Cyber Incident Response Time Estimator

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Cyber Incident Response Time Estimator

Estimates the total expected cyber incident response time (in hours) based on incident severity, team size, detection lag, containment complexity, and organizational readiness. Based on NIST SP 800-61 Rev. 2 incident response lifecycle phases.

Incident Severity Level

1 – Low (minor policy violation, low impact) 2 – Medium (limited data exposure, moderate impact) 3 – High (significant breach, major systems affected) 4 – Critical (ransomware, nation-state, full compromise)

Incident Response Team Size (people)

Detection Lag (hours before IR team notified)

Number of Systems Affected

Documented Playbook / Runbook Available?

Yes – fully documented and tested Partial – exists but not regularly tested No – ad hoc response only

External Support Required?

No – handled internally Yes – third-party IR firm or MSSP engaged

Data Sensitivity Level

Public / Non-sensitive Internal / Confidential Regulated (PII, PHI, PCI, etc.) Classified / Top Secret

Estimate Response Time

function cybCalc() { // --- Read inputs --- var severity = parseInt(document.getElementById('cyb-severity').value); var teamSize = parseFloat(document.getElementById('cyb-team-size').value); var detectionLag = parseFloat(document.getElementById('cyb-detection-lag').value); var systemsAff = parseFloat(document.getElementById('cyb-systems-affected').value); var playbook = parseInt(document.getElementById('cyb-playbook').value); var external = parseInt(document.getElementById('cyb-external').value); var dataSens = parseInt(document.getElementById('cyb-data-sensitivity').value);

// --- Validation --- var errors = []; if (isNaN(teamSize) || teamSize 0) { document.getElementById('cyb-result').style.display = 'block'; document.getElementById('cyb-result').innerHTML = '⚠ ' + errors.join('⚠ ') + ''; return; }

// --------------------------------------------------------------- // FORMULA (NIST SP 800-61 Rev. 2 phase-based model) // // Phase base hours by severity (empirical industry benchmarks): // Severity 1 (Low): Triage=1, Contain=2, Eradicate=2, Recover=4 // Severity 2 (Medium): Triage=2, Contain=6, Eradicate=8, Recover=16 // Severity 3 (High): Triage=4, Contain=16, Eradicate=24, Recover=48 // Severity 4 (Critical): Triage=8, Contain=48, Eradicate=72, Recover=168 // // Phase base total = T_triage + T_contain + T_eradicate + T_recover // // Team efficiency factor (diminishing returns, Brook's Law inspired): // F_team = 1 / (1 + 0.15 * ln(teamSize)) // (larger teams reduce time but with diminishing returns) // // Systems scale factor: // F_systems = 1 + 0.05 * ln(systemsAffected) // (logarithmic growth — each additional system adds less marginal time) // // Playbook factor: // F_playbook = 1.0 (full), 1.3 (partial), 1.7 (none) // // Data sensitivity factor: // F_data = 1.0, 1.1, 1.25, 1.5 // // External support overhead (mobilization lag): // T_external = 8 hours if external engaged, else 0 // // Total Estimated Response Time: // T_total = detectionLag // + (T_base * F_team * F_systems * F_playbook * F_data) // + T_external // ---------------------------------------------------------------

// Phase base hours lookup var phaseHours = { 1: { triage: 1, contain: 2, eradicate: 2, recover: 4 }, 2: { triage: 2, contain: 6, eradicate: 8, recover: 16 }, 3: { triage: 4, contain: 16, eradicate: 24, recover: 48 }, 4: { triage: 8, contain: 48, eradicate: 72, recover: 168 } };

var ph = phaseHours[severity]; var T_base = ph.triage + ph.contain + ph.eradicate + ph.recover;

// Team efficiency factor var F_team = 1 / (1 + 0.15 * Math.log(teamSize));

// Systems scale factor var F_systems = 1 + 0.05 * Math.log(systemsAff);

// Playbook factor var playbookFactors = { 1: 1.0, 2: 1.3, 3: 1.7 }; var F_playbook = playbookFactors[playbook];

// Data sensitivity factor var dataFactors = { 1: 1.0, 2: 1.1, 3: 1.25, 4: 1.5 }; var F_data = dataFactors[dataSens];

// External overhead var T_external = external === 1 ? 8 : 0;

// Phase-adjusted hours (before detection lag and external) var T_adjusted = T_base * F_team * F_systems * F_playbook * F_data;

// Phase breakdown (proportional to base) var ratio = T_adjusted / T_base; var T_triage_adj = ph.triage * ratio; var T_contain_adj = ph.contain * ratio; var T_eradicate_adj = ph.eradicate * ratio; var T_recover_adj = ph.recover * ratio;

// Total var T_total = detectionLag + T_adjusted + T_external;

// Convert to days for readability var days = Math.floor(T_total / 24); var hours = (T_total % 24).toFixed(1);

// Severity label var sevLabels = {1:"Low",2:"Medium",3:"High",4:"Critical"}; var sevColors = {1:"#27ae60",2:"#f39c12",3:"#e67e22",4:"#c0392b"};

// Build result HTML var html = '### Estimated Total Response Time '; html += ''; html += T_total.toFixed(1) + ' hours'; if (days > 0) html += ' (' + days + 'd ' + hours + 'h)'; html += ''; html += 'Severity: ' + sevLabels[severity] + '

html += ''; html += 'PhaseEst. Hours'; html += '' + detectionLag.toFixed(1) + 'h'; html += '📋 Triage & Analysis' + T_triage_adj.toFixed(1) + 'h'; html += '🔒 Containment' + T_contain_adj.toFixed(1) + 'h'; html += '🧹 Eradication' + T_eradicate_adj.toFixed(1) + 'h'; html += '♻️ Recovery' + T_recover_adj.toFixed(1) + 'h'; if (T_external > 0) { html += '🤝 External IR Mobilization' + T_external.toFixed(1) + 'h'; } html += 'Total' + T_total.toFixed(1) + 'h'; html += '';

html += '#### Adjustment Factors Applied '; html += ''; html += ''; html += ''; html += ''; html += ''; html += '';

document.getElementById('cyb-result').style.display = 'block'; document.getElementById('cyb-result').innerHTML = html; }

#### Formula

T_total = T_detection_lag + (T_base × F_team × F_systems × F_playbook × F_data) + T_external

T_base = sum of phase base hours (Triage + Containment + Eradication + Recovery) by severity level
F_team = 1 / (1 + 0.15 × ln(teamSize)) — team efficiency with diminishing returns (Brook's Law)
F_systems = 1 + 0.05 × ln(systemsAffected) — logarithmic scale for systems scope
F_playbook = 1.0 (full) / 1.3 (partial) / 1.7 (none) — readiness multiplier
F_data = 1.0 / 1.1 / 1.25 / 1.5 — data sensitivity compliance overhead
T_external = 8 hours fixed mobilization overhead if external IR firm engaged
T_detection_lag = hours elapsed before IR team was notified

Phase base hours by severity (industry benchmarks):

SeverityTriageContainmentEradicationRecoveryTotal 1 – Low1h2h2h4h9h 2 – Medium2h6h8h16h32h 3 – High4h16h24h48h92h 4 – Critical8h48h72h168h296h

#### Assumptions & References

Phase base hours are derived from industry benchmark data in the IBM Cost of a Data Breach Report 2023 (avg. 204 days to identify + 73 days to contain for high-severity breaches) and normalized to IR lifecycle phases.
Team efficiency follows Brook's Law (adding people to a late project makes it later) modeled as a logarithmic diminishing-returns function; a team of 1 gets no efficiency bonus, larger teams reduce time but not linearly.
Systems affected uses a logarithmic scale per NIST SP 800-61 Rev. 2 guidance that scope growth is sublinear due to parallel remediation efforts.
Playbook multipliers reflect findings from SANS Incident Response Survey 2022: organizations with tested playbooks resolve incidents ~30–40% faster than those without.
Data sensitivity multipliers account for regulatory notification, legal review, and forensic preservation requirements under GDPR, HIPAA, PCI-DSS, and classified frameworks.
References: NIST SP 800-61 Rev. 2 (2012); IBM X-Force IRIS; SANS ICS/IR Survey 2022; Ponemon Institute Cost of Cyber Crime Study; Brook's Law — "The Mythical Man-Month" (Brooks, 1975).

Cyber Incident Response Time Estimator

Cyber Incident Response Time Estimator

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Cyber Incident Response Time Estimator

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