Phishing Risk Exposure Calculator

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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.

Total Employees

Phishing Emails Received Per Employee Per Year

Baseline Employee Click Rate (%)

Industry average without training: ~14%. With training: ~2–5%.

Credential Compromise Rate per Click (%)

Percentage of clicks that result in credential theft or malware installation.

Average Cost Per Phishing Incident (USD)

Includes investigation, remediation, downtime, legal, and reputational costs. IBM avg: ~$17,700/incident.

MFA Adoption Rate (%)

MFA reduces credential-based compromise risk by ~99.9%; modeled here as 95% effectiveness on covered users.

Security Awareness Training Click-Rate Reduction (%)

Effective training reduces click rates by 50–75%. Enter 0 if no training program exists.

Email Filtering Catch Rate (%)

Percentage of phishing emails blocked before reaching employees. Enterprise filters: 85–99%.

Calculate Phishing Risk Exposure

function phiCalc() { // --- Collect inputs --- const employees = parseFloat(document.getElementById('phi-employees').value); const emailsPerYear = parseFloat(document.getElementById('phi-emails-per-year').value); const clickRatePct = parseFloat(document.getElementById('phi-click-rate').value); const compromiseRatePct= parseFloat(document.getElementById('phi-compromise-rate').value); const incidentCost = parseFloat(document.getElementById('phi-incident-cost').value); const mfaAdoptionPct = parseFloat(document.getElementById('phi-mfa-adoption').value); const trainingRedPct = parseFloat(document.getElementById('phi-training-reduction').value); const filterRatePct = parseFloat(document.getElementById('phi-email-filter').value);

const resultDiv = document.getElementById('phi-result');

// --- Validation --- const fields = [ [employees, 'Total Employees', 1, 1000000], [emailsPerYear, 'Phishing Emails Per Employee Per Year', 1, 10000 ], [clickRatePct, 'Baseline Click Rate', 0.1, 100 ], [compromiseRatePct, 'Credential Compromise Rate', 0.1, 100 ], [incidentCost, 'Average Cost Per Incident', 100, 1e8 ], [mfaAdoptionPct, 'MFA Adoption Rate', 0, 100 ], [trainingRedPct, 'Training Click-Rate Reduction', 0, 90 ], [filterRatePct, 'Email Filtering Catch Rate', 0, 99 ], ];

for (const [val, name, lo, hi] of fields) { if (isNaN(val)) { resultDiv.style.display = 'block'; resultDiv.innerHTML = '⚠ Please enter a value for: ' + name + '.'; return; } if (val hi) { resultDiv.style.display = 'block'; resultDiv.innerHTML = '⚠ ' + name + ' must be between ' + lo + ' and ' + hi + '.'; return; } }

// --- Core Formula --- // Step 1: Total phishing emails sent to org per year const totalEmailsSent = employees * emailsPerYear;

// Step 2: Emails reaching employees after filtering // Emails_Delivered = Total_Emails × (1 - Filter_Rate) const filterRate = filterRatePct / 100; const emailsDelivered = totalEmailsSent * (1 - filterRate);

// Step 3: Adjusted click rate after security awareness training // Adjusted_Click_Rate = Baseline_Click_Rate × (1 - Training_Reduction) const trainingRed = trainingRedPct / 100; const adjustedClickRate = (clickRatePct / 100) * (1 - trainingRed);

// Step 4: Number of clicks // Clicks = Emails_Delivered × Adjusted_Click_Rate const clicks = emailsDelivered * adjustedClickRate;

// Step 5: MFA effectiveness — reduces compromise probability for covered users // MFA blocks 95% of credential compromises for users with MFA enabled // Effective_Compromise_Rate = Compromise_Rate × [(1 - MFA_Adoption) + MFA_Adoption × 0.05] const mfaAdoption = mfaAdoptionPct / 100; const mfaResidualFactor = (1 - mfaAdoption) + mfaAdoption * 0.05; const effectiveCompromiseRate = (compromiseRatePct / 100) * mfaResidualFactor;

// Step 6: Expected incidents per year // Incidents = Clicks × Effective_Compromise_Rate const incidents = clicks * effectiveCompromiseRate;

// Step 7: Annual Loss Expectancy (ALE) // ALE = Incidents × Cost_Per_Incident const ale = incidents * incidentCost;

// Step 8: Annualized Rate of Occurrence per employee const aroPerEmployee = incidents / employees;

// Step 9: Risk per employee const riskPerEmployee = ale / employees;

// --- Severity classification --- let severity, sevColor; if (ale '$' + v.toLocaleString('en-US', {minimumFractionDigits:0, maximumFractionDigits:0}); const fmtN = v => v.toLocaleString('en-US', {minimumFractionDigits:1, maximumFractionDigits:1}); const fmtP = v => (v * 100).toFixed(2) + '%';

resultDiv.style.display = 'block'; resultDiv.innerHTML = ` ### Phishing Risk Exposure Results

Annual Loss Expectancy (ALE) ${fmt(ale)}

Risk Severity ${severity}

Total Phishing Emails Sent / Year ${Math.round(totalEmailsSent).toLocaleString()}

Emails Delivered (after filtering) ${Math.round(emailsDelivered).toLocaleString()}

Adjusted Employee Click Rate ${fmtP(adjustedClickRate)}

Expected Clicks / Year ${fmtN(clicks)}

Effective Compromise Rate (post-MFA) ${fmtP(effectiveCompromiseRate)}

Expected Incidents / Year ${fmtN(incidents)}

Annualized Rate of Occurrence (per employee) ${aroPerEmployee.toFixed(4)}

Risk Exposure Per Employee ${fmt(riskPerEmployee)}

Interpretation: Your organization faces an estimated ${fmt(ale)} in annual phishing-related losses across approximately ${fmtN(incidents)} incidents per year. ${ale >= 500000 ? ' Immediate investment in layered controls is strongly recommended.' : ' Continue monitoring and improving controls.'}

`; }

#### 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

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