Phishing Simulation Failure Rate

Goal of the analysis:

Measure how often users fail phishing simulations (e.g., click, credential submission, macro enablement), how quickly they report suspicious messages, and which cohorts are most susceptible. Phishing Simulation Failure Rate (PSFR) is a leading indicator of identity risk and security culture. Executives use it to target awareness investments, calibrate technical controls, satisfy regulatory expectations, and reduce real-world incident likelihood. The goal is a low, steadily improving failure rate (especially for high-severity actions like credential submission), high report rates, fast time-to-report, and shrinking repeat-offender cohorts—without training fatigue.

Data required:

• Simulation campaign data:
  • Campaign ID, send window, lure theme and difficulty, payload type (link-only, credential harvest, attachment/macro, QR code, SMS/voice), and target cohort size.
  • User-level outcomes: delivered, opened, clicked, submitted credentials, enabled macro, reported (via phishing button/forward), ignored.
  • Timestamps (send, first open/click/report) for latency analysis; device/channel (desktop, mobile).
• User and organizational context:
  • Directory attributes: employee/contractor/guest, department/role, region/site, manager, tenure, privileged/admin flags.
  • High-risk cohorts: finance/payments, executives/EAs, IT admins, support agents, third parties.
• Email and security telemetry (context and validation):
  • Secure email gateway logs (delivered, quarantined, user-released); phishing button telemetry; SIEM correlation for duplicates.
  • Block lists/allow lists and mail flow rules that may affect delivery.
• Training and readiness data:
  • Security awareness training completion dates, course types, quiz scores, coaching sessions.
  • History of prior simulation outcomes per user (repeat-offender tracking).
• Policy and exceptions:
  • Campaign exclusions (legal, unions, markets with consent limits), privacy rules, and disciplinary policy thresholds.
• Systems and sources:
  • Phishing simulation platforms (Microsoft Attack Simulation Training, Proofpoint, Cofense, KnowBe4), email security (Microsoft Defender for Office 365, Proofpoint), SIEM/SOAR, HRIS/IdP, LMS, BI/warehouse.

Detailed step-by-step instruction on how to conduct the analysis:

1. Define metrics, severity, and scope:
   • Primary PSFR = Users who failed ÷ Users who received the simulation × 100%.
   • Define failure hierarchy (most severe to least): credential submission, macro enablement/attachment execution, link click. Treat report-without-click as success.
   • Report Rate = Users who reported via sanctioned channel ÷ Delivered × 100%.
   • Median Time-to-Click and Time-to-Report from send time; Repeat-Offender Rate = % users failing ≥2 of last N campaigns.
2. Extract campaign and outcome data:
   • Export user-level results for the last 12–18 months; include timestamps and payload metadata.
   • Pull email gateway delivery logs to confirm in-scope deliveries (exclude bounces/quarantined).
3. Cleanse and normalize:
   • Standardize identities (UPN), map to HR attributes and privileged flags; remove service/test accounts.
   • Deduplicate events (multiple clicks = one failure); unify outcome definitions across platforms.
4. Compute core metrics per campaign and rolling period:
   • Overall PSFR and by failure type (click/submit/macro); Report Rate; Median Time-to-Click/Report.
   • Weighted PSFR (severity-weighted) = (5 × submitted + 3 × macro + 1 × click) ÷ Delivered × 100%.
   • Coverage: % workforce included in last quarter; % high-risk cohorts covered.
5. Segment and compare:
   • By department/role (finance, engineering, sales), region/site, tenure band, identity type (employee/contractor/guest), privileged vs standard.
   • By payload theme and difficulty (brand spoof, QR code, voice/SMS, invoice, executive impersonation).
   • By device/channel (mobile vs desktop) where available.
6. Trend and cohort analysis:
   • Plot monthly PSFR and Report Rate with 3-month rolling averages; annotate training rollouts and control changes (e.g., number matching).
   • User cohorts: first-time participants vs repeaters; track improvement after coaching/training completion.
7. Root-cause diagnostics:
   • Pareto by theme and cohort driving most failures; analyze top 10 lures with highest severity-weighted failures.
   • Correlate failures with training status (overdue vs completed), tenure, region language fit, and after-hours timing.
8. Effectiveness of interventions:
   • Compare PSFR before/after targeted coaching, policy changes (MFA hardening), and UX nudges (phish-report button placement).
   • Measure sustained change in repeat offenders 30/60/90 days post-intervention.
9. Risk lens:
   • Focus on credential-submission and macro enablement among privileged users and finance/payment roles.
   • Estimate potential incident reduction: change in severity-weighted PSFR × exposure of high-risk cohorts.
10. Validate and institutionalize:
    • Review segmentation and privacy guardrails with HR/Legal; ensure opt-outs and consent rules are respected.
    • Automate monthly dashboards; set alerts for campaigns exceeding thresholds or spikes in privileged failures.

Format of the output of analysis:

• Executive summary: latest PSFR (overall and severity-weighted), Report Rate, Time-to-Report, repeat-offender trends, top risky cohorts, and actions.
• Campaign scorecards: PSFR by failure type, Report Rate, latency metrics, theme/difficulty, cohort breakdown.
• Heat maps: PSFR by department/region/role and by payload theme.
• Pareto charts: top cohorts and lures contributing to 80% of severe failures.
• Trend charts: monthly PSFR and Report Rate with intervention annotations.
• Cohort panels: improvement of coached users vs control; privileged cohort spotlight.
• Detail table: user (or anonymized ID), cohort attributes, outcome, severity, time-to-click/report, training status, coaching action and date.

How to interpret results:

• Low PSFR with high Report Rate and fast Time-to-Report indicates strong security culture; validate that difficulty remains realistic and coverage is broad.
• High click but low credential submission often reflects early-stage awareness; focus on reporting and MFA resilience while continuing training.
• Concentration in specific cohorts (e.g., new hires, certain regions, contractors) suggests targeted coaching, localized content, or process gaps.
• Privileged user failures or credential submissions warrant immediate action (policy review, additional controls) regardless of aggregate PSFR.
• Beware gaming: repeated very-easy lures can artificially deflate PSFR; maintain a balanced difficulty mix aligned to current threat tactics.
• Trend matters: sustained quarter-over-quarter PSFR reduction and rising Report Rate show real improvement; temporary spikes around complex lures are informative, not necessarily negative.

Steps a company can take to improve on this measure:

• Program design and content:
  • Run continuous, varied simulations (email, QR, SMS/voice) aligned to current threats; include executive impersonation and business email compromise (BEC) scenarios.
  • Localize language and context; rotate difficulty; avoid predictability (timing, sender).
• Targeted coaching and enablement:
  • Deliver just-in-time microtraining upon failure; assign short mandatory coaching for credential submission or privileged users.
  • New-hire and high-risk cohort boosters within first 30 days; refresher cadence every 6–12 months.
• User experience and reporting:
  • Deploy a one-click report button across clients and mobile; advertise its use; measure and recognize reporters.
  • Provide rapid feedback loops (“You reported a simulation—well done!” or “This was a test—here’s what to look for”).
• Technical controls and guardrails:
  • Enforce MFA (phishing-resistant for admins), number matching/push protection; block legacy protocols.
  • Harden email controls: DMARC/DKIM/SPF, advanced phishing protections, URL detonation/safe links, attachment sandboxing.
  • Protect risky flows: disable auto-forwarding to external domains; limit OAuth consent; monitor QR-based exfil vectors.
• Governance and metrics:
  • Set thresholds (e.g., severity-weighted PSFR <2%; credential submission <0.5%; Report Rate ≥25–40%); review monthly at security council.
  • Track repeat offenders; escalate to manager coaching only after supportive interventions; avoid punitive culture.
• Targeted actions by signal:
  • If credential submissions spike: deploy immediate awareness comms, add step-up authentication for risky sign-ins, and run follow-up targeted drills.
  • If report rates are low: improve button availability, run “Report-to-Win” campaigns, and spotlight model reporters.
  • If a region underperforms: localize content, adjust timing to business hours, and partner with regional leadership.

Benchmark comparisons:

General benchmarks:

• Click Failure Rate: 2–5% in mature programs; 8–15% in earlier stages or with difficult lures.
• Credential Submission Rate: ≤0.5–1.0% mature; 2–5% early-stage or high-difficulty campaigns.
• Report Rate (via sanctioned button): 20–40% mature; aim for ≥30% on routine campaigns.
• Time-to-Report (median): ≤15 minutes on operational cohorts; faster is better for containment.
• Repeat-Offender Rate: ≤1–2% after targeted coaching programs.

Segment- or industry-specific benchmarks:

• Financial services/healthcare (regulated): lower targets (click ≤2–3%, credential ≤0.5%) with strong report culture (≥35%).
• Digital-native/tech: generally low failure rates but sophisticated lures (OAuth consent, GitHub tokens) may raise credential risk—focus on technical controls and developer training.
• Retail/field-heavy workforces: slightly higher click rates; prioritize mobile-friendly training and report tooling.

If external benchmarks vary by tooling and lure mix, construct internal benchmarks: track PSFR (overall and severity-weighted), Report Rate, Time-to-Report, and repeat-offender share by cohort over 4–8 quarters. Set targets at internal top quartile per cohort and maintain guardrails (coverage of high-risk groups, realistic difficulty mix) to ensure improvements represent genuine resilience.