
0:00-43:55
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As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today’s black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems.
Featuring:
- Alizishaan Khatri – LinkedIn
- Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
- Daniel Whitenack – Website, GitHub, X
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- 0:00Welcome to Practical AI
- 0:48Meet the guest
- 3:14Security for AI
- 8:46Finding a starting point
- 10:47Guarding the gate
- 13:25Sponsor: Miro
- 15:44Interpretability
- 23:19Spectrum of burden
- 26:18Guardrails
- 30:34Accuracy and quality
- 40:25The future question
- 42:29Outro