Trust and Resistance Toward AI-Based Threat Detection Tools Among Security Analysis

Abstract

Artificial intelligence (AI) is defined as a replication of human intelligence in machines that can be used to execute functions like learning, problem-solving and decision-making. Threat detection systems in cybersecurity AI models are created to work with high amounts of network traffic to identify other patterns and outliers and enable organizational security in the face of planned cyberattacks. The present research explores the credibility and mistrust of security analysts with regard to AI threat detection systems with reference to their impact on adoption and effective use at the operational setting. The paper used  a qualitative research method  involving the use of document and content analysis of secondary sources to understand the perceptions of the analysts, the collaboration between humans and AI, and organizational aspects that precondition the attitude to AI technologies.The analysis shows  the factors that significantly affected trust are system transparency, explainability, accuracy and reliability whereas the factors that contributed to resistance are the high false-positive rates, non-explanability and the fear of the diminishing role of human. More so, human-focused system design, organizational support, training were also found to be important in terms of increasing adoption and reducing resistance. It is emphasized that the key to successful AI application in cybersecurity lies in the equilibrium between technological performance and human and organizational needs, enabling to ensure that the application of AI analyzing devices will be effective and will not transfer control over decision-making to AI. The research will offer cybersecurity personnel, organizations, and researchers lessons on how to design, implement, and manage AI-driven cybersecurity systems that are trusted, broadly adopted, and operational.