When gathering open source data and transforming it into actionable intelligence, it is critical to recognize that humans are not objective observers. Conscious and unconscious assumptions drive analysts' choices about which data to analyze and how much importance to ascribe to each resource. Furthermore, analysts' personal conceptual frameworks about reality and how the world works can undermine the process of objectively translating data into intelligence. These implicit assumptions, otherwise known as cognitive biases, can lead to missed data, skewed intelligence, illogical conclusions, and poor decision making. In this presentation I will illustrate some of the cognitive biases relevant to OSINT and what can be done about them.