One of the ways to prevent accidents at sea is to detect risks caused by humans and to counteract them. These tasks can be executed through an analysis of ship maneuvers and the identification of behavior considered to be potentially dangerous, e.g., based on data obtained online from the automatic identification system (AIS). As a result, additional measures or actions can be taken, e.g., passing at a distance greater than previously planned. The detection of risks at sea requires a prior definition of behavior patterns and the criteria assigned to them. Each pattern represents a specific navigator’s safety profile. The criteria assigned to each pattern for the identification of the navigator’s safety profile were determined from previously recorded AIS data. Due to a large amount of data and their complex relationships, these authors have proposed to use data mining tools. This work continues previous research on this subject. The conducted analysis covered data recorded in simulation tests done by navigators. Typical ship encounter situations were included. Based on additional simulation data, the patterns of behavior were verified for the determination of a navigator’s safety profile. An example of using the presented method is given.