AI isn’t hallucinating—but your data might be

“Hallucination” has become the catch-all term for AI getting things wrong. It describes a real phenomenon: Large language models can generate confident, fluent answers that are simply untrue. But in IT operations, many errors labeled as hallucinations have a more ordinary explanation. The AI isn’t inventing facts. It’s faithfully reporting information that happens to be outdated, incomplete, or contradictory. I’ve seen this play out across enough environments to know the cause usually sits upstream, in the data the system learned from and the picture of the estate it read. Fix the data foundation under your AI systems → That distinction matters, because the fixes are different. You don’t tune the model. You fix the information it relies on. When the answer is wrong, but the logic is right Imagine a service desk analyst asking an AI assistant whether a user’s laptop has received the latest security update. The assistant says yes. The analyst closes the ticket. Two days later, the device is flagged in a vulnerability scan. What happened? The assistant pulled from an endpoint record last updated before the device went offline for a week. The data was accurate when it was written. It …

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