The impact of prompt clarity, accuracy, and context on Generative AI performance in cybersecurity operations

As Generative AI systems become integrated into specialized domains such as cybersecurity, the clarity, contextual richness, and accuracy of user interaction prompts for Generative AI virtual assistant chatbots are becoming increasingly critical factors that influence performance, accuracy, usability, and usefulness of the technology. This paper investigates the impact of user prompt ambiguity - which is defined as the degree of contextual manipulation and accuracy - on the efficacy of Generative AI technologies for cyber defensive operations in a live security setting. Through a series of controlled, emulated experiments, we evaluate how variations in prompt clarity affect accuracy, consistency, and usability for threat detection and incident response. Our findings reveal a correlation between prompt clarity and performance, particularly in technically dense scenarios that typify live cybersecurity operations. These findings provide guidance for effective security analyst usage of Generative AI in live security operations. This work contributes to the growing body of research on Generative AI interaction dynamics, prompt design and construction, and the operational deployment of Generative AI in highly technical, specialized domains.