Phishing Susceptibility Test: How to Measure, Compare, and Reduce Human Risk Across Every Channel Safely

Key takeaways

• A phishing susceptibility test measures a chain of behaviors, from delivery and clicks to credential attempts and reports, so a single click rate tells only part of the story.
• Every rate needs a stated numerator, denominator, and exposure unit, and campaign comparisons need confidence intervals and matched conditions.
• Ethical programs disclose testing at the policy level, never collect real credentials, protect individual results, and treat mistakes as coaching opportunities.
• Mature programs extend testing beyond email to vishing, smishing, QR-code phishing, collaboration tools, and deepfake impersonation.
• Reporting speed, repeat-failure reduction, and delayed retention reveal real behavioral change more reliably than training completion alone.

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A phishing susceptibility test measures how employees respond to simulated social engineering attempts. It gives security teams a practical view of risky actions and protective behavior before real cyberattacks cause harm. This guide explains how to design safe email, mobile, voice, QR-code, collaboration, and deepfake scenarios without collecting real credentials or blaming employees.

It also covers how to capture clicks, attachment interactions, credential-page attempts, reports, and response time. It also separates susceptibility from resilience, and a quiz from a realistic phishing simulation. Security leaders will find formulas for susceptibility and reporting rates, plus methods for fair cohort comparisons, confidence ranges, privacy controls, and repeat testing.

Treating employee action as one part of a layered defense, consistent with NIST guidance in the NIST Measurement Guide for Information Security (SP 800-55 Volume 1), recognizes that no single employee determines breach probability. The outcome is a measurement program that turns observed behavior into targeted phishing awareness training, immediate feedback, and evidence of retained skills across changing attack channels. Organizations ready to act on these signals can explore Adaptive Security’s human risk management platform.

What Is a Phishing Susceptibility Test?

A phishing susceptibility test is a controlled assessment of how employees respond to simulated phishing and related social engineering attempts. It measures whether people click links, open attachments, submit credentials, approve unusual requests, pause to verify, or report suspicious messages in a safe environment. The result describes workforce exposure and control effectiveness. It says nothing about an employee’s intelligence, character, or value.

The Purpose of a Phishing Susceptibility Test

A phishing susceptibility test shows how human decisions affect exposure before a real cyberattacker creates financial, operational, or regulatory consequences. Security teams send controlled email phishing test messages. Mature programs add simulated vishing, smishing, business email compromise (BEC), and other social engineering attempts.

The assessment records what happens after delivery, including whether the recipient engages, verifies, reports, or ignores the request.

The point of the assessment is diagnosis, so punishing employees for the result defeats it. A high click rate identifies a decision pattern that needs attention, while a high reporting rate shows that employees are detecting and escalating suspicious activity. These signals help security leaders choose targeted phishing awareness training for employees, stronger verification procedures, or better reporting workflows.

A useful assessment also accounts for exposure before an employee makes a decision. A message using a public job title, a real vendor relationship, or an executive’s communication style tests a different risk than an obviously generic lure.

Cyberattackers use open-source intelligence (OSINT) to personalize spear phishing. A meaningful test should therefore reflect the context employees encounter in daily work. It should do so without collecting unnecessary personal information or creating avoidable distress.

The test measures a system of human decisions, exposure, and controls. Email filtering, multifactor authentication, payment approval rules, reporting tools, and manager verification all affect the outcome.

An employee who spots a suspicious request but has no practical reporting route is operating within a weak control system. An employee who reports quickly and follows a second-channel verification process strengthens the organization’s defensive layer.

Susceptibility Versus Resilience

Susceptibility describes the likelihood that a person or team will take a risky action when confronted with a deceptive request. Common indicators include clicking a link, opening an attachment, entering credentials into a simulated login page, replying with sensitive information, or approving a payment-related instruction. These actions reveal where a cyberattacker could gain momentum.

Resilience describes what happens before, during, and after that risky moment. A resilient employee may question an urgent request, verify the sender through a trusted channel, report the message, remove a suspicious attachment, or alert colleagues. Resilience also includes response speed and recovery behavior.

Consider an employee who clicks a simulated link but immediately reports the message and changes a potentially exposed password. In that case, the organization gains a signal about both the initial weakness and the strength of its recovery process.

This distinction changes how leaders interpret results. A single click should not become a permanent label. Nor does a low click rate show that the organization is safe. Employees face different levels of exposure based on role, access, workload, and communication patterns.

Finance staff receive payment requests, executives attract impersonation attempts, and help desk teams handle credential-related conversations. The same test result can carry different consequences in each context.

A strong methodology compares risky actions with protective actions over time. It asks whether reporting becomes faster, whether employees use approved verification methods, and whether targeted training changes behavior in later assessments.

That approach turns phishing awareness training into practical skill building: employees learn to recognize pressure, authority cues, and unusual requests. Employees learn to recognize pressure, authority cues, and unusual requests, while security leaders gain evidence for improving controls.

A modern phishing simulation program can connect signals across email, voice, and SMS so email behavior is only one part of the picture. That broader view matters because an employee who resists a suspicious email can still face a convincing vishing call or smishing message built around the same fabricated scenario.

How Does a Quiz Differ From a Realistic Phishing Simulation?

A phishing quiz measures recognition in a knowledge-testing environment. It might ask an employee to identify warning signs in a sample message, select the safest response, or define terms such as spoofing and spear phishing. Quizzes establish vocabulary and reinforce phishing awareness training. However, they generally measure what a person says is correct when the decision is clearly presented as a test.

A phishing susceptibility test measures behavior under realistic conditions. An employee might receive an unexpected email phishing test during a normal workday, see a familiar-looking sender name, or face a request that combines urgency with authority. The assessment records the decision made in context, where competing priorities and workplace habits influence the response.

That context does not make the employee responsible for the deception. It makes the exercise more useful for identifying which skills and safeguards need reinforcement.

A one-time phishing test is narrower still. It provides a snapshot from one campaign, one message, and one moment. The result can establish a baseline. It cannot show whether behavior improves, whether a department remains exposed, or whether employees can transfer their judgment to a new attack channel.

Phishing awareness training teaches concepts and responses. It explains how phishing works, why cyberattackers create urgency, and how employees should report suspicious activity. Training becomes more effective when it follows a relevant test and addresses the specific behavior observed. A person who submits credentials needs a different intervention from someone who recognizes the message but fails to report it.

A full phishing simulation program combines repeated assessments, targeted training, reporting workflows, risk analysis, and follow-up measurement. It can test email, vishing, smishing, QR-code phishing, and deepfake-enabled impersonation across appropriate roles.

A perfect score on every exercise is an unrealistic target. The goal is to reduce risky behavior, increase reporting, and strengthen recovery when deception succeeds.

The most credible interpretation avoids a simple verdict that employees passed or failed. A phishing susceptibility test identifies where human judgment, organizational exposure, and technical controls intersect. Security leaders can use that evidence to improve employee phishing awareness training, refine verification procedures, and measure whether the organization is becoming more resilient as cyberattackers move across channels.

What Does a Phishing Susceptibility Test Measure?

A phishing susceptibility test measures how employees respond to a controlled social engineering lure. It does not measure whether they are inherently careless or whether the organization will suffer a breach. The test distinguishes leading indicators, which capture risky or protective behavior before damage occurs, from outcome indicators, which show whether an employee completed a more consequential action.

A click rate measures initial engagement with a lure. Credential-page interaction, attempted submission, and rapid reporting reveal deeper progression toward compromise or defensive recovery.

The most useful test combines these signals with technical context, training completion, and retention data. No single simulation result equals a direct probability of breach.

Which Risky Actions Does a Phishing Susceptibility Test Measure?

A well-designed phishing susceptibility test records a sequence of actions and avoids assigning a pass or fail after one click. Each event shows how far an employee progressed through an attack path and where training should focus.

• Delivery: Delivery confirms whether the simulated message reached the intended inbox, mobile device, or other channel. It establishes the denominator for every later rate and identifies filtering, quarantine, or integration issues unrelated to employee behavior.
• Open rate: An open rate measures whether a message was viewed, but it is reliable only when the test platform has trustworthy telemetry. Privacy proxies, image prefetching, mobile clients, and security scanners can create false opens, so organizations should treat this metric as directional.
• Click rate: Click rate measures whether a recipient selected a link or call to action. It is a leading indicator of susceptibility. It does not prove that the employee trusted the message, entered information, or exposed a credential.
• Attachment interaction: This records whether an employee opened, downloaded, or enabled content associated with a simulated attachment. Those actions represent different risk levels and should be recorded as separate events.
• QR-code scan: A quishing test records whether the recipient scanned a QR code and reached the controlled destination. QR-code phishing moves the decision to a mobile device, so this channel deserves its own measurement, separate from email click data.
• Reply behavior: A reply indicates that the employee engaged directly with the sender. That behavior matters in business email compromise (BEC) scenarios, where the cyberattacker wants a conversation more than an immediate login.
• Credential-page interaction: This measures whether the employee reached a simulated login page, focused on a field, or began entering information. It is a deeper outcome than a click because the user continued along the attack path.
• Attempted submission: An attempted submission records whether the employee tried to send a username, password, one-time code, or other requested data. The platform must use dummy fields and must not collect real credentials.
• Repeat failure: Repeat failure identifies whether the same employee, role, or department repeats a risky action after feedback or training. It is more useful for remediation than a one-time aggregate click rate because it reveals an unresolved behavioral gap.

A click alone does not equal credential submission or compromise. A recipient might click to inspect a suspicious destination, trigger an automated browser safety check, or immediately close the page. Conversely, a recipient can avoid clicking while still replying, forwarding sensitive information through another channel, or complying with a follow-up phone call.

The test therefore needs an event chain and clear severity levels in place of a single headline percentage.

Which Protective Actions Should the Test Measure?

Protective actions are leading indicators of defensive behavior. They show whether employees recognize a suspicious request, interrupt the attack, and create useful information for the security team.

The core protective metric is reporting rate, defined as the percentage of delivered simulations reported through the approved channel, such as a one-click phishing report button or security mailbox. Report quality matters as well. A report that includes the original message and arrives through the correct workflow gives analysts actionable context, while a vague chat message or forwarded screenshot may not.

Report time measures the interval between delivery and the employee’s report. A fast report limits exposure for colleagues and gives the security team more time to investigate, warn users, and remove related messages. A slow but accurate report still demonstrates recognition, yet it creates a different operational risk than an immediate report.

A mature testing program also tracks whether an employee reports a message after clicking it. That action should receive credit for recovery while remaining distinct from a clean report before interaction. Treating the two outcomes as identical discourages honest reporting and hides where the user’s judgment interrupted the attack.

Training data adds longer-term context. Training completion records whether an employee finished the assigned lesson, while retention measures whether safer behavior persists in later simulations or delayed checks.

Completion proves exposure to instruction, and retention demonstrates behavioral change. A program that reports only completion can show activity without showing reduced human risk.

Organizations should connect these measures to role and scenario. Finance employees should be evaluated on invoice and payment requests, executives on impersonation and BEC, and help desk teams on password-reset lures. The relevant question is whether each person receives practice that matches the decisions the role requires. Finding the employee with the lowest score matters far less.

How Does Response Time Change the Meaning of a Test Result?

Response time turns a static susceptibility score into an operational measurement. A click that occurs three minutes after delivery differs from a click that occurs three days later. The first would give a real cyberattacker more time for follow-on interaction and lateral exposure before detection.

Measure at least three intervals: time to first interaction, time to report, and time to complete remediation. Time to first interaction shows how quickly a lure captured attention. Time to report shows how quickly the employee recovered or recognized the cyberthreat. Time to complete remediation shows how quickly the organization closes the behavioral gap with targeted training or coaching.

Use medians and distributions in addition to averages. A small group of rapid clicks can disappear inside an average dominated by inactive accounts. Segment results by channel, device, role, seniority, and scenario difficulty, then compare the same population over time.

A reduction in median report time with stable delivery conditions is a stronger sign of operational improvement than a lower click rate produced by an easier lure.

Response time also exposes the difference between individual and organizational performance. An employee can report quickly, yet a security team can take hours to quarantine related messages. Conversely, a delayed employee report can still produce limited impact if automated controls block the destination.

A phishing susceptibility test measures the human decision point. Technical telemetry and incident response metrics remain necessary alongside it.

How Do Behavioral and Technical Contexts Differ?

Behavioral metrics describe what a person did. Technical context explains why the event occurred and what happened around it. Both are necessary for fair interpretation.

A test should record the channel, device, message variant, delivery status, authentication path, browser destination, and whether security controls altered the experience. Email security scanners can open links automatically, mobile clients can render content differently, and privacy features can obscure opens. Separate those events from confirmed human interaction wherever the platform can distinguish them.

The same behavior also carries different business risk depending on access. A click by a new contractor with limited permissions carries far less weight than the same click by an administrator or payroll employee. Risk scoring should combine behavior with role, privilege, data access, and exposure context without turning the result into a character judgment.

A controlled simulation cannot prove that an employee will respond the same way to a real attack. It also cannot prove that the organization is breach-proof or that a reported click would have caused compromise.

The test cannot measure every out-of-band action, such as a phone call to a supposed vendor or a transfer approved in a separate system. It also cannot establish a direct probability of breach from one event. What it provides is evidence about behavior under defined conditions.

Treat the test as a measurement system that informs decisions without delivering verdicts. Establish a baseline, classify leading and outcome indicators, deliver role-specific training, test retention later, and review technical context before taking action.

Organizations that need a broader view can connect these findings to phishing simulation and human-risk measurement. That approach evaluates email, vishing, smishing, and other social engineering behaviors as one continuous program.

How Do Phishing Susceptibility Tests Work?

Phishing simulation tests measure whether employees recognize and report a simulated attack before a real attack of the same kind could reach sensitive data or business processes. Each phishing susceptibility test follows a sequence: set a measurable objective, select a controlled audience, approve the risk, and create a realistic but harmless scenario. The team then configures technical safeguards, delivers randomized messages, captures events, and applies immediate feedback.

Treat the initial campaign as a baseline. Then repeat comparable tests after training to measure behavioral change beyond completion alone. Every simulation must protect genuine incident reporting. It must also avoid collecting real passwords or creating an exploitable route into the organization. For a deeper walkthrough, see this guide on how to run phishing simulations.

1. Set the Objective Before Testing

A phishing susceptibility test should inform a decision the security team needs to make. The objective might be to establish an organization-wide baseline or compare risk between finance and engineering. It might also be to test whether employees report suspicious messages or to measure whether training reduced unsafe actions. Define one primary outcome before building the campaign so the results lead to a specific action.

Use measurable event definitions. A click records link interaction, and a submission records an attempted entry on the landing page. A report records use of the approved reporting channel, and a repeat visit records continued interaction after the warning.

Do not treat every click as a breach indicator. A user who clicks once and reports immediately presents a different training need from a user who clicks, enters text, ignores the warning, and repeats the behavior later.

A useful baseline captures the starting point before new training begins. Record audience size, delivery rate, click rate, attempted submission rate, reporting rate, and time to report. The baseline gives later campaigns a fair comparison and prevents leaders from confusing high course completion with safer decisions.

2. Select the Audience and Sample

Audience selection determines whether the test answers a real risk question. For a baseline, include the full employee population when the organization can support the operational volume. For a focused study, use a representative sample divided by role, department, location, employment status, and exposure to sensitive workflows.

Do not select only volunteers or security-conscious teams. That produces a flattering result with little operational value. Include finance staff who approve payments, executive assistants who manage calendars, administrators with elevated access, and newly hired employees who have not yet absorbed internal reporting procedures. Contractors and third parties require separate approval because their systems and employment relationships differ.

Randomize recipients within each approved group. Randomization reduces selection bias and prevents employees from predicting who will be tested. Preserve enough group information to compare outcomes, but avoid reporting individual results to managers unless policy and privacy review explicitly allow it. The purpose is to improve decisions and build employee skill, and a public ranking works against both.

3. Obtain Risk Approval and Define Safeguards

A simulation creates operational risk because employees and responders can confuse it with a real incident. Obtain written approval from security, legal, privacy, human resources, and the system owners responsible for email, identity, and help desk operations. Document the campaign window, audience, scenario, approved domains, collection fields, emergency stop process, and notification plan.

Set hard boundaries before launch. The campaign must not request or retain a real password, MFA code, payment instruction, customer record, or confidential document. It must also avoid cloning a live login page, sending users to an unapproved domain, altering mailbox rules, downloading software, or creating a route that a cyberattacker could reuse. A safe simulation tests judgment without handling secrets.

Protect genuine reporting throughout the exercise. The phishing report button, help desk queue, and incident response process must continue to treat real messages as real. Do not suppress alerts, delete reports, penalize employees for reporting, or tell the help desk to ignore reports during the campaign.

The CISA phishing training guide recommends frequent, realistic testing alongside clear reporting guidance and a no-blame culture. Rapid reporting remains valuable even when an employee has already clicked.

4. Create a Realistic but Harmless Scenario

Scenario design should mirror a credible decision, and an obvious fake logo falls short of that standard. Select an attack pattern relevant to the audience, such as a vendor invoice change for finance, a shared-document alert for legal, a benefits update for human resources, or an urgent account notice for remote staff.

Use OSINT only within approved boundaries. Exclude sensitive personal information that employees reasonably expect the organization to protect.

Build the scenario around a single action. The message can ask the recipient to review a document, confirm a scheduling change, or inspect an account notice, but the destination must lead to a controlled landing page. Avoid lures that exploit protected characteristics, medical circumstances, family emergencies, or recent personal events. Realism should improve recognition skills without causing distress.

A phishing simulator differs from a static visual quiz because it records behavior in context. A quiz asks whether a screenshot appears suspicious. A simulator tests what happens when a realistic message arrives in the employee’s normal workflow. It records whether the person opens it, follows the link, attempts an unsafe action, reports it, and responds to feedback. The test measures decisions and reporting habits in addition to visual recall.

5. Design the Landing Page as a Safety Control

The landing page should make the simulation harmless when the employee reaches it. Use a controlled page that resembles a real authentication portal no more closely than necessary. State that the message was a security exercise, identify the cues the employee should have checked, and provide the correct reporting path.

Never present a password field that accepts or stores a real credential. The safest design does not require text entry. If the exercise needs to measure attempted submission, use a non-secret placeholder field with clear instructions not to enter a password. Discard the value immediately without transmitting it to analytics or administrators.

Include an immediate learning moment. Explain why the request was suspicious, such as an unfamiliar domain, unusual urgency, a mismatched sender, or a request to bypass normal approval. Reinforce the behavior the organization wants in future cases: stop, verify through a known channel, and report the message. Do not use a public “gotcha” page or language that shames the employee.

6. Configure Microsoft 365 and Google Workspace Controls

Technical preparation prevents the simulation from being mistaken for an uncontrolled attack. In Microsoft 365, coordinate with the tenant administrator to use the organization’s approved simulation configuration and to document the sending domains and URLs. Confirm that mail-flow, Safe Links, and user-reporting policies handle the campaign as intended.

Test the message with a small internal group before wider delivery. Confirm that the report-phishing workflow still reaches the security team.

In Google Workspace, coordinate with the administrator on approved sender identities, link destinations, Gmail routing, and the organization’s phishing-reporting workflow. Verify that the landing page is reachable only through the intended test path and that authentication controls do not redirect employees to a genuine sign-in experience.

Keep the test separate from email security enforcement. The objective is to measure human decisions while leaving gateway, filtering, and identity controls intact.

Maintain an allowlist only where necessary and only for the campaign’s documented domains, URLs, and sending infrastructure. Restrict those exceptions to the test window and remove them after validation. An allowlist that remains active becomes an avoidable exposure.

7. Deliver Messages Randomly and Monitor Safely

Send the campaign in randomized waves, avoiding the release of every message at once. Staggered delivery reduces operational noise, limits the impact of a configuration error, and makes it harder for employees to warn one another before their own test arrives. Keep the campaign window short enough to support consistent analysis, but long enough to capture different work schedules and time zones.

Use unique, non-secret identifiers to connect delivery events with recipients. Capture only what the objective requires, such as delivery, open, click, landing-page visit, report, and time to report. Do not collect message content from personal mailboxes, keystrokes, passwords, MFA codes, or unrelated browsing activity.

Create an emergency stop condition before launch. Stop the campaign if the message reaches an unintended audience, the landing page behaves unexpectedly, or the report channel fails. The same applies if a real incident is confused with the exercise or a user attempts to submit sensitive information. The response team should know who can terminate delivery and how to remove remaining messages without deleting genuine evidence.

8. Capture Events and Preserve Context

Event collection turns a message into a usable risk signal. Connect delivery logs, landing-page events, phishing-report records, and training assignments using pseudonymous identifiers where possible. Record timestamps in a consistent time zone and separate technical events from behavioral outcomes.

Analyze rates by approved group, but avoid interpreting small groups as precise measurements. One click in a five-person team carries a different weight than one click in a five-thousand-person population.

Pair quantitative results with operational context. That context includes whether the scenario reached mobile devices, whether a reporting button was available, and whether mail controls altered the message.

Track positive actions as carefully as unsafe ones. A report made before a click indicates stronger recognition than a report made after interacting with the link. A report made within minutes of delivery indicates a faster defensive response than one made at the end of the campaign. These distinctions show where employees already provide an effective human control.

9. Provide Immediate Feedback and Targeted Training

Feedback should arrive immediately after the simulated action while the decision remains memorable. Explain the indicators that exposed the scenario, show how to verify the request safely, and direct the employee to the approved reporting process. If the employee reports the simulation correctly, reinforce that behavior and skip unnecessary remediation.

Assign short, role-specific follow-up training to employees who clicked or attempted an unsafe action. Finance employees might rehearse invoice verification, while executives and assistants might practice voice-based verification for urgent requests. Do not assign identical modules to every employee when event data identifies different decision gaps.

A modern phishing simulations program can connect email, voice, SMS, and deepfake scenarios to the same human-risk workflow. Each channel still requires its own consent, safety review, and reporting path.

Expanding beyond email matters because a strong email result does not prove that an employee can identify vishing, smishing, or executive impersonation.

10. Repeat the Test and Remediate the Program

A single campaign captures one moment, while a security program requires repetition. After baseline testing, deliver repeated campaigns at planned intervals with varied scenarios and channels. Keep enough conditions consistent to compare results while changing the lure, so employees learn verification habits and avoid simply memorizing one template.

Compare the post-training campaign with the baseline using the same core measures. Look for lower click and attempted-submission rates, higher reporting rates, faster reports, and fewer repeat failures. Also review whether employees report genuine suspicious messages more consistently. A lower click rate is not always good news. If reporting drops at the same time, employees may simply be avoiding messages they suspect are tests, which hides real risk rather than reducing it.

Close the loop with program changes. Update training content, improve the reporting button, revise approval procedures, and brief managers on the behaviors that need reinforcement. Remove temporary allowlisting and test artifacts when the campaign ends.

The strongest phishing susceptibility test leaves the organization with better verification habits and safer technical controls. It also sustains a reporting culture that remains fully active when a real message arrives.

How Can Organizations Design an Ethical Phishing Susceptibility Test?

An ethical phishing susceptibility test begins with clear governance, limited data collection, and a training purpose employees understand. Define the simulation rules before launch, protect individual results, provide constructive feedback, and review local labor requirements. Treat every employee as a security asset, using each missed signal as a coaching opportunity with no disciplinary intent.

1. Establish Informed Governance Before Testing

A phishing simulation must not become a hidden employee-surveillance program. Tell employees that simulations occur, explain why the organization runs them, identify the information recorded, and describe how results will be used. Advance disclosure does not require revealing the exact date, sender identity, or scenario. It does require making the program reasonably foreseeable.

Write these rules into the acceptable-use policy, security awareness training policy, or employee privacy notice:

• The purpose of testing, such as practicing detection and reporting
• The channels covered, including email, SMS, voice, or video
• The signals recorded, such as link interaction, credential entry, report time, and follow-up training completion
• The people who can view individual results
• The retention period and deletion process
• The circumstances under which results can be combined with other human-risk signals
• The route for asking questions, reporting accessibility barriers, or challenging inaccurate records

Use realistic scenarios without collecting real passwords, financial information, or sensitive personal data. A test page should accept a safe placeholder or stop at the interaction point. Each exercise should teach a behavior the organization expects, such as inspecting a sender, verifying an unusual request through a known phone number, or reporting a suspicious message through the approved channel.

Consult the right stakeholders before deployment. In the United States, involve legal counsel, human resources, and, where relevant, employee representatives. In the United Kingdom and European Union, consult the data protection officer and works council or other labor representatives where local rules require it.

The Information Commissioner’s Office employment guidance addresses worker monitoring and data protection, giving UK organizations a basis for reviewing their program design.

Do not make a single failed simulation an automatic disciplinary trigger. One interaction can reflect workload, unclear reporting procedures, a mobile interface problem, or a carefully engineered scenario. Managers should review patterns over time and consider context before taking employment action. If a serious policy violation requires escalation, follow the organization’s established process and give the employee a fair opportunity to respond.

2. Protect Privacy With Strict Access Controls

Individual simulation results are security-related personnel data and do not belong in general management circulation. Collect only the fields needed to run the exercise, provide feedback, and measure improvement. Avoid storing message content that contains unnecessary personal information, and separate identity data from performance data when the platform supports it.

Apply role-based access so each audience sees only what it needs. Security awareness administrators may need individual results to assign coaching. Managers may need aggregated trends for their teams, but a company-wide leaderboard serves no legitimate purpose.

Human resources and legal teams should receive access only when their defined responsibilities require it. Administrators should review access regularly, log sensitive activity, and remove permissions when roles change.

Set a written retention period before the first test. Keep raw event data only as long as it supports feedback, trend analysis, or a documented legal requirement. After that, delete it or convert it into an irreversible aggregate.

Management reports should use pseudonymized or grouped results wherever individual identity is unnecessary. A report can show that a department’s reporting rate improved without naming the employees who clicked.

Explain these controls in plain language. Employees should know whether results feed an individual risk profile, whether managers can see them, whether data leaves the country, and how to request correction or access.

Do not use automated scoring as the sole basis for a decision that produces significant employment effects. Human review remains necessary when a result could affect an employee’s status, access, or evaluation.

A documented privacy review should cover the scenario, data fields, recipients, retention schedule, vendor access, and deletion process. Revisit it when the organization adds vishing, smishing, deepfake video, or OSINT personalization, because each channel changes the type and sensitivity of data collected.

3. Make Feedback Accessible, Constructive, and Private

Feedback determines whether a phishing susceptibility test builds judgment or creates fear. Send it promptly after the interaction, explain the warning signs the employee missed, and show the correct action. Use language such as “This request used urgency and an unverified payment change” in place of “This test was failed.” The goal is a safer next decision, and a public display of error undermines it.

Avoid publishing rankings, circulating screenshots of individual mistakes, or asking managers to shame employees during team meetings. Public comparison suppresses reporting because employees start hiding uncertainty and stop asking for help. Managers should discuss recurring patterns privately, recognize employees who report suspicious messages, and provide targeted practice when someone needs it.

Make the exercise usable for the entire workforce. Test messages and landing pages with screen readers, keyboard-only navigation, high-contrast settings, text enlargement, captions, and alternative text. Ensure reporting instructions work on phones and tablets as well as desktop mail clients. Mobile workers should receive equivalent guidance when connectivity is intermittent or when their work occurs across time zones.

Account for assistive technologies and different work arrangements before measuring behavior. A button hidden behind a mobile interface, a screen-reader-incompatible page, or a timed scenario sent outside a worker’s normal schedule can measure accessibility friction in place of phishing judgment.

Offer an alternate reporting route, such as a monitored mailbox or phone channel. Record that accommodation without labeling the employee as higher risk.

Close each simulation with a practical action. Tell employees how to verify an unusual request through a trusted channel, where to report it, and what happens after they report. Organizations can reinforce that behavior through security awareness training focused on role-specific practice, while keeping simulation results separate from punitive performance management.

An ethical phishing susceptibility test measures whether people become more capable of spotting and reporting social engineering over time. One imperfect decision should never become a permanent label. That standard protects employee trust, improves reporting quality, and gives security leaders a more accurate picture of human risk.

What Scenarios Should a Phishing Susceptibility Test Include?

A phishing susceptibility test should compare employee responses across attack channels, business contexts, and increasing levels of difficulty. Channel-based testing reveals whether someone handles email, SMS, voice, or collaboration messages safely. Context-based testing shows whether urgency, authority, or familiarity changes the decision.

Email scenarios provide the baseline because they test links, credentials, and attachments in a controlled format. Multichannel scenarios create a stronger behavioral test because employees must verify requests that arrive through several trusted-looking paths.

A mature program uses both approaches. Every scenario must use approved organizational context, remain clearly contained, and avoid exposing real secrets.

How Should Email and Attachment Scenarios Be Designed?

Email scenarios should begin with common phishing, then progress toward spear phishing, BEC, CEO fraud, and reply chain attacks. The goal is not to trick employees but to rehearse a specific decision. Each scenario should rehearse the exact decision an employee must make before opening a file, changing payment details, sharing credentials, or responding to an urgent executive request.

A practical progression includes:

• Baseline email phishing: Use a generic account notice or document-share message to test recognition of suspicious links and credential prompts across the workforce.
• Spear phishing: Personalize the message around an approved role, department, project name, or public company event. Target finance, HR, executives, and administrators because their access and authority create higher consequences.
• BEC and CEO fraud: Test requests to change bank details, approve an unusual payment, or release sensitive information. These scenarios should reach finance, procurement, executive assistants, and senior leaders, with approval workflows that stay isolated from live transactions.
• Reply-chain attacks: Place the simulated request inside a realistic conversation thread. This tests whether employees verify a familiar sender before trusting the appearance of continuity.
• Malicious attachments: Use inert files that cannot execute code, collect credentials, or contact external systems. The exercise should measure whether recipients pause, inspect the file type, and report it. It should not test whether endpoint defenses can stop a real payload.

The safest personalization uses approved organizational context such as department names, standard software, fictional invoices, and publicly listed business events. It must exclude private health information, personal relationships, compensation details, breached credentials, and sensitive customer data. Before launch, security and legal teams should approve the scenario, landing page, sender identity, data collection, and rollback plan.

Organizations can connect this testing to multichannel phishing simulations that expose the human decision point without creating an actual vulnerability. The simulation should record safe signals such as reporting, link interaction, attachment opening, and verification behavior. It should then route the employee to brief coaching, with no public reprimand.

Which Mobile and Collaboration Scenarios Belong in a Test?

Mobile and collaboration scenarios matter because employees often inspect messages quickly on small screens, where sender details and URLs are harder to evaluate. Scenarios involving smishing, quishing, mobile phishing, and collaboration apps should therefore test whether employees verify a request before tapping, scanning, or approving it. The differences between vishing and smishing shape how each scenario should be built.

A smishing scenario can imitate a delivery update, payroll notice, or multifactor authentication alert without using a real carrier, bank, or employee account. A quishing scenario can place a harmless QR code in a simulated poster, email, or document and direct the user to an internal training page. The test should measure scanning and reporting behavior and must not collect passwords.

Collaboration-app scenarios should reflect the tools employees actually use. Examples include a fictional direct message from a project lead, a fake guest invitation, or a request to upload a document. These tests are most appropriate for remote teams, contractors, developers, and project managers who exchange files outside email.

Social media impersonation scenarios belong in executive, communications, recruiting, and sales programs. In those roles, a fraudulent profile or direct message can trigger disclosure, reputation damage, or a rushed payment.

Vendor fraud should be tested with procurement, accounts payable, and business owners. A simulated supplier can request a bank-account change or updated tax form, but the exercise must point employees toward the organization’s existing out-of-band verification process.

Mobile tests should not send messages to personal numbers without documented consent. Collaboration tests should not create external guests or alter production permissions.

How Should Voice, Deepfake, and AI-Generated Scenarios Work?

Voice, deepfake, and AI-generated scenarios should be introduced after employees understand verification rules in simpler channels. Vishing can simulate a help desk caller requesting a password reset, a supplier confirming new payment instructions, or an executive demanding immediate action. High-risk roles include finance, IT support, executive assistants, reception, and anyone authorized to approve money movement or access changes.

Deepfake social engineering requires stricter controls because a realistic face or voice can create authority under pressure. In 2024, an employee in Hong Kong approved about $25 million in fraudulent transfers after joining a video call populated by deepfake participants, according to CNN’s report on the Arup incident.

The same year, an apparent AI impersonation of former Ukrainian Foreign Minister Dmytro Kuleba targeted U.S. Sen. Ben Cardin, according to NBC News. These incidents justify controlled rehearsal. They do not justify cloning a real executive without written consent.

Use fictional executives, approved synthetic personas, or clearly authorized likenesses. Keep the scenario inside a sandbox, prohibit financial or credential collection, and terminate the exercise if an employee appears distressed or reports a real incident. Every voice or video request should reinforce a durable control: pause, use a known contact method, and confirm the request through an independent channel. A deepfake awareness training checklist can help structure these exercises.

Progressive difficulty keeps the test useful and prevents it from feeling punitive. Start with a generic email, add role-specific context, and then introduce reply chains, mobile delivery, and collaboration messages. Reserve coordinated voice or video requests for employees who demonstrate safe reporting and verification.

Use the results to assign targeted coaching. A strong phishing susceptibility test builds practiced judgment across the channels cyberattackers use, while ensuring that no simulation becomes the vulnerability it was designed to expose.

How Is a Phishing Susceptibility Rate Calculated?

A phishing susceptibility rate becomes useful only when each metric has a defined numerator, denominator, and exposure unit. Calculate raw rates first, separate users from messages, and then apply severity weights and confidence intervals before comparing campaigns. A benchmark such as 4.2 has no meaning until its formula, denominator, campaign type, and exposure context are stated. Measuring a phishing simulation program depends on these definitions.

1. Define the Population and Exposure Before Calculating

Record what the campaign actually measured before calculating a rate. A user-based campaign sends one simulated message to each employee and evaluates people. A message-based campaign sends multiple messages to the same employee and evaluates events, so the denominators cannot be combined.

Use these terms consistently:

• Audience exposed: The number of unique users intentionally included in the campaign.
• Messages delivered: The number of simulated messages that reached recipients’ inboxes or devices.
• Unique users observed: The number of distinct people who generated at least one valid event, such as a click, report, or credential submission.
• Valid observations: Events that passed data-quality checks and can be attributed to a known user and campaign, excluding test accounts, duplicate webhooks, blocked messages, and corrupted records.

The denominator must match the question. To find the share of employees who clicked at least once, use unique users exposed. To find the share of delivered messages that generated a click, use messages delivered. To find how often recipients reported messages, use delivered messages or unique users, but label the choice clearly.

A large study of simulated phishing behavior illustrates why campaign design matters. The 2025 IEEE Symposium on Security and Privacy study by researchers at the University of Chicago and UC San Diego followed about 19,500 UC San Diego Health employees over eight months, using 10 types of simulated phishing email. Repeated campaigns like these reveal far more about employee behavior than one click on one message.

2. Apply the Core Formulas

The basic phishing susceptibility rate measures the share of exposed unique users who performed a defined risky action at least once:

Phishing susceptibility rate = unique users with one or more qualifying risky actions ÷ unique users exposed × 100

For example, if 1,000 employees receive a campaign and 73 different employees click the simulated link, the susceptibility rate is:

73 ÷ 1,000 × 100 = 7.3%

Count each person once in this user-level metric. If one employee clicks three times, that employee remains one susceptible user. This prevents highly active or repeatedly targeted individuals from distorting the organization-wide rate.

The simulation failure rate, often used in security awareness reporting, requires an explicit definition because organizations calculate it in different ways. A defensible user-level formula is:

Simulation failure rate = unique users who clicked, opened an attachment, or submitted data ÷ unique users exposed × 100

If 90 of 1,000 exposed employees perform at least one qualifying action, the simulation failure rate is 9%. Do not silently include credential submission in one campaign and exclude it in another. Maintain a versioned event taxonomy that states whether opening, clicking, downloading, replying, or submitting information counts as a failure.

A message-level version is also valid:

Message click rate = messages with at least one click ÷ messages delivered × 100

If 1,200 messages are delivered and 84 generate a click, the message click rate is 7%. That figure is not interchangeable with a 7% user susceptibility rate. One employee can receive 10 messages while another receives one. The message-level formula measures message performance, and the user-level formula measures user behavior.

The reporting rate measures defensive behavior:

Reporting rate by message = messages reported ÷ messages delivered × 100

If employees report 180 of 1,200 delivered simulations, the message reporting rate is 15%. For a user-based view, use:

Unique-user reporting rate = unique users who report at least one message ÷ unique users exposed × 100

If 140 of 1,000 exposed employees report at least one message, the unique-user reporting rate is 14%. Both figures can be correct, yet they describe different behavior. A user who reports five messages adds five to the message reporting numerator but only one to the unique-user numerator.

A report-to-click ratio should be defined before use because no universal industry formula exists. One practical version compares defensive reports with risky clicks:

Report-to-click ratio = message reporting rate ÷ message click rate

If the message reporting rate is 15% and the message click rate is 7%, the report-to-click ratio is 2.14. Under that exact definition, the campaign generated 2.14 reports for every click. A ratio above 1.0 simply means that reports outnumbered clicks. It does not mean that more than 100% of users resisted.

This ratio becomes misleading when the two rates use different denominators. If reporting is calculated per unique user but clicking is calculated per delivered message, the result mixes people and events. Use the same unit, population, and observation window on both sides of the ratio. Otherwise, report the two underlying rates without dividing them.

3. Assign Greater Weight to More Dangerous Actions

Clicking and credential submission deserve different severity levels when the goal is to estimate business risk. A click indicates interaction with a simulation. Credential submission demonstrates that the employee crossed a more consequential decision point and would have exposed authentication data in a real attack.

Use a weighted severity score when the program needs more detail than a binary pass-or-fail rate:

Weighted severity score = sum of event weights ÷ sum of maximum possible weights × 100

For example, assign a click a weight of 1, an attachment download a weight of 2, a reply containing sensitive information a weight of 3, and a credential submission a weight of 4. If 100 valid observations include 60 clicks, 20 downloads, 15 replies, and five credential submissions, the weighted score is:

(60 × 1 + 20 × 2 + 15 × 3 + 5 × 4) ÷ (100 × 4) × 100 = 41.25%

This score is a measurement convention and should not be read as a probability of breach. Publish the weights with every result, keep them stable across comparison periods, and avoid presenting the score as objective fact. The organization can also report separate rates for clicking and credential submission, which prevents a severe event from disappearing inside an average.

Distinguish an accidental click from deliberate submission through event sequence and interaction evidence. A click followed immediately by page closure differs from a completed form, multiple field entries, a paste event, or a submission request.

Do not infer intent from dwell time alone. A slow click does not show deliberation, and a fast submission does not show carelessness. Classify the observed action and leave the employee’s character out of the analysis.

A clean taxonomy might record “clicked,” “opened attachment,” “entered data,” “submitted credentials,” and “reported.” If a user clicks and then reports the message, retain both events. Then define whether the campaign’s primary susceptibility metric counts the user as susceptible, resilient, or both. A useful dashboard can show the initial risky action alongside the recovery action so neither label erases the other.

4. Add Confidence Intervals and Compare Campaigns Correctly

A campaign rate is an estimate from a finite sample and can change as the workforce changes. For a user-level susceptibility rate, calculate a binomial confidence interval. The Wilson interval is preferable to the simple normal (Wald) interval, particularly when the rate is close to zero or the sample is small.

For a proportion p = x/n, the approximate 95% Wilson interval is:

(p + z²/(2n) ± z√[p(1-p)/n + z²/(4n²)]) ÷ (1 + z²/n)

Use z = 1.96 for a 95% interval. Statistical software should calculate the interval and preserve the numerator, denominator, and confidence level in the report.

Do not declare one campaign better because its rate moved from 8% to 6%. Compare the two proportions with a two-proportion test or with Fisher’s exact test when expected counts are small. A regression model can also adjust for department, role, campaign difficulty, and repeat exposure.

Report the absolute difference, relative difference, and confidence interval. A small sample can produce a dramatic-looking percentage that remains statistically uncertain.

Campaign comparisons also require matched exposure context. Keep the audience definition, channel, message difficulty, send timing, authentication controls, training intervention, and observation window consistent. A finance-team vishing simulation should not be compared directly with an organization-wide low-risk email test. If the campaigns differ, stratify results by role or channel and state the limitation.

A phishing simulation reporting framework should preserve the calculation inputs alongside the result so leaders can reproduce the comparison and act on the underlying signal.

Avoid publishing a benchmark such as 4.2 without its formula. It could mean 4.2% of unique users clicked, 4.2 clicks per 100 delivered messages, a weighted severity score, or a ratio with no percentage basis.

State the numerator, denominator, campaign type, delivery count, unique-user count, valid-observation rule, and exposure context. That documentation turns a memorable number into a decision-grade phishing susceptibility test result, giving security teams a reliable basis for targeted behavioral change.

How Should Phishing Susceptibility Test Results Be Compared Across Teams and Roles?

A phishing test becomes useful when results are compared across cohorts that face similar work conditions. A raw department score shows who clicked. A risk-adjusted comparison accounts for exposure, access, device, work pattern, and lure difficulty.

A finance employee receiving a payment-change request faces a different test than a help desk employee receiving a fake password-reset alert, and the two should be evaluated accordingly. Industry benchmarks provide context, but an organization’s own baseline shows whether its controls and training are changing behavior.

Fair comparisons of phishing susceptibility test results require consistent cohorts, repeated measurements, and careful treatment of uncertainty.

How Should Organizations Build Risk-Adjusted Cohorts?

Risk-adjusted cohorts separate job context from individual behavior. Segment results by department and role, then add seniority, geography, device type, email exposure, remote-work pattern, and access privilege.

Finance teams may encounter invoice fraud and vendor-payment lures, and HR may see benefits or payroll requests. Help desk staff may face credential-reset messages, administrators may receive privileged-access prompts, and customer-facing teams may be targeted through account or contract themes. These differences reveal distinct attack paths. They do not indicate which employees deserve blame.

Use a consistent segmentation model that preserves enough participants in each cohort:

• Business context: Department, job role, seniority, geography, and customer-facing responsibility.
• Technical exposure: Desktop or mobile device, email volume, external correspondence, and remote-work pattern.
• Impact potential: Privileged administrator status, access to payment systems, sensitive records, or executive communications.
• Scenario conditions: Email phishing, spear phishing, business email compromise (BEC), vishing, smishing, QR phishing, and deepfake impersonation.

Executives require the same analytical discipline. Their visibility, authority, and access can make them attractive targets, but executive status alone is no evidence of greater susceptibility. Compare executives with similar message volume, assistant or delegate workflows, travel patterns, mobile use, and approval responsibilities. Measure exposure to executive impersonation and high-consequence requests separately from click behavior.

A leader who reports a suspicious message quickly may represent lower operational risk than a nonexecutive who clicks less often. That holds especially when the nonexecutive has privileged access and does not report the event.

The scenario must remain visible in every dashboard. A difficult, highly aligned lure can inflate click rates without indicating a general decline in judgment.

A 2025 field study of 12,511 fintech employees found click rates ranging from 7% for easy simulated emails to 15% for hard messages. That finding supports using lure difficulty as a comparison variable, since campaigns of different difficulty cannot be treated as equivalent. The 2025 study grounded in the NIST Phish Scale also shows why scenario type belongs beside department and role when interpreting outcomes.

How Reliable Are Phishing Test Comparisons?

Statistical reliability depends on the number of people exposed, the number of events observed, and the consistency of the test design. A cohort of 10 employees with one click carries far less certainty than a cohort of 1,000 employees with 100 clicks, even though both produce a 10% click rate. Small groups should be labeled directional and kept out of definitive high-risk rankings.

Set a minimum cohort size before launching a campaign, and report the numerator and denominator with every rate. Calculate uncertainty ranges, such as 95% confidence intervals, around click, report, and attachment-open rates. A 20% result from 15 people carries far more uncertainty than a 20% result from 1,500 people. When two teams’ intervals overlap substantially, describe the results as similar or inconclusive and avoid declaring a winner.

Repeated-measures analysis produces a stronger signal than a single campaign. Follow the same employees or stable cohorts across several simulations while rotating scenario types and controlling for delivery conditions. Compare each person’s behavior with prior results, then compare the cohort trend with a matched control or organization-wide baseline.

This approach distinguishes behavioral change from campaign noise, seasonal workload, onboarding effects, or a lure that happened to fit one department’s routine.

Cohort consistency is essential. Do not compare a fully remote customer-support group tested on mobile devices in December with an office-based finance group tested on desktop devices in March, then attribute the difference to department. Keep the sampling frame, delivery window, difficulty mix, reporting channel, and outcome definitions consistent. If those conditions change, document the change and treat the campaign as a new measurement series.

How Should Security Leaders Interpret Repeat Offenders?

Repeat behavior deserves targeted support, and a permanent label helps no one. A single click can reflect an unusually realistic lure, a rushed moment, or a technical reporting failure. Repeated clicks across different channels and scenario types indicate a recurring behavior pattern that warrants just-in-time training, coaching, and closer measurement.

Track the event sequence and avoid assigning a fixed identity. Record whether the employee clicked, entered data, opened an attachment, reported the message, completed follow-up training, and improved on the next comparable test.

A useful profile distinguishes repeated failure on one lure family from broad susceptibility across email, vishing, and smishing. It also separates failure to recognize a cyberthreat from failure to follow the reporting process.

Human risk scores should decay or be recalculated as new evidence arrives. Recent behavior should carry more weight than an old event, and successful reporting should offset earlier mistakes when the evidence supports improvement.

Restrict individual results to authorized security and management personnel, exclude them from performance evaluations, and report aggregate trends to business leaders. The objective is to direct practice toward a specific skill gap while preserving trust in the program.

Why Should Internal Baselines Outrank Industry Benchmarks?

Industry benchmarks show whether a result appears unusual, but they cannot account for an organization’s mail flow, culture, technology stack, workforce mix, or simulation design. A benchmark built from different industries and lure types can create false reassurance or unnecessary alarm. An internal baseline provides the more relevant counterfactual because it shows how the same workforce performs under comparable conditions.

Establish the baseline with a representative first campaign, record cohort definitions and scenario difficulty, and then repeat the measurement on a planned schedule. Report absolute rates, percentage-point changes, confidence intervals, and reporting behavior together.

A falling click rate with no increase in reporting can indicate avoidance instead of stronger detection. Meanwhile, a stable click rate alongside faster reporting can still reduce time to containment.

Use human risk reporting to compare results by role and exposure, identify specific skill gaps, and assign targeted practice. A single score cannot deliver a verdict. The useful signal is the pattern across people, scenarios, and time, where improved reporting and faster escalation translate into a stronger operational response.

How Often Should Organizations Run Phishing Susceptibility Tests?

A phishing simulation program should begin with a baseline assessment and continue through randomized simulations, thematic campaigns, and targeted retesting. Set the phishing susceptibility test cadence around exposure, organizational change, channel expansion, and employee fatigue. An arbitrary annual deadline is a poor guide.

Retention serves as the final checkpoint. Compare delayed results, reporting behavior, and responses to unfamiliar scenarios, and avoid rewarding employees for memorizing one lure.

1. Establish a Baseline and Set a Practical Cadence

A baseline phishing simulation shows where the organization starts and which roles, departments, and attack channels require attention. Run it before assigning broad remedial training, using realistic but controlled scenarios across the channels employees actually use. Record click rate, credential-submission rate, reporting rate, time to report, and the cues employees missed.

After the baseline, use a continuous cadence with enough variation to measure behavior without turning every inbox into a test environment:

• Monthly or other recurring randomized simulations: Send unpredictable tests at varied times and difficulty levels. Rotate credential theft, vendor impersonation, business email compromise (BEC), QR phishing, and urgent document-sharing requests. Randomization keeps employees focused on building detection skills, since no calendar pattern exists to learn.
• Quarterly thematic campaigns: Dedicate each quarter to a risk pattern, such as finance-focused invoice fraud, executive impersonation, smishing, or vishing. Coordinate email, SMS, and voice scenarios when the organization faces multichannel social engineering.
• Annual program review: Use the yearly review to reset goals, revise scenarios, and compare department-level trends. The annual review should supplement testing throughout the year.

The IEEE study described earlier tracked behavior across repeated campaigns. One annual phishing test measures a moment. Repeated tests reveal whether behavior holds under changing conditions.

Cadence must still account for fatigue. Excessive testing with repetitive lures can teach employees to distrust every message, weaken trust in legitimate internal communications, and reduce reporting quality. Keep scenarios varied, explain the learning objective after each test, and reserve high-frequency campaigns for groups facing higher exposure.

A phishing simulation program built around varied email, voice, and SMS scenarios extends testing beyond an email-only pattern that no longer reflects how cyberattackers operate.

2. Retest After Risk-Changing Events

Trigger-based retesting closes the gap between a scheduled campaign and a real change in exposure. Run a focused simulation after a confirmed phishing incident, a near miss involving sensitive data, or a material increase in suspicious reports. A phishing incident response playbook can define when these retests begin.

Punishing the employee who missed the original signal is never the aim. The retest determines whether the team can recognize a related attack after receiving timely coaching.

Organizational changes also justify targeted tests. Retest employees after they join a new department, take on payment approval, receive privileged access, or move into an executive-support role. Repeat the assessment after mergers, acquisitions, major software migrations, and return-to-office changes, because new vendors, workflows, and communication habits create fresh opportunities for impersonation.

Just-in-time training should lead to a follow-up test after a reasonable delay. Give employees enough time to apply the lesson, and present a different lure that tests the same skill. Someone who clicked a fake password-reset link should later face a vendor invoice or shared-document request in place of the same password-reset template. The goal is transferable judgment that goes beyond template recognition.

3. Measure Retention With Delayed and Unfamiliar Tests

Retention measurement starts with a delayed follow-up, well after the training ends. A second attempt minutes later reveals little. Compare the original result with performance several weeks later, and test again during a later thematic campaign. Track whether the employee reports the message, how quickly the report arrives, and whether the person avoids unsafe actions before opening a security ticket.

Use three signals together. First, measure performance on a new scenario that shares the same attack principle but changes the sender, pretext, channel, and visual design. Second, measure reporting behavior, including report rate, false-positive rate, and time to report. Third, compare results across time to separate durable improvement from a short-lived response to a recent lesson.

A lower click rate by itself does not establish retention. Employees can ignore one familiar lure while remaining vulnerable to a convincing executive request, a smishing message, or an AI-generated voice call. Strong measurement compares delayed follow-up results across new scenarios and channels while preserving a safe learning environment that treats mistakes as signals for better practice.

Review the findings by role and exposure level in addition to the company average. If finance improves on invoice fraud but remains slow to report vishing, the campaign should target that gap. If reporting rises while false positives also rise sharply, refine the instruction so employees know what to report and how to describe the signal.

A risk-based cadence turns each phishing simulation into a training decision. The result is a cycle of assessment, practice, measurement, and adjustment.

How Can a Phishing Susceptibility Test Improve Security Awareness Training?

A phishing susceptibility test becomes valuable when its result changes what an employee practices next. Another dashboard percentage adds little on its own. A failed simulation identifies behavior under pressure, while targeted coaching turns that moment into a safer decision when a real request arrives.

The 2025 IEEE Symposium on Security and Privacy study found no significant relationship between how recently employees completed annual awareness training and whether they failed simulated phishing. That result reinforces the need for repeated, behavior-focused security awareness training and measurement.

How Should Security Teams Diagnose Phishing Test Results?

Diagnosis starts with the failure type, with the employee’s identity secondary. Clicking a credential-harvesting page signals a different training need than opening a malicious attachment, approving a fake invoice, or scanning a QR code. Responding to smishing or vishing and trusting a deepfake executive request each signal distinct needs as well. Treat each event as a behavioral signal that shows which warning signs the employee missed.

Map each result to a short lesson delivered immediately after the simulation. A link failure should trigger practice with domain inspection, unexpected redirects, urgency cues, and mismatched sender context. An attachment failure should focus on file-type risk, malware delivery, and confirming the request through a known channel.

A business email compromise (BEC) failure should rehearse payment-change verification. Credential-harvesting remediation should show how fake login pages imitate familiar brands.

The same approach must cover channels beyond email. QR-code failures require practice in previewing the decoded destination before opening it. SMS failures call for sender verification and resistance to delivery, payroll, or account-lockout pressure. Voice failures should reinforce callback procedures and the danger of trusting familiar caller ID, which can be spoofed. Deepfake failures should teach employees to verify unusual executive requests independently, even when a face and voice appear authentic.

Click rate is only one diagnostic signal. Compare whether the employee reported the message, how long reporting took, whether the user entered information, and whether the failure repeated under a different scenario. Someone who clicks but reports the message within seconds needs different coaching from someone who submits credentials and does not alert the security team.

How Should Remediation Become Personalized?

Personalized remediation assigns the right practice to the right role, channel, and risk pattern. A role-based security awareness training program puts this principle into practice. Finance employees should rehearse invoice fraud, vendor bank-detail changes, and executive payment requests. Human resources teams need scenarios involving payroll documents, benefits enrollment, and sensitive employee records.

IT teams should practice fake password resets, MFA prompts, and urgent administrator requests. Executives and their assistants need deepfake, vishing, and impersonation drills that reflect their public exposure.

Short lessons work best when they explain the missed signal while the event remains memorable. Show the simulated message again and identify the sender anomaly, suspicious domain, pressure tactic, or missing verification step that would have interrupted the attack. Give the employee one action to repeat, such as opening the reporting control, calling a known number, or navigating directly to a service without using the supplied link.

A phishing susceptibility test should trigger constructive follow-up, and should not lead to punishment. If an employee repeatedly fails, assign brief coaching with a security awareness lead, involve the manager in scheduling, and review whether workload, access complexity, or unclear procedures contributed.

Security leaders can review elevated privileges, payment authority, mailbox rules, and sensitive-data access. They should apply the least restrictive safeguard that addresses the demonstrated risk.

That escalation protects the organization without turning training into a disciplinary trap. Employees are more likely to report near misses when the program treats mistakes as opportunities to build judgment. A security awareness training program built around repeated practice makes the correct response easier to remember and execute.

How Can Organizations Prove Retention Over Time?

Improvement requires repeated tests that vary the lure while preserving the behavior being measured. Test with a suspicious link, a vendor impersonation email, a QR code, or a voice request to determine whether the employee learned a durable verification habit or memorized one template. The IEEE study cited above supports measuring behavior beyond completion records, a theme explored further in this look at cybersecurity awareness training effectiveness.

Track five outcomes together: reporting rate, time to report, repeat-failure reduction, training completion, and delayed retention. Reporting rate shows whether employees activate the defense process. Time to report indicates whether they act before a cyberattacker can escalate. Repeat-failure reduction shows whether remediation changed behavior.

Completion confirms exposure to the lesson. A delayed retest weeks later shows whether the warning signs remain available under pressure.

Set a baseline, deliver targeted remediation, and retest at planned intervals with new scenarios. Segment results by role, department, channel, and severity so the workforce is never averaged into one reassuring number. If reporting improves but credential submission continues, strengthen training on inspecting login pages. If email performance improves while voice failures persist, shift practice toward vishing and deepfake verification.

A strong program reports movement in human behavior, which matters more than activity totals. Leaders should see which failure patterns are declining, which groups need coaching, and whether employees retain the response after immediate feedback fades. Those measurements turn a phishing susceptibility test from a pass-or-fail exercise into a continuous cycle of diagnosis, practice, verification, and safer action.

How Phishing Susceptibility Test Data Fits Into Human Risk Management

A phishing susceptibility test becomes useful human-risk evidence when combined with reporting behavior, training completion, exposure context, and real incident signals. It does more than pass or fail employees. It shows where employees need practice, which helps security teams prioritize without ranking people as liabilities. This is the foundation of human risk management.

How Should Phishing Test Data Be Correlated With Security Operations?

Operational correlation connects a test result to the conditions surrounding it. A single simulated click shows that an employee interacted with one controlled lure. It does not establish that the employee caused a production incident or will repeat the behavior.

Analysts should examine whether the same department also generates more security operations center alerts or help desk tickets involving suspicious messages. Incident records involving credential exposure, unusual forwarding, or unauthorized payment requests are also relevant. These signals add context without collapsing distinct types of evidence into one misleading score.

Simulation events should remain in a controlled training dataset, while production alerts, help desk tickets, and incident records remain operational evidence. A shared employee or department identifier can support trend analysis. However, the records should not be combined to claim that a failed simulation caused a breach.

Correlation does not establish causation. A finance team might click more simulated invoices because it receives more realistic vendor fraud attempts, even if its judgment is as sound as that of other teams.

That distinction protects analytical accuracy and employee trust. The Cybersecurity and Infrastructure Security Agency’s 2023 phishing guidance recommends prompt reporting, remediation of successful phishing attempts, and documented response processes.

A practical human-risk view compares simulation reporting rates, time to report, repeat interaction with similar lures, training completion after an event, and the volume of related operational alerts.

The objective is to identify where coaching or process changes are needed. Labeling an employee as unsafe does not serve that objective. Employees who report suspicious activity provide an important security signal, even when reporting temporarily increases alert volume.

Organizations can strengthen this analysis by adding exposure context. Publicly available role information, privileged access, customer-data access, payment authority, and remote-work patterns change the potential consequence of a mistake. An employee with a low simulation failure rate but high financial authority still needs focused verification training because one rushed decision could carry significant impact.

A new employee with limited access and one isolated failure needs constructive practice, and escalation based on a single event would be disproportionate. Human risk management reporting brings these factors together so security leaders can compare behavior, exposure, and business impact without reducing human risk to one metric.

How Should Management Report Human Risk?

Management reporting should translate behavior into trends, concentration, and response improvement. Boards gain little from employee rankings or a headline completion percentage that reveals nothing about whether the organization is safer.

Boards need to know which business processes carry the most exposure and whether high-risk groups are improving. They also need to know how quickly employees report suspicious activity and where controls still depend on individual judgment.

A useful management view answers four questions:

• Is susceptibility declining across repeated simulations?
• Is reporting becoming faster and more accurate?
• Is risk concentrated in a role, location, business unit, or high-impact workflow?
• Are real incidents and security operations center signals showing fewer repeat patterns after targeted training?

These measures show movement over time and connect human behavior to operational resilience. They also give security leaders evidence for targeted coaching, which is typically more efficient than broad, repetitive training that treats every employee the same.

Reports should explain their limitations. A simulation tests a designed scenario under controlled conditions, while a production incident involves real cyberattackers, real pressure, and potentially different channels. A lower click rate does not show that phishing risk has disappeared. A higher reporting rate can signal greater willingness to escalate concerns, even when it raises alert volume.

Management should interpret simulation results alongside investigation quality, response-time improvement, and avoided exposure. For example, employees who report a suspicious payment request before credentials or funds are released demonstrate a stronger control outcome than completion data alone can show.

How Should Human-Risk Data Guide Security-Budget Decisions?

Security budgets should follow concentrated exposure and measurable improvement, with raw training completion playing a minor role. If finance shows repeated interaction with payment-themed lures, funding should support out-of-band verification drills and targeted coaching. If help desk tickets reveal confusion about account-reset messages, investment should address the reporting workflow and identity-verification process.

If executives face high OSINT exposure, leadership-specific exercises deserve priority because executive identities carry greater authority in social engineering attacks. The same principle applies to employees with privileged access, payment authority, or access to sensitive customer data.

This approach makes avoided exposure visible without claiming that a simulation prevented a breach. Leaders can report that employees identified a simulated vendor-payment request, reduced median reporting time, or stopped repeating a behavior after targeted practice. Those outcomes connect spending to observable risk reduction and support the case for security awareness training ROI, while preserving the distinction between controlled evidence and real incident outcomes.

Human risk measurement should guide decisions, not punish employees. Employees are more likely to report suspicious activity when the organization treats reporting as a security contribution and uses test results to build skills. The reliability of those decisions depends on defining exactly what a phishing susceptibility test measures and applying that definition consistently.

What a Modern Phishing Susceptibility Program Looks Like

A modern program built on phishing susceptibility test results measures how employees recognize and report social engineering across the channels they use every day. CISA's *Phishing Guidance: Stopping the Attack Cycle at Phase One* calls for phishing awareness training and reporting procedures, but exposure changes faster than an annual awareness cycle can capture. Effective programs move from baseline testing to targeted practice, adaptive learning, and board-level human-risk reporting.

What Does a Mature Phishing Susceptibility Program Include?

Program maturity begins with a controlled baseline. Run a representative phishing simulation before assigning new training, then record who clicked, entered information, opened an attachment, reported the message, or ignored it. Segment results by role, department, privilege level, and attack type.

A finance employee who handles vendor invoices faces a different exposure pattern from an engineer who manages production credentials. For that reason, one organization-wide susceptibility percentage cannot guide effective remediation.

Each result should trigger a specific learning action. An employee who interacts with a simulated credential lure should receive short microlearning on the warning signs, followed by a later test using a different pretext. This approach treats employees as developing defenders who take an active role in security.

It also helps security teams distinguish a knowledge gap from a process problem, such as unclear reporting instructions or approval workflows that reward speed over verification.

Personalization makes simulations plausible without making them punitive. OSINT can identify public details cyberattackers might use in spear phishing, including a person’s role, conference appearances, or company relationships. Adaptive Security combines OSINT-informed personalization with AI-powered security awareness training, allowing simulations to reflect realistic employee exposure in a controlled setting.

Its phishing simulations extend practice beyond generic email templates to the human decisions that determine whether an attack progresses.

How Should Phishing Simulations Cover Multiple Channels?

Email-only testing no longer represents the full attack surface. A modern phishing susceptibility program should include vishing, smishing, QR-code phishing, collaboration-platform scams, vendor impersonation, business email compromise (BEC), AI-generated phishing, and deepfake social engineering.

Each channel tests a different trust cue. Email relies on sender identity and links, and voice attacks exploit familiarity and urgency. Collaboration-tool scams borrow legitimacy from shared projects and internal conversations.

Real incidents show why cross-channel practice matters. The Arup deepfake video call and the apparent AI impersonation that targeted Sen. Cardin, both described earlier, demonstrate that deepfake social engineering can pursue payments or sensitive information. In each case, a convincing face or voice made independent verification essential.

Coverage should follow observed exposure more closely than a fixed calendar. A workforce with frequent mobile use needs smishing and voice exercises. Finance and procurement teams need invoice fraud, vendor impersonation, and BEC scenarios. Executives need deepfake video, AI voice cloning, and public-profile impersonation drills. Every scenario should reinforce the same action pattern: pause, verify through a trusted channel, and report the attempt.

How Do Organizations Measure Phishing Risk Continuously?

Continuous measurement turns a phishing susceptibility test into a risk management process. After every simulation, compare interaction rates with reporting rates, time to report, repeat-failure patterns, training completion, and performance by channel. A lower interaction rate matters, but faster reporting can reduce the period in which a cyberattacker retains access. Repeated failures should trigger targeted training and a follow-up simulation in place of another generic assignment.

Risk reporting must combine signals, since completion percentages alone reveal little. Unified human-risk reporting can connect simulation behavior, training response, OSINT exposure, and other relevant indicators to department and executive views. Adaptive Security’s risk monitoring model uses these signals to support continuous scoring, while its AI Content Studio creates role-specific modules from policies or observed gaps.

Board-ready reporting should show where exposure is concentrated, whether risk is declining, how quickly employees report cyberthreats, and which controls require investment.

This measurement loop keeps the program aligned with changing cyberattacker behavior. Security leaders establish a baseline, test multiple channels, deliver immediate coaching, retest the behavior, and report the trend. That discipline turns awareness training from an annual checkbox into an evidence-based defense for the human layer. In that layer, clear verification habits determine whether a suspicious request becomes a security incident.

Phishing Susceptibility Test FAQs

What Is a Good Phishing Susceptibility Rate for an Organization?

No universal target applies. A good phishing susceptibility rate is one that falls steadily from a documented baseline while reporting improves. Set targets by action severity, separating clicks, credential submissions, attachment interactions, and reports so each event is weighed on its own terms. A 5% click rate and a 5% credential-submission rate signal very different exposure.

CISA recommends frequent, realistic simulations because they build lasting awareness, and it emphasizes prompt reporting as a defensive behavior in its phishing simulation guidance. Track results by consistent cohort, scenario, channel, and denominator. The practical goal is sustained reduction in high-severity actions with faster reporting. A zero score that suppresses honest reporting defeats that goal.

How Many Users Are Needed for a Statistically Reliable Phishing Susceptibility Test?

A statistically reliable phishing susceptibility test needs about 385 users for a 95% confidence level and a plus-or-minus 5-percentage-point margin of error. That figure applies when the expected rate is unknown and the population is large. The estimate uses the conservative 50% proportion, which produces the largest sample requirement.

Smaller margins require substantially larger samples, while a small total workforce requires a finite-population adjustment. NIST cites phishing tests as an example of security measurement and notes that confidence intervals provide more insight than single figures in its Measurement Guide. Treat results based on only about 30 responses as directional. Report the numerator, denominator, confidence interval, and response count so leaders understand the uncertainty behind every rate.

Should Employees Be Told in Advance That Phishing Simulations Will Occur?

Employees should be told that phishing simulations are part of the organization’s security program. They should not be told when a particular message will arrive or what lure it will use. Advance policy-level notice establishes legitimate purpose, supports trust, and gives employees a clear route to report concerns. Combined with policy-level notice, withholding campaign timing preserves the behavioral value of an assessment without turning it into a surprise disciplinary exercise.

The policy should state that simulations will not collect real passwords, expose personal results publicly, or punish a single mistake. CISA recommends realistic, frequent simulations to build lasting awareness in its phishing training guidance. Pair testing with immediate, constructive feedback.

How Can Organizations Validate That a Phishing Simulation Predicts Real-World Compromise Risk?

Organizations can validate a phishing simulation by testing whether simulated behaviors align with independently recorded real-world signals over time. Compare clicks, credential-page interactions, reporting speed, and repeat failures with confirmed phishing incidents, help desk reports, identity alerts, and containment events. Use matched cohorts and preserve a holdout period so the organization tests whether results predict future incidents in addition to explaining past ones.

In its study of differential phishing susceptibility, NIST describes simulated phishing campaigns as useful proxies because they can closely mimic actual attacks. Measure calibration and trend direction, and avoid claiming that one click equals compromise. A valid program improves prioritization while technical controls and incident evidence remain essential.

What Privacy Safeguards Should Apply to Individual Phishing Test Results?

Individual phishing test results should be limited to the data needed for remediation, protected by role-based access, and retained only as long as a defined purpose requires. Restrict identifiable records to authorized security or training staff and pseudonymize management dashboards. Encrypt stored and transferred data, document retention and deletion rules, and explain how results are used.

Do not publish rankings or attach permanent labels to employees based on one event. Review consent, notice, accessibility, labor, and works council requirements before launch. The NIST Privacy Framework recommends managing privacy risk through organizational policies and controls, including authorized access and data minimization. These safeguards keep measurement focused on constructive coaching and away from surveillance.

See How Adaptive Security Measures and Reduces Phishing Risk Across the Organization

A phishing susceptibility test limited to email leaves vishing, smishing, QR codes, and other human-targeted attack paths unmeasured. Adaptive Security connects multichannel simulations with security awareness training and human-risk measurement, giving teams clearer behavioral signals and targeted follow-up. Take a self-guided tour of the phishing simulation platform.