From the Trenches: Solving AI Governance Challenges
summary
Practical examples focus on building an internal AI development platform that provisions test QA and production environments, ships guardrails-as-a-service by default, and lets teams fine‑tune filters; automated logging and tooling capture dataset extraction and model documentation to avoid manual burden; governance uses the OECD definition to cover both in‑house and procured systems; performance reviews and leadership buy‑in enforce uptake, and vendor/supply‑chain checks extend controls across the lifecycle.
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chapters (9)
- 0:00AI governance introduction
- 1:46Defining AI governance
- 6:00Security lessons for AI
- 9:12Legacy frameworks for AI
- 12:41Efficiency versus compliance
- 17:34Governance in developer workflows
- 20:02AI governance scope
- 24:25Legal landscapes for AI
- 26:51Adaptive AI governance
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