Digital twins, virtual replicas that learn and evolve in real time, are giving security teams a way to see threats before they strike. For the first time, organizations can stage tomorrow’s attacks today, turning defense from a reaction into a rehearsal. Instead of waiting for a zero-day exploit to spread through production systems, organizations can use their twin to anticipate how an attack might unfold and block it before it becomes a problem. In short, digital twins give defenders foresight in a domain long defined by hindsight. Analysts describe this new approach as a “cyber sandbox,” but one operating at the same scale and fidelity as the production environment. Inside this mirrored environment, teams can stage ransomware attacks, phishing waves and insider threats. Before rolling out a new SaaS integration or shifting workloads into a multicloud environment, teams can rehearse the move inside their twin. If misconfigurations, privilege escalations or API blind spots emerge, they are patched in the model before they exist in production. This approach transforms change management from a gamble into a calculated maneuver, tightening resilience without slowing innovation. Startups are combining AI-driven attack generation with digital twins, producing probability maps that indicate the likelihood of future threats succeeding. In effect, these are predictive laboratories where attackers’ moves can be anticipated, not just countered.