A Taxonomy of SETA Methods and Linkage to Delivery Preferences

Abstract

Cybersecurity threats targeting users are common in today's information systems. Threat actors exploit human behavior to gain unauthorized access to systems and data. The common suggestion for addressing this problem is to train users to behave better using SETA programs. The notion of training users is old, and several SETA methods are described in scientific literature. Yet, incidents stemming from insecure user behavior continue to happen and are reported as one of the most common types of incidents. Researchers argue that empirically proven SETA programs are needed and point out focus on knowledge rather than behavior, along with poor user adoption, as problems with existing programs. The present study aims to research user preferences regarding SETA methods, with the motivation that a user is more likely to adopt a program perceived positively. A qualitative approach is used to identify existing SETA methods, and a quantitative approach is used to measure user preferences regarding SETA delivery. We show that users prefer SETA methods to be effortless and flexible and outline how existing methods meet that preference. The results outline how SETA methods respond to user preferences and how different SETA methods can be implemented to maximize user perception, thereby supporting user adoption.