Loss Exceedance Curve
Interactive Monte Carlo simulator (lognormalA distribution where the log of values is normally distributed, common for loss sizes., 90% bounds).
Chart summary updates after you run the simulation.
Each run draws new random years; expect small changes. Round when you brief.
How to use
- Enter assumptions on the right. Use formats like 9%, 0.09, 3M, or 3 million.
- The curve updates live. Dots mark exceedance thresholds, and the darker dot marks your materiality threshold.
- Net event probability = Outside-In minus Inside-Out, clamped to 0-1.
- Lognormal parameters: mu = (ln(UB) + ln(LB)) / 2, sigma = (ln(UB) - ln(LB)) / 3.29. The 3.29 divisor treats your low/high bounds as a 90% confidence interval (3.29 ≈ 2 × 1.645).
- Expected annual loss (EAL) is the average loss across all simulated years — the event probability times the average loss when an event occurs.
Learning Debrief
What You Just Learned
- Translate probability and loss ranges into a curve you can explain.
- Use percentiles to communicate expected loss ranges.
- Identify the probability of crossing a material loss threshold.
- Compare scenarios by changing inputs and watching the curve shift.
Applying This to Cyber Risk
Loss exceedance curves help connect controls to dollars and decisions.
Control Investment Tradeoffs
Model how an added control shifts the curve and lowers the chance of exceeding a board-defined loss threshold.
Scenario Comparison
Compare ransomware, third-party outage, and insider scenarios using the same materiality threshold.