SecurityWeek–Managing the Trust-Risk Equation in AI: Predicting Hallucinations Before They Strike

New physics-based research suggests large language models could predict when their own answers are about to go wrong, offering potential game-changing implications for trust, risk, and security in AI-driven systems. Researchers are applying physics principles to predict AI hallucinations in real time, which could significantly reduce risks for cybersecurity, defense, and other high-stakes industries.

Brad Micklea, CEO and co-founder of Jozu, notes that this approach differs from most hallucination detection methods which require the response to be complete before evaluation can occur. "This is different from most of the hallucination detection / fixing approaches which require the response to be complete before you can evaluate it," Micklea comments, highlighting the potential for real-time monitoring and intervention.

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