Developing ethical principle awareness and reasoning in a cybersecurity context: Enhancing user understanding using ripple down rules

Cybersecurity breaches are often attributed to human behaviour, where individuals fail to integrate ethical principles in their decision-making. This empirical study investigates the effectiveness of the Ripple Down Rules (RDR) method, a knowledge acquisition and representation method, in enhancing ethical awareness and reasoning in cybersecurity contexts. The proposed approach combines rule-based reasoning, case-based learning, reflection, and situated cognition to bridge the gap between ethical knowledge and action by systematically connecting scenario elements to ethical principles. Participants, recruited from a cohort of first-year psychology students, were exposed to training incorporating five ethical principles—Beneficence, Non-Maleficence, Justice, Autonomy, and Explicability—applied to realistic cybersecurity scenarios. The study employed a randomised controlled design with two treatment and one control groups, using pre- and post-study assessments to evaluate improvements in ethical principle identification and reasoning. Participants rated RDR as a clear and helpful tool for understanding ethical reasoning, with sensibility and helpfulness scores ranging from moderate to high. Results demonstrate that RDR training significantly improved participants' ability to identify ethical principles compared to learning without RDR, particularly for principles like autonomy and explicability. However, challenges persisted in distinguishing overlapping principles, such as beneficence and non-maleficence. Implications and guidance for use of RDR for ethics training are discussed.

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Publication Details

Journal
Computers & Security
Published
2025-11-17
DOI
https://doi.org/10.1016/j.cose.2025.104761
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
0.00
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article

Developing ethical principle awareness and reasoning in a cybersecurity context: Enhancing user understanding using ripple down rules

Deborah Richards, Amal Abdulrahman, Paul Formosa, Ayse Bilgin
Computers & Security
Ethics and Social Impacts of AI
article

Developing ethical principle awareness and reasoning in a cybersecurity context: Enhancing user understanding using ripple down rules

Deborah Richards, Amal Abdulrahman, Paul Formosa, Ayse Bilgin
article en

Abstract

Cybersecurity breaches are often attributed to human behaviour, where individuals fail to integrate ethical principles in their decision-making. This empirical study investigates the effectiveness of the Ripple Down Rules (RDR) method, a knowledge acquisition and representation method, in enhancing ethical awareness and reasoning in cybersecurity contexts. The proposed approach combines rule-based reasoning, case-based learning, reflection, and situated cognition to bridge the gap between ethical knowledge and action by systematically connecting scenario elements to ethical principles. Participants, recruited from a cohort of first-year psychology students, were exposed to training incorporating five ethical principles—Beneficence, Non-Maleficence, Justice, Autonomy, and Explicability—applied to realistic cybersecurity scenarios. The study employed a randomised controlled design with two treatment and one control groups, using pre- and post-study assessments to evaluate improvements in ethical principle identification and reasoning. Participants rated RDR as a clear and helpful tool for understanding ethical reasoning, with sensibility and helpfulness scores ranging from moderate to high. Results demonstrate that RDR training significantly improved participants' ability to identify ethical principles compared to learning without RDR, particularly for principles like autonomy and explicability. However, challenges persisted in distinguishing overlapping principles, such as beneficence and non-maleficence. Implications and guidance for use of RDR for ethics training are discussed.

Computers & SecurityVol. 161
Openalex Percentile: Top 65%
Ethics and Social Impacts of AI
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