Human-AI complementarity is a work model in which AI assists people rather than replacing them. In cyber security, this means machines handle scale and speed—such as sorting alerts, clustering logs, summarizing threat intelligence, or drafting detection rules—while humans provide context, judgment, and accountability. The goal is not to automate every decision, but to combine pattern recognition from AI with the analyst’s understanding of business risk, attacker intent, and operational constraints.
This matters because security work is full of noisy data and ambiguous signals. Overreliance on AI can produce confident but shallow conclusions, while human review can catch false positives, missing context, and adversarial manipulation. In real defenses, complementarity appears in SOC workflows, incident response, and phishing analysis, where AI accelerates triage and humans decide escalation, containment, and recovery. It also helps explain attacks: defenders must assume criminals may use AI to scale phishing or recon, so human oversight remains essential when evaluating model outputs and unusual behavior.