Trust, Risk, and Investment Confidence in Defensive Cyber Deception Adoption

Abstract

Defensive cyber deception technologies use decoys, honeytokens, simulated services, and false artifacts to create high-confidence signals when adversaries interact with resources that legitimate users should not touch. Although these tools are often discussed as technical controls, their adoption also depends on investment confidence: whether practitioners believe the capability is valuable, trustworthy, controllable, and worth organizational resources. This study examines that problem using secondary analysis of an existing deidentified survey dataset collected from industrial control system and operational technology professionals. The analysis tested whether investment confidence, self-efficacy/direct experience, social validation, and instructional support predicted adoption readiness and effective utilization beyond education, experience, and sector. The complete-case sample for the primary regression was 264. Demographics alone explained little variance in adoption readiness, R-squared = 0.026, p = 0.225. Adding perception-based predictors increased explained variance to R-squared = 0.639, p < 0.001. Investment confidence remained a significant predictor in the full model, beta = 0.251, p < 0.001, alongside self-efficacy/direct experience and instructional support. A second model showed that self-efficacy and social validation significantly predicted investment confidence itself. The findings suggest that defensive cyber deception investment is not only a tool-budget issue. It is a trust, capability, and risk-communication issue that must be addressed before organizations can treat deception as a credible security investment.