this right aren't choosing between the two, they're building for both, and it's increasingly how prospects describe what they need in their own words: “we don't want creativity, we want consistency.”

This is the fourth talk in a track record that's called each shift early: “GPT and Me” (2023), “Generative Value” (2024), and “The Claude-ification of Legal” (June 2026). In this session, Shaz Aziz, Head of Client Solutions at Neota Logic, unpacks what separates a governed answer from a confident guess, and gives you a way to evaluate what you're actually buying, not just a warning.

What you'll walk away with

• Why consistency, not correctness, is the real accountability gap in legal AI
• The difference between a confident answer and a defensible one, and why you need both
• How a lawyer's mistakes get caught and priced today, and why a model's drift doesn't yet
• The questions to ask any AI vendor about how their answers are versioned, checked, and defended over time