Human–AI Collaboration in Security Operations: Measuring Alert Trust, Automation Bias, and Analyst Upskilling in AI-Augmented SOC Environments
Year: 2025
Type: article
Abstract: The rapid integration of artificial intelligence (AI) in Security Operations Centers (SOCs) has created both opportunities and challenges for cybersecurity teams. This paper explores the effects of the levels of AI automation on trust of the analysts, decision-making quality, and skill development. The study delves into three main areas: alert trust, automation bias, and skill adaptation. Using an experimental SOC simulator, participants had to use AI detection tools that generated alerts with varied prediction accuracy to examine the effects on trust and performance. Findings suggest that moderate automation promotes healthy trust and improved decision-making accuracy, while high automation could induce excessive reliance and less alertness. Feedback also aids independent detection skill and confidence in analysts. These studies illustrate the imperative to design trust-sensitive adaptive training systems that can allow for better analyst-AI collaboration in cybersecurity.
Author: Alex Mathew
Language: English
Subfield: Human Factors and Ergonomics
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4415487189