Fireside Chat: AI Trust and Governance in QE - Who Actually Signs Off
summary
Argues that QE must own an accountability layer as teams ship code that AI writes and runs tests, showing why governance either becomes compliance theater or actual controls that detect model drift, provide traceability of prompts and test artifacts, and limit decisions that models can make. Covers concrete pipelines: embed governance checks, observability for post‑production drift, tool selection strategies (reuse existing vendors, adopt AI‑native tools, or build an enterprise control plane integrating models like Anthropic/Google/AWS), and operational rules like fail fast and fix fast.
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chapters (11)
- 0:00Who Owns AI Trust and Governance in Quality Engineering?
- 2:40How Did Software Testing Change Before the AI Era?
- 5:12Why Does AI Introduce New Quality and Governance Risks?
- 7:12Why Trust Matters More Than Traditional Test Coverage
- 7:42If AI Writes and Runs Tests, Who Should Sign Off?
- 8:13How Do You Build Governance Into AI Testing Pipelines?
- 11:16Will AI Replace Quality Engineers or Change Their Role?
- 16:24What Evidence Is Needed to Approve AI Systems for Production?
- 20:00How Should Enterprises Choose AI Testing Tools and Frameworks?
- 24:37How Do You Test AI Agents for Safety, Bias, Security, and Reliability?
- 29:17Why Human Sign-Off Still Matters in AI Quality Engineering
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