Defense in Depth How Best to Prepare Your Data for Your Tools
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Jan 22, 2026 Matt Goodrich, Director of Information Security at Alteryx, shares insights on AI data hygiene and governance. He highlights the challenges of relying on polished AI outputs, warning about their potential misleading nature. Goodrich emphasizes the importance of integrating human oversight in AI workflows and proposes using multiple AIs for cross-verification. He advocates for treating AI outputs as research rather than authoritative, pushing for a skeptical approach, and underscoring the need for governance to establish trust in AI-driven security systems.
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Polished Outputs Can Hide Garbage
- Large language models can turn poor inputs into polished but misleading outputs that seduce users.
- You must evaluate inputs early because beautiful results can hide garbage data.
Always Verify AI Sources
- Verify sources presented by an AI and 'taste test' them by inspecting originals.
- Ask follow-up questions to check provenance and avoid trusting context-less answers.
Keep Humans In The Loop Initially
- Put humans into AI workflows where stakes are high and remove them only after trust is proven.
- Build citation, double-verification, and escalation paths before reducing human oversight.