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.

The model layer keeps moving. Anthropic's Claude for Legal set the terms of this conversation months ago, Google has since answered with Gemini Enterprise for Legal, and OpenAI's new GPT-6 Astra is being pitched as its most capable release yet. Each vendor is competing on the same axis: how smart the model is. None of them are promising the same answer twice.

An LLM will always give you a confident answer. A rules engine gives you a defensible one. Legal AI needs both: the fluency to interpret a question, and the structure to guarantee the same answer holds up next time.

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