Phishing Risk Exposure Calculator
Estimates your organization's annual financial exposure from phishing attacks using industry-standard risk modeling: threat frequency, vulnerability rates, and control effectiveness.
Formula
Step 1 — Total Emails Sent:
Total_Emails = Employees × Emails_Per_Employee_Per_Year
Step 2 — Emails Delivered (after filtering):
Emails_Delivered = Total_Emails × (1 − Email_Filter_Rate)
Step 3 — Adjusted Click Rate (after training):
Adjusted_Click_Rate = Baseline_Click_Rate × (1 − Training_Reduction)
Step 4 — Expected Clicks:
Clicks = Emails_Delivered × Adjusted_Click_Rate
Step 5 — Effective Compromise Rate (MFA-adjusted):
Effective_Compromise_Rate = Compromise_Rate × [(1 − MFA_Adoption) + MFA_Adoption × 0.05]
MFA is modeled as 95% effective for covered users (residual factor = 0.05).
Step 6 — Expected Incidents:
Incidents = Clicks × Effective_Compromise_Rate
Step 7 — Annual Loss Expectancy (ALE):
ALE = Incidents × Cost_Per_Incident
This follows the FAIR (Factor Analysis of Information Risk) model:
Risk = Threat_Event_Frequency × Vulnerability × Loss_Magnitude
Assumptions & References
- Baseline phishing click rate of ~14% is the industry average for untrained employees (Proofpoint State of the Phish, 2023).
- MFA is modeled as 95% effective at preventing credential compromise for enrolled users, consistent with Microsoft research showing MFA blocks 99.9% of automated attacks; a 5% residual accounts for MFA bypass techniques (SIM swapping, adversary-in-the-middle).
- Average phishing incident cost of $17,700 is derived from IBM Cost of a Data Breach Report 2023 and Ponemon Institute phishing cost studies.
- Email filtering catch rates of 85–99% are typical for enterprise-grade secure email gateways (Gartner, 2023).
- Security awareness training can reduce click rates by 50–75% when conducted regularly (SANS Security Awareness Report, 2023).
- Incident costs include: IT investigation and remediation, user downtime, legal and regulatory fees, customer notification, and reputational damage estimates.
- This model uses expected value (mean) estimation; actual losses follow a heavy-tailed distribution — tail risk may significantly exceed ALE.
- Model follows NIST SP 800-30 (Risk Assessment Guide) and the FAIR quantitative risk framework.