"Inside information" comes in many forms: knowledge of a corporate takeover, a terrorist attack, unexpectedly poor earnings, the FDA's acceptance of a new drug, etc. Anyone who knows some piece of soon-to-break news possesses inside information. Historically, insider trading has been detected after the news is public, but this is often too late: fraud has been perpetrated, innocent investors have been disadvantaged, or terrorist acts have been carried out. This paper explores early detection of insider trading - detection before the news breaks. Data mining holds great promise for this emerging application, but the problem also poses significant challenges. We present the specific problem of insider trading in option markets, compare decision tree, logistic regression, and neural net results to results from an expert model, and discuss insights that knowledge discovery techniques shed upon this problem.