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This paper reports the first set of results from a comprehensive set of experiments to detect realistic insider threat instances in a real corporate database of computer usage activity. It focuses on the application of domain knowledge to provide starting points for further analysis. Domain knowledge is applied (1) to select appropriate features for use by structural anomaly detection algorithms, (2) to identify features indicative of activity known to be associated with insider threat, and (3) to model known or suspected instances of insider threat scenarios. We also introduce a visual language for specifying anomalies across different types of data, entities, baseline populations, and temporal ranges. Preliminary results of our experiments on two months of live data suggest that these methods are promising, with several experiments providing area under the curve scores close to 1.0 and lifts ranging from ×20 to ×30 over random.