AI-generated misinformation poses a significant threat to critical infrastructure as these systems increasingly rely on artificial intelligence to inform decision-making. When AI models are uncertain, they produce responses based on patterns in their training data, even if those responses are incorrect, thereby exploiting human trust in the technology. This phenomenon, known as AI hallucinations, can have severe consequences as incorrect yet confident outputs can lead to misguided decisions. The lack of a mechanism to recognize uncertainty in AI models means that erroneous information can be presented as fact, creating real security risks. For instance, an AI system may incorrectly identify a benign event as a security threat, triggering unnecessary and potentially disruptive responses. The potential for AI hallucinations to compromise critical infrastructure underscores the need for robust validation and verification mechanisms to ensure the accuracy of AI-generated information, so practitioners must prioritize the development of more transparent and reliable AI systems to mitigate these risks1.