Building organizational trust in generative AI in female-led cybersecurity startups: dual pathways of ethical responsibility and privacy assurance

Purpose This study examines how organizational trust in generative AI is built in high-stakes entrepreneurial settings, particularly female-led cybersecurity startups, through ethical responsibility and privacy assurance as complementary governance pathways. Design/methodology/approach Survey data were collected from 288 mid- and senior-level managers in female-led cybersecurity startups across North America and Europe. The model was estimated using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 3.2.8 with 5,000 bootstrap subsamples. Findings Responsible entrepreneurial leadership and cybersecurity innovation practices both positively predict organizational trust in generative AI. Perceived ethical responsibility and perceived privacy assurance carry significant indirect effects, respectively. The conditional indirect effects increase across higher levels of gender diversity climate and regulatory environment, and both indices of moderated mediation have 95% BCa confidence intervals that exclude zero. Research limitations/implications The study relies on cross-sectional perceptual data from a specific organizational context. Future research should test the model longitudinally, incorporate objective governance indicators and examine broader organizational and institutional settings. Practical implications Organizations can strengthen trust in generative AI by institutionalizing ethical responsibility and developing credible privacy assurance practices. Supportive diversity climates and clear regulatory conditions further enhance the effectiveness of these trust-building mechanisms. Social implications The findings highlight the importance of responsible and privacy-protective AI governance in high-stakes domains, where trustworthy deployment can reduce misuse concerns and strengthen stakeholder confidence. Originality/value The study develops and tests a dual-pathway moderated mediation model of organizational trust in generative AI, showing how ethical responsibility and privacy assurance jointly shape trust under specific organizational and regulatory conditions.