Forecast bias is a systematic error in prediction, where estimates consistently tilt too optimistic, too pessimistic, or simply too confident. Instead of random noise, the mistake follows a pattern: the model, analyst, or process is using assumptions that push results in one direction. In practice, this often comes from overfitting past trends, ignoring rare events, or treating today’s conditions as if they will continue unchanged.
In cybersecurity, forecast bias matters because defenders use predictions to plan budgets, staffing, threat hunting, and control upgrades. If forecasts are biased low, teams may underprepare for attack volume, cloud abuse, or identity compromise. If they are biased high, organizations may spend on low-probability risks while missing nearer-term gaps. Attackers also benefit from biased forecasts in intelligence and planning, because a defender who expects the wrong threat shape may leave real paths open. Good security forecasting reduces bias by comparing predictions with outcomes, testing assumptions, and updating models as attacker behavior, tooling, and exposure change.