Gender bias is a systematic skew that treats people differently based on gender or assigns them unequal roles, traits, or value. In cyber security, it matters because security tools and AI assistants increasingly help with hiring, access decisions, content moderation, user support, and threat analysis. If those systems learn biased patterns from training data or policy rules, they may recommend different actions, produce uneven explanations, or misclassify people in ways that affect trust, safety, and opportunity.
In real attacks, gender bias can be exploited in social engineering, phishing, and disinformation by generating more convincing messages that match stereotypes about authority, care, or work roles. In defenses, biased models can weaken detection or response by treating some language, job titles, or user behaviors as more suspicious than others. Because the problem often hides behind average accuracy, teams should test outputs across groups, review prompts and data sources, and monitor model behavior over time.