Cyber Security Awareness Campaigns: Why do they fail to change behaviour?
read the original abstract
The present paper focuses on Cyber Security Awareness Campaigns, and aims to identify key factors regarding security which may lead them to failing to appropriately change people's behaviour. Past and current efforts to improve information-security practices and promote a sustainable society have not had the desired impact. It is important therefore to critically reflect on the challenges involved in improving information-security behaviours for citizens, consumers and employees. In particular, our work considers these challenges from a Psychology perspective, as we believe that understanding how people perceive risks is critical to creating effective awareness campaigns. Changing behaviour requires more than providing information about risks and reactive behaviours - firstly, people must be able to understand and apply the advice, and secondly, they must be motivated and willing to do so - and the latter requires changes to attitudes and intentions. These antecedents of behaviour change are identified in several psychological models of behaviour. We review the suitability of persuasion techniques, including the widely used 'fear appeals'. From this range of literature, we extract essential components for an awareness campaign as well as factors which can lead to a campaign's success or failure. Finally, we present examples of existing awareness campaigns in different cultures (the UK and Africa) and reflect on these.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
ConGISATA: A Framework for Continuous Gamified Information Security Awareness Training and Assessment
ConGISATA framework uses mobile sensors for continuous gamified ISA training and assessment, with evaluation showing improved awareness in sensor metrics and attack simulations.
-
SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training
SentinelSphere integrates an AI threat detector using an enhanced DNN on benchmark datasets with a fine-tuned quantized LLM for user training and awareness.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.