AI-Supported Dynamic Cyber Risk Assessment for Cyber Situational Awareness: A Cross-Sectional Survey

Abstract

Small and medium-sized enterprises (SMEs) have limited resources and governance that might restrict their ability to conduct dynamic cyber risk assessment (DCRA) and maintain effective cyber situational awareness (CSA). This study investigates stakeholders’ perceptions of CSA, DCRA, and AI-enabled cybersecurity to develop a conceptual framework targeted for SMEs. The online survey was cross-sectional, and 302 completed responses were gathered. The valid sample size for the items ranged from 288 to 299. Out of 293 respondents, 54 (18.4%) indicated prior usage of CSA techniques, 21 (7.2%) reported prior use of DCRA tools, and 226 (77.1%) backed AI in the cybersecurity field. The highest rated DCRA requirements were continuous threat updates, identification of attacks and vulnerabilities, and prioritization of alerts based on risk. The highest rated implementation challenges were accuracy, relevance, and integration with current infrastructure. Four multi-item measures had good-to-outstanding internal consistency (α = 0.868–0.926; ω = 0.870–0.929), and parallel analysis supported a single factor for each. Exploratory findings suggested that Information Technology (IT) and cybersecurity professionals had greater familiarity with CSA and DCRA than did leaders and managers. There was a moderate-to-strong positive association between familiarity with CSA and DCRA (ρ = 54). The framework defines AI as a layer of analytical decision support, DCRA as the process of translating changing evidence into updated and prioritized risk information, and CSA as decision-relevant interpretation and use of that information. This framework will help SMEs to improve CSA and will help their leaders to make the right decisions when dealing with cyber threats.