Many AI incidents begin with human behavior, not advanced exploitation.
Employees paste confidential data into public models to “move faster.” Teams trust AI-generated outputs without verification. Managers treat fluent responses as evidence of correctness. This is the illusion of intelligence problem: language quality gets confused with operational reliability.
In security terms, that illusion creates a new social-engineering surface. Attackers no longer need only to trick people; they can trick people through AI systems that users already trust. Internal overreliance then amplifies the damage by turning one bad output into many downstream decisions.
The answer is not “ban AI.” It is disciplined usage: clear data handling rules, role-based model access, mandatory verification for high-impact outputs, and training that focuses on judgment under time pressure, not generic awareness slides.
AI safety is socio-technical by definition. If your control framework assumes perfect user behavior, it is already broken. Do your people know when AI is a tool, when it is a risk multiplier, and when to stop and escalate?