Cyber Risk Financial Impact Simulator
A cross-disciplinary risk tool that estimates breach economics through company profile, attack type, severity, and uncertainty. The page is designed to show how cyber incidents translate into a financial range rather than a single dramatic headline number.
Model Loss, Distribution, Control Lift
Build a cyber-loss scenario, then compare the deterministic estimate with the Monte Carlo distribution. The important question is not just “what could this cost?” but “how much do posture and incident response change the loss range?”
What This Models
A cyber-loss framework combining sector loss baselines, attack-type severity, company scale, security maturity, and recovery uncertainty.
How To Use It
Start with a company profile, set the attack characteristics, then compare the base-case loss estimate with the higher-percentile outcomes in the simulation tab.
How To Read The Output
The key insight is how quickly the distribution widens when security maturity is weak. This is a loss-range tool, not a single-point prediction engine.
Methodology
- Base loss comes from sector and attack-type assumptions scaled by company size and severity.
- Security maturity compresses or widens the loss profile by changing resilience and recovery efficiency.
- The Monte Carlo layer introduces uncertainty around company-specific, severity, industry, and recovery conditions.
Limits
- This is a planning model, not a full cyber-actuarial platform.
- Insurance structure, legal venue, and incident-response execution are simplified.
- The correct reading is directional loss exposure and control value, not exact breach-cost prediction.