Understanding the Role of Training for Trust in AI-Based Cybersecurity

Aim/Purpose This paper investigates the impact of training on trust in AI-based cybersecurity solutions, addressing challenges related to skills development and trust dynamics. Background Implementing AI in cybersecurity has proven effective by enhancing threat identification, management, and prevention capabilities. Proper training and education facilitate comprehension of AI solutions and concepts, assisting cybersecurity professionals in utilizing the technology. Experts use AI-based cybersecurity systems based on their trust in these systems. Methodology A structured survey was conducted with 100 cybersecurity experts. Data were analyzed using multiple regression and structural equation modeling to explore the relationships between training, skills, perceived effectiveness, and trust. Contribution This study provides insights into how training influences trust through skill-building and perceived effectiveness. It contributes to better training programs’ design and fosters trust in AI-driven cybersecurity solutions. Findings The study finds that training enhances skills, which in turn affects perceived effectiveness and trust, though highly skilled individuals may develop skepticism toward AI systems. Training significantly enhances AI-related skills. However, its impact on trust is indirect, as it primarily improves skills, which in turn influences perceived effectiveness and trust. Trust is mediated by improved perceptions of effectiveness driven by skill development. Advanced skills may paradoxically reduce trust due to increased awareness of system limitations. Recommendations for Practitioners Training programs should integrate hands-on experiences and explainable AI techniques to balance skill-building with trust-enhancing strategies. Recommendations for Researchers Further investigation is needed into the trust-skills paradox and the cultural or contextual factors influencing trust in AI systems. Impact on Society Enhancing trust in AI-based cybersecurity systems promotes broader adoption, contributing to improved cybersecurity resilience. Future Research Future studies should focus on objective performance assessments, diverse user groups, and cultural factors affecting trust dynamics

Authors

Institutions

Publication Details

Journal
Informing Science and IT Education Conference
Published
2025-01-01
DOI
https://doi.org/10.28945/5576
Primary Topic
Information and Cyber Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding the Role of Training for Trust in AI-Based Cybersecurity

Ruti Gafni, Itzhak Aviv
Informing Science and IT Education Conference
Information and Cyber Security
article

Understanding the Role of Training for Trust in AI-Based Cybersecurity

Ruti Gafni, Itzhak Aviv
article en

Abstract

Aim/Purpose This paper investigates the impact of training on trust in AI-based cybersecurity solutions, addressing challenges related to skills development and trust dynamics. Background Implementing AI in cybersecurity has proven effective by enhancing threat identification, management, and prevention capabilities. Proper training and education facilitate comprehension of AI solutions and concepts, assisting cybersecurity professionals in utilizing the technology. Experts use AI-based cybersecurity systems based on their trust in these systems. Methodology A structured survey was conducted with 100 cybersecurity experts. Data were analyzed using multiple regression and structural equation modeling to explore the relationships between training, skills, perceived effectiveness, and trust. Contribution This study provides insights into how training influences trust through skill-building and perceived effectiveness. It contributes to better training programs’ design and fosters trust in AI-driven cybersecurity solutions. Findings The study finds that training enhances skills, which in turn affects perceived effectiveness and trust, though highly skilled individuals may develop skepticism toward AI systems. Training significantly enhances AI-related skills. However, its impact on trust is indirect, as it primarily improves skills, which in turn influences perceived effectiveness and trust. Trust is mediated by improved perceptions of effectiveness driven by skill development. Advanced skills may paradoxically reduce trust due to increased awareness of system limitations. Recommendations for Practitioners Training programs should integrate hands-on experiences and explainable AI techniques to balance skill-building with trust-enhancing strategies. Recommendations for Researchers Further investigation is needed into the trust-skills paradox and the cultural or contextual factors influencing trust in AI systems. Impact on Society Enhancing trust in AI-based cybersecurity systems promotes broader adoption, contributing to improved cybersecurity resilience. Future Research Future studies should focus on objective performance assessments, diverse user groups, and cultural factors affecting trust dynamics

Informing Science and IT Education Conference
Academic College of Tel Aviv-Yafo (IL)
Openalex Percentile: Top 52%
Information and Cyber Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.