The Normalization of Deviance in AI (embracethered.com) AI

The blog argues that AI systems—especially agentic ones—risk “normalizing deviance” by gradually over-trusting unreliable LLM outputs and treating the lack of past failures as proof of safety, despite growing evidence of issues like prompt injection, data exfiltration, and risky tool actions. It cites the idea in the spirit of the Challenger disaster’s warning-sign rationalization and points to multiple vendor warnings and examples where guardrails are limited or human oversight is absent. The author concludes that AI should remain human-led in high-stakes contexts with downstream security controls and threat modeling rather than assuming models will “do the right thing.”