# EP 40: Governance First: The Architecture Framework That Makes AI Auditable, Defensible, and 99% Cheaper

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Text version: https://stenobird.com/podcast/data-science-with-sam-4345646/ep-40-governance-first-the-architecture-framework-that-makes-ai-auditable-defensible-and-99-cheaper.md
Podcast: [Data Science With Sam](https://stenobird.com/podcast/data-science-with-sam-4345646)
Published: 2026-05-21T23:50:57+00:00
Episode link: https://rss.com/podcasts/data-science-with-sam/3006408
Audio file: https://content.rss.com/episodes/395137/3006408/data-science-with-sam/2026_07_20_13_47_09_2b365fa5-bd8b-4e39-942a-8f7e362f69a3.mp3
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Duration seconds: 1650

## Resource

Most AI governance is a policy document that nobody enforces. And in high-stakes environments - legal, healthcare, finance - that gap between policy and architecture is where disasters happen. In this episode, Dan Driver, founder of Driver AI Agency, walks through exactly how he built CaseReady Intake AI: a legal AI system with governance baked into every architectural decision, zero hallucination risk by design, prompt injection blocked at the pipeline, and a single architectural choice that cut per-call compute costs by over 99%. Dan is not a lawyer. Not a developer by trade. He's a 25-year problem-solver with a Six Sigma and ISO background from DuPont, who navigated the EEOC employment discrimination process twice without an attorney - and then built the tool he needed. This is a technical governance conversation grounded in lived experience. ▸ WHAT YOU'LL LEARN ▪ What 'governance in motion' actually means: Dan's 10-page charter that every architectural decision is audited against — and how a pre-launch UPL (unlicensed practice of law) audit delayed his release by two weeks, and why that was the right call ▪ Why governance can't just be a PDF: how banning AI without a governance framework only creates shadow IT and makes the risks invisible rather than eliminating them ▪ How deterministic controls eliminate hallucination risk: Python-based Boolean filters and regex on the front and back end of the LLM pipeline mean the AI is never left alone with a surface that can create legal exposure ▪ When NOT to use an LLM: date calculations, scope checks, and out-of-range warnings are all handled by deterministic Python — the LLM only handles what it's actually suited for ▪ Prompt injection defence in practice: the final stage of CaseReady's pipeline is an AI check that valida…

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