End User Security Awareness Training Maturity Model: 5 Stages to Measurable Behavior Change and Lower Human Risk

Key takeaways

• Maturity describes the program: The five stages assess governance, coverage, realism, measurement, and improvement. They do not grade individual employees or assign blame.
• Completion rates cannot prove behavior change: A finished course record shows that content was delivered but says nothing about how an employee handles an urgent payment request or a cloned executive voice.
• Five stages define the progression: Nonexistent, compliance-focused, behavior-change, culture-change, and optimized. Many organizations operate at different stages across departments, channels, and workforce populations.
• One evidence model serves every framework: Records covering assignment, testing, reporting, intervention, and management review support NIST, ISO 27001, SOC 2, HIPAA, PCI DSS, GDPR, and CMMC from a single source.
• Multi-channel testing separates mature programs: Email click rate may not reflect how an employee responds to a cloned executive voice, an urgent SMS request, or a deepfake video meeting, so each channel needs its own measurement.

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An end user security awareness training maturity model shows how consistently an organization prepares employees, contractors, and suppliers to recognize cyberthreats, report them, and make safer decisions before human risk becomes business risk. This guide helps security and IT leaders assess five stages, from nonexistent and compliance-focused programs to behavior-change, culture-change, and optimized operations.

The sections below explain how to identify a current stage, set practical transition criteria, and build a roadmap that connects phishing simulations, role-based employee training, reporting behavior, and targeted intervention. They also explain why completion rates alone cannot demonstrate reduced exposure to social engineering, spear phishing, business email compromise (BEC), vishing, smishing, deepfakes, or AI-enabled cyberattacks.

The framework connects program evidence to human risk management, governance, audit readiness, and board reporting across NIST, ISO 27001, SOC 2, HIPAA, PCI DSS, GDPR, and CMMC. It treats employees as a critical security capability and uses psychological safety, privacy-aware measurement, and cross-functional ownership to improve reporting without punishing mistakes.

Applying the model allows security leaders to prioritize the next maturity investment and measure whether safer behavior persists across channels and roles as cyberthreats change. See how Adaptive Security measures behavior change across the workforce.

What Is an End User Security Awareness Training Maturity Model?

An end user security awareness training maturity model evaluates how consistently an organization prepares, measures, and improves employee security behavior. It shows whether training operates as a one-time compliance task or as a continuous program that changes decisions across email, voice, SMS, collaboration tools, and in-person work.

The model is directional. It does not function as a universal certification or a single score, so leaders use it to identify the next improvement without labeling employees as secure or insecure.

What Is the Purpose of an End User Security Awareness Training Maturity Model?

The purpose of an end user security awareness training maturity model is to turn vague questions about "security culture" into observable program conditions. Security leaders can assess whether the organization reaches the right people, teaches relevant behaviors, tests those behaviors, measures results, and adjusts training when risk changes.

That structure connects awareness work to outcomes such as faster reporting, stronger verification of unusual requests, and fewer unsafe responses to social engineering.

A training checklist answers whether assigned activities happened. It records tasks such as publishing an annual course, sending a phishing simulation, collecting completion data, or distributing a policy. Those records support audits, but they do not show whether an employee can recognize a convincing BEC request, challenge an unexpected payment instruction, or report a suspicious message quickly.

A maturity model answers a broader question: how reliably does the entire program produce and sustain safer behavior? It evaluates the whole operating process, and completion of individual tasks is only one input.

An organization can achieve 100% course completion and still have an immature program if its content is generic, its phishing simulations cover email only, its contractors are excluded, and its leaders never review behavior data.

A security culture assessment measures shared attitudes, norms, and perceptions about security. It asks whether employees believe reporting mistakes is safe, whether managers reinforce verification, and whether teams treat security as part of responsible work.

Culture is both an input and an outcome of awareness maturity, and the assessment does not replace the operating model that delivers training, phishing simulations, reporting workflows, and measurement.

A broader human risk management program includes the maturity model but extends beyond awareness training. It can combine phishing simulation behavior, training response, reporting activity, role-based exposure, credential risk, open-source intelligence (OSINT) exposure, and risky use of AI or SaaS tools.

End user security awareness training remains one part of that program, focused on building the knowledge, judgment, and habits people need to act safely.

Modern programs should connect maturity planning to security awareness training that measures behavior beyond completion. That connection keeps the framework practical by moving leaders from identifying a gap to assigning role-specific learning, running a relevant rehearsal, measuring the response, and repeating the cycle.

What Distinguishes Each of the Five Maturity Stages?

The five-stage progression provides a common language for describing program development. Each stage reflects how consistently the organization prepares its workforce, including employees, contractors, temporary workers, suppliers, and hybrid teams.

• 1. Nonexistent: The organization has no coordinated awareness program or cannot demonstrate who receives training. Security expectations differ by department, new hires receive inconsistent guidance, and third parties with system access may receive none. Improvement starts with ownership, population mapping, baseline policy, and a repeatable way to reach every workforce group.
• 2. Compliance-focused: The organization assigns required courses and tracks completion to satisfy an audit, contract, or internal policy. Content is usually periodic and standardized, while success is defined by attendance or completion percentages. Improvement starts by adding behavior-based objectives, realistic practice, reporting instructions, and metrics that show how people act under pressure.
• 3. Behavior-change: The program tests specific decisions and uses results to target follow-up training. Employees practice identifying suspicious requests, verifying payment changes, reporting phishing, protecting sensitive data, and challenging unusual authority claims. Training becomes more relevant when scenarios reflect job roles, communication channels, and current cyberattack patterns.
• 4. Culture-change: Security becomes a shared operating norm reinforced by managers, executives, onboarding, procurement, HR, and incident response. Reporting is treated as an early-warning capability, and employees do not experience it as an admission of failure. Leaders review trends by role and business process, while teams use lessons from phishing simulations and actual incidents to improve procedures.
• 5. Optimized: The organization continuously adjusts training and controls using timely evidence. Risk signals determine who needs practice, which scenarios require revision, and where workflows create avoidable pressure. The program evaluates outcomes across employees, contractors, temporary workers, suppliers, and hybrid workforces, while governance leaders use trend data to prioritize investment.

The stages are progressive, but they are not a race in which every organization must reach the optimized stage at once. A regulated financial institution with high-value payment workflows may prioritize role-specific verification for finance teams, while a growing technology company may need consistent onboarding and contractor coverage.

The right improvement depends on exposure, business processes, workforce structure, and the organization's ability to measure change.

NIST SP 800-50 Rev. 1, Building a Cybersecurity and Privacy Learning Program (2024), frames learning programs around behavior change and the development of a security and privacy culture. That framing supports a maturity approach because it moves the objective beyond course delivery toward repeatable decisions that reduce human-layer risk.

How Should Organizations Use Maturity Language Without Blaming Employees?

Maturity language should describe program conditions. An employee's worth, intelligence, or character sits outside its scope. "Compliance-focused" means the organization measures completion and leaves behavior largely unmeasured. It does not mean employees do not care about security, just as "behavior-change" does not mean every employee responds correctly to every scenario.

Employees often work within systems that shape their choices. A finance worker who receives an urgent payment request without a clear verification route faces a process problem as well as a training need. A temporary worker who never receives onboarding cannot be judged against an employee who has practiced the same scenario. A supplier without a reporting channel creates a coverage gap that program owners must address.

Use maturity language to assign action at the program level:

• Nonexistent: Establish ownership, define expectations, and map workforce coverage.
• Compliance-focused: Add behavioral objectives, realistic phishing simulations, and reporting metrics.
• Behavior-change: Reinforce manager participation and connect reporting data to workflow improvements.
• Culture-change: Expand measurement across suppliers, contractors, and hybrid teams.
• Optimized: Test whether the program adapts quickly as cyberthreats, roles, and technology change.

The model remains directional. A company can operate at different stages across departments, channels, or workforce populations. Its email training may support behavior change while its vishing and smishing preparation remains compliance-focused. A supplier program may be nonexistent even when employee onboarding is optimized.

Reporting one overall label hides those differences. Mapping maturity by population, channel, and risk gives leaders a clearer improvement plan.

A practical assessment asks five questions:

1. 1Who is covered?
2. 2What behavior is being taught?
3. 3How is that behavior rehearsed?
4. 4Which evidence shows improvement?
5. 5How quickly does the program respond when risk or work practices change?

Those questions help security leaders build a workforce that can recognize pressure, verify unusual requests, and report suspicious activity without fear of blame. The quality of that reporting behavior depends on whether the organization turns its findings into better workflows, targeted practice, and sustained reinforcement.

Why End User Security Awareness Training Maturity Matters for Human Cyber Risk

An end user security awareness training maturity model shows whether an organization is building safer decisions or merely recording course completions. Immature programs leave employees exposed to social engineering, phishing, spear phishing, BEC, ransomware, vishing, smishing, deepfakes, credential theft, and generative AI-enabled cyberattacks.

Mature programs turn cyberthreat signals into measured decisions, rapid reports, targeted interventions, and governance evidence.

Annual completion rates do not demonstrate reduced risk. They show that employees opened or finished assigned content. They say little about whether a finance employee challenged an urgent payment request, reported a suspicious voice call, rejected a credential prompt, or recognized an AI-generated executive video.

The immediate consequence is a false sense of control. The organization appears compliant while cyberattackers continue exploiting the gap between knowing a rule and applying it under pressure.

What Are the Business Consequences of an Immature Awareness Program?

An immature program increases the time between a cyberattack signal and a protective action. A suspicious email can sit unreported while an employee verifies a payment request through the same compromised channel. A stolen password can remain active while the security team lacks behavioral evidence showing who encountered the lure, who reported it, and who needs focused reinforcement.

The business impact compounds across attack types. Phishing can lead to credential theft, while spear phishing uses OSINT to make a message specific to its recipient. BEC targets payment authority and executive trust. Ransomware often begins with a user action that gives a cyberattacker access to an account, endpoint, or cloud service.

Organizations should rehearse the decision employees must make, provide an obvious reporting route, and measure reporting speed and accuracy alongside click behavior.

Voice and video cyberattacks create a separate maturity test because employees cannot rely on spelling errors or suspicious domains. In 2024, criminals used deepfake participants during a video call to persuade an employee at Arup in Hong Kong to transfer approximately $25 million to fraudulent accounts, according to CNN's May 2024 report.

Asking employees to become forensic analysts is the wrong defense. The practical control is mandatory second-channel verification for payment changes, confidential requests, password resets, and other high-impact actions.

AI-assisted impersonation also reaches public officials. In 2024, an individual posing as former Ukrainian Foreign Minister Dmytro Kuleba used an apparent AI-generated video call to contact U.S. Sen. Ben Cardin, according to NBC News.

The action path is to treat visual or vocal familiarity as one signal among several, then require an independently initiated callback or confirmation through a trusted directory before acting.

Generative AI accelerates cyberattacker preparation. It produces fluent messages, tailors pretexts to job roles, and creates convincing follow-up conversations at a scale that annual content updates cannot match.

Mature programs rotate scenarios across email, voice, SMS, collaboration tools, and video. They refresh training when a new cyberattack pattern appears, without waiting for the annual cycle.

The clearest downstream outcomes include unauthorized payments, exposed credentials, operational disruption, regulatory investigation, customer notification, and higher insurance scrutiny.

Leading indicators appear earlier, including the percentage of employees who report simulated cyberthreats, median time to report, verification attempts on high-risk requests, repeat susceptibility by attack type, and the time required to assign targeted reinforcement.

How Does Awareness Maturity Connect to Human Risk Management?

Awareness maturity becomes human risk management when employee behavior is treated as a changing risk signal and no longer as a yearly administrative record. A mature program connects the cyberthreat presented, the decision made, the report submitted, and the intervention delivered. That sequence shows whether risk is concentrated in a role, department, workflow, executive group, or attack channel.

The distinction between downstream outcomes and leading indicators matters. A successful BEC payment is a downstream outcome that arrives after multiple missed opportunities. A delayed report, repeated response to a vendor impersonation scenario, or failure to verify a voice request is a leading indicator that gives the security team time to intervene. Security leaders should act on those signals before they become an incident.

A useful maturity model evaluates five connected capabilities, separate from the five maturity stages:

• Signal coverage: The program tests email phishing, spear phishing, BEC, vishing, smishing, deepfake impersonation, credential theft, and generative AI-enabled lures. Testing email alone leaves much of that surface untested.
• Decision quality: Employees demonstrate whether they pause, verify, report, and protect information under realistic pressure.
• Response velocity: The organization measures the time from exposure to report, triage, containment, and targeted coaching.
• Risk-based intervention: Employees receive reinforcement tied to the behavior and channel that created exposure, without blame or public shaming.
• Executive visibility: Leaders receive trend data connecting human risk to business processes, financial authority, privileged access, and regulatory obligations.

This is why a human risk management program must combine behavior, exposure, and response data. A person who completes every module but repeatedly approves simulated invoice fraud presents a different risk profile from someone who misses one phishing simulation, reports the next three, and improves verification behavior.

Completion supplies context. Observed behavior supplies the evidence. That contrast is the practical difference between legacy awareness training and human risk management.

Maturity also shortens the intervention loop. A low-maturity program identifies an annual training requirement, assigns a course, and archives a completion report. A mature program detects a risky decision, delivers focused training, retests the behavior, and updates the employee's risk profile.

A failed phishing simulation should lead to coaching. Repeated practice continues until the safer response becomes easier to execute under pressure.

How Does Maturity Support Governance, Insurance, and Compliance?

Maturity converts security awareness from a training department metric into governance evidence. The NIST Cybersecurity Framework 2.0, published in 2024, emphasizes understanding, assessing, and reducing cybersecurity risk through organizational outcomes, continuous evaluation, and documented improvement.

A mature awareness program supports that approach by showing which human-layer risks were identified, which controls were applied, and whether measured behavior changed over time.

The same evidence strengthens audits and cyber-insurance discussions. A completion spreadsheet proves attendance. A mature evidence set can show assigned populations, role-based scenarios, phishing simulation results, reporting rates, time to report, remediation records, repeat-risk trends, policy acknowledgments, and management review.

Insurers and auditors can then assess whether the organization operates a recurring control or performs a once-a-year campaign.

Framework mapping should describe what the training and evidence support, without implying certification. A mature program can map awareness activities to the NIST Cybersecurity Framework through workforce awareness, risk assessment, detection, response, and improvement outcomes.

It can map training content and records to ISO 27001 control expectations, SOC 2 security criteria, HIPAA workforce security requirements, PCI DSS security awareness obligations, GDPR data-protection responsibilities, and CMMC awareness and training practices.

Cybersecurity awareness training compliance requirements differ by framework, and each one emphasizes different evidence. NIST CSF and ISO 27001 benefit from risk-based governance and continuous improvement records. SOC 2 requires evidence that controls operate consistently.

HIPAA requires workforce members to understand their responsibilities around protected health information. PCI DSS focuses on awareness appropriate to payment-card environments. GDPR connects employee behavior to lawful handling and protection of personal data. CMMC requires documented practices for protecting controlled information.

The action path is to create one evidence model that serves all applicable frameworks. Record who received training, why the content was assigned, which behavior was tested, what happened after the test, and how leaders reviewed the result. Retain that evidence according to the organization's audit, privacy, and insurance requirements.

An end user security awareness training maturity model ultimately answers a board-level question: Is the organization becoming harder to manipulate? The answer cannot come from completion rates alone. It comes from faster reporting, stronger verification, fewer repeat-risk behaviors, targeted intervention, and documented improvement across the human attack surface.

What Are the Five Stages of End User Security Awareness Training Maturity?

The end user security awareness training maturity model shows how an organization progresses from disconnected awareness activity to a continuously measured human-risk program. The dividing line between stages is whether training exists mainly to satisfy an obligation or to change decisions employees make under pressure.

Early programs track assignments and attendance. Mature programs track reporting behavior, risk signals, business-unit performance, and resistance across email, voice, SMS, and video. A culture-change or optimized program also distributes ownership among security, leaders, managers, HR, legal, and employees.

Use the stages as a diagnostic framework. A scorecard treatment hides more than it reveals, because an organization can operate at different maturity levels across departments, regions, and risk scenarios.

Stage 1: Nonexistent, Where Awareness Has No Operating Model

Stage 1 is the foundation phase in which security awareness training is absent or too fragmented to influence risk. Entry indicators include no accountable owner, inconsistent onboarding, no baseline measurement, and reactive communication after a phishing incident, policy change, or audit request.

Employees receive different guidance depending on when they join, which department they enter, or which security event has just occurred.

The exit condition is operational control. A polished curriculum can wait. A security leader must name an owner, define the employee population, document minimum expectations, and establish a baseline through a short assessment or controlled phishing exercise.

An initial cadence can remain simple: onboarding content for new hires, an annual policy review, and targeted communications when a material cyberthreat affects the organization.

Ownership often begins with IT, compliance, HR, or an individual security practitioner carrying the responsibility informally. Evidence at this stage includes a program charter, an employee inventory, an initial risk snapshot, and records showing that critical groups received the same core guidance.

The common failure mode is remaining reactive. An organization sends warnings after an incident, then mistakes visible communication for a functioning program.

Stage 2: Compliance-Focused, Where Completion Becomes the Primary Metric

Stage 2 remains part of the foundation phase, but the organization now delivers a recognizable program. Employees receive annual assignments, generic modules, policy acknowledgments, and deadline reminders. Administrators can report who completed training, who is overdue, and which departments satisfied an audit requirement.

The entry indicator is a recurring assignment process without meaningful behavioral feedback. Content is usually identical for a finance employee, software developer, executive assistant, and senior leader, even though their exposure to BEC, credential theft, data handling, and executive impersonation differs.

The exit indicator is a shift from completion-only reporting to risk-based priorities, role-specific content, and at least one measure of what employees do when confronted with a suspicious request.

The typical cadence remains annual or quarterly, with occasional phishing simulations added to satisfy policy expectations. Compliance, HR, or security owns delivery, while managers often remain outside the process.

Evidence includes completion rates, attestations, course timestamps, and audit exports. Those records prove participation. Retention and decision quality remain unmeasured.

The central failure mode is treating completion as protection. A full course roster can coexist with poor reporting behavior, weak verification of payment requests, and low recognition of vishing or smishing. Advancement requires evidence of what employees did when a realistic cyberthreat reached them.

Stage 3: Behavior-Change, Where Practice Produces Measurable Improvement

Stage 3 marks the transition from foundation to behavior. The organization assigns training according to role and measured risk, then gives employees repeated opportunities to practice recognition, reporting, verification, and escalation.

Effective programs combine phishing simulations with just-in-time reinforcement. A person who interacts with a simulated cyberthreat then receives a short lesson or coaching moment tied to the decision that person made.

A mature behavior-change program treats employees as a trainable defensive capability. Finance teams rehearse invoice fraud and BEC, executives practice verification against impersonation, and customer-facing teams encounter vishing or smishing scenarios relevant to their workflows.

A modern phishing simulations program can extend these exercises beyond email to voice, SMS, and deepfake video, allowing leaders to measure whether behavior holds across channels.

Entry indicators include role-based and risk-based training, recurring phishing simulations, and a defined reporting process. Exit indicators include measurable improvement in reporting rates, time to report, verification behavior, repeat susceptibility, and risk concentration by team. The cadence is monthly or continuous, with targeted reinforcement after a phishing simulation, policy change, or current cyberthreat.

Security awareness managers typically own the program with support from security operations, HR, and business-unit leaders. Evidence includes trend data, remediation records, phishing simulation performance, reported-threat quality, and documented reductions in repeat risky behavior.

The common failure mode is running realistic tests without teaching employees what to do next. A phishing simulation that produces embarrassment without building skill weakens trust and suppresses reporting.

Stage 4: Culture-Change, Where Secure Behavior Becomes Part of Work

Stage 4 is the advanced behavior phase. Security awareness is no longer a campaign owned only by the security team. It becomes a shared operating practice supported by leadership, managers, HR, legal, communications, and employees.

Psychological safety matters because employees must be able to report a mistake quickly without expecting blame. Fast reporting gives defenders more time to contain a compromised account, fraudulent request, or malicious message.

Entry indicators include executive support, cross-functional participation, employee feedback, and security champions who translate policy into department-specific habits. Exit indicators show that secure behavior is embedded into workflows.

Examples include payment verification in finance procedures, identity checks in help desk processes, reporting prompts in email and collaboration tools, and secure data-handling guidance inside project onboarding.

The cadence is continuous, supported by quarterly reviews of behavior and employee experience. Ownership is distributed. Security defines risk priorities, managers reinforce expectations, HR supports onboarding and role changes, and champions surface friction from the front line.

Evidence includes participation outside the security team, qualitative feedback, manager reinforcement, workflow adoption, and comparable performance across business units.

The common failure mode is declaring a culture initiative without changing operating conditions. Posters, slogans, and leadership messages do not create a security culture when employees lack time, clear escalation paths, or permission to pause urgent requests.

The program advances when secure decisions become easier to make inside normal work.

Stage 5: Optimized, Where the Program Continuously Adapts to Risk

Stage 5 is the optimization phase. The organization measures human risk continuously, runs multi-channel phishing, vishing, and smishing simulations, automates enrollment and reinforcement, and updates content as cyberthreats change, without waiting for an annual calendar.

Threat-informed content addresses the cyberattack methods employees actually face, including spear phishing personalized with OSINT, BEC, vishing, smishing, credential theft, and deepfake impersonation.

Entry indicators include human-risk scoring, automation, business-unit benchmarks, and a test-and-learn operating model. Exit indicators are not a perfect score or the disappearance of mistakes. They are faster detection of changing exposure, reliable improvement after intervention, and clear evidence connecting safer behavior to business priorities.

The cadence is continuous, and automation assigns targeted learning, launches follow-up phishing simulations, updates dashboards, and alerts owners when risk rises. Security leaders compare departments, roles, geographies, and attack channels without relying on one organization-wide average.

Ownership remains distributed, with executives reviewing trends and operational teams acting on them.

Evidence includes longitudinal human-risk scores, reporting quality, time to report, multi-channel simulation results, repeat-failure trends, business-unit benchmarks, and controlled experiments comparing training approaches.

Experimentation distinguishes activity from impact. A team can test whether shorter coaching, manager-led reinforcement, or a different phishing simulation sequence produces stronger reporting behavior.

The common failure mode is dashboard optimization without human insight. A program can generate sophisticated scores while ignoring whether employees understand the guidance or trust the reporting process.

An optimized program combines automation with employee feedback and threat intelligence, turning measurement into decisions that reduce exposure without making security harder to practice.

How to Determine an Organization's Current Maturity Level Using an End User Security Awareness Training Maturity Model

An end user security awareness training maturity model is useful only when it shows where human-risk controls work, where they fail, and what to improve. Assess governance, staffing, budget, audience coverage, content, phishing simulations, reporting, culture, accessibility, privacy, and evidence quality.

Score departments separately, validate each judgment against operational records, and treat the result as a planning tool with no claim to scientific precision.

1. Build a Maturity Assessment Worksheet

Create a worksheet with one row for each department and one column for each program dimension. Apply a five-level scale consistently:

• Level 1, ad hoc: The control is absent or reactive.
• Level 2, developing: Isolated activities exist.
• Level 3, defined: A documented and repeatable process operates.
• Level 4, measured: Outcomes are tracked and used for decisions.
• Level 5, adaptive: The program changes continuously in response to risk signals.

Do not assign a single organizational score at the outset. Finance may operate at Level 4 for BEC training while a recently acquired sales team remains at Level 1 for onboarding and reporting.

Averaging those results creates a reassuring number that conceals exposure. Record each department's score, confidence level, and supporting evidence.

Assess governance before reviewing activity metrics. Identify the executive owner, program manager, policy authority, review cadence, and escalation path for high-risk behavior. A mature program assigns written ownership across security, human resources, legal, privacy, and business leadership, and documents decisions so they do not depend on one administrator.

Evaluate staffing and budget. Record whether the program has dedicated personnel, instructional design support, analytics capacity, and funding for phishing simulations, translation, and accessibility. A large content library does not indicate maturity when nobody has time to analyze results or coach employees who need support.

Review the remaining dimensions in the worksheet:

• Audience coverage: Include employees, contractors, privileged administrators, executives, temporary workers, remote staff, and acquired teams. Record exclusions and the reason for each.
• Content quality: Check whether material is role-specific, current, brief, accessible, and relevant to phishing, spear phishing, BEC, vishing, smishing, deepfake attacks, and data handling.
• Onboarding: Measure whether training begins before or immediately after access is granted, and whether completion exceptions receive follow-up.
• Training cadence: Record annual requirements, just-in-time intervention, refresher frequency, and how quickly content changes after a new cyberthreat appears.
• Phishing simulation design: Examine channel coverage, scenario realism, role targeting, difficulty, consent controls, debriefing, and whether phishing simulations test reporting in addition to clicks.
• Reporting and intervention: Track who receives dashboards, how quickly security responds to reports, and whether employees receive targeted coaching after risky actions.
• Culture: Examine whether leaders model verification behavior, employees can report without shame, and managers treat mistakes as opportunities to build skill.
• Accessibility and language coverage: Verify captions, keyboard navigation, screen-reader compatibility, readable design, language support, and accommodations for different work environments.
• Privacy controls: Document data minimization, retention periods, access restrictions, employee notice, legal review, and the separation of coaching data from punitive employment decisions.
• Evidence quality: Rate whether records are complete, time-stamped, attributable, and reproducible across enrollment, completion, phishing simulation, reporting, and intervention activity.

Use the lowest score in a critical dimension as a priority signal, even when the department's overall profile looks mature. A department with excellent completion rates but no incident-reporting data has an evidence gap. Its profile does not describe a Level 4 program.

2. Validate Scores Against Operational Evidence

A worksheet captures the program team's view. Validation tests whether that view matches daily operations. For every score above Level 2, attach at least one artifact and one independent signal. This prevents policies from being mistaken for practices and completion percentages from being mistaken for behavioral change.

Review policies, annual plans, role matrices, training assignments, content revision records, phishing simulation templates, onboarding workflows, translation inventories, accessibility tests, privacy assessments, and leadership reports. Check dates and ownership. An old policy with no review history should not support a high governance score.

Compare those artifacts with operational data. Interview security administrators, help-desk staff, human resources partners, department managers, and a sample of employees.

Ask how suspicious messages are reported, who receives alerts, what happens after a failed phishing simulation, and whether employees understand when a request requires out-of-band verification. Interviews expose friction that dashboards often miss.

Connect the assessment to help-desk data, incident records, and identity-and-access signals. Look for password-reset spikes, repeated account-lockout events, suspicious forwarding rules, unusual privilege requests, impossible-travel alerts, risky authentication patterns, and access changes following onboarding or role transfers.

These signals do not prove that an employee caused an incident, but they show whether the program addresses the behaviors the organization needs to manage.

Phishing simulation results require the same discipline. Compare click, credential-entry, report, time-to-report, and repeat-failure rates by department, role, channel, and scenario type. A low click rate in email does not establish readiness for vishing or deepfake impersonation.

Review whether phishing simulations were representative, whether employees had a clear reporting route, and whether the organization acted on the results.

Compare a current state with a target state, and avoid declaring a department mature in the abstract. The NIST CSF 2.0 Organizational Profile guidance, published in 2024, recommends documenting current and target profiles side by side so teams can identify gaps and prioritize action.

Apply that method to human risk by recording the present capability, desired capability, evidence gap, owner, and review date. A human risk reporting framework can help organize those findings into department-level and executive views.

3. Document Audit-Ready Evidence Without Gaming the Score

Audit evidence should demonstrate that the program operates as designed and produces usable records. Build an evidence register that maps each maturity judgment to a source, date, owner, scope, and retention location.

Preserve enrollment exports, completion records, phishing simulation configurations, report volumes, intervention logs, training updates, exception approvals, accessibility checks, and privacy decisions.

Separate evidence of activity from evidence of effectiveness. A hypothetical 98% completion rate proves that an assignment was recorded. It does not prove that employees recognized a realistic impersonation attempt.

Stronger evidence combines completion with reporting behavior, time-to-report, repeat outcomes, incident trends, interview findings, and documented corrective action.

Protect employees while collecting evidence. Restrict individual-level data to authorized personnel, use department-level reporting for leadership where possible, and define how long behavioral records remain identifiable.

Never publish a league table that turns learning into humiliation. A mature culture makes reporting safe, explains how data is used, and directs coaching toward risky behavior without labeling the person.

Make audit records reproducible. Another reviewer should be able to determine which population was assessed, which version of the content or phishing simulation was used, what result occurred, who intervened, and whether follow-up happened.

Link the human-risk record to identity lifecycle events without expanding access to unnecessary personal information. Clear reporting routes and constructive responses keep employees engaged when a suspicious request reaches them. That engagement makes the evidence more useful and the control more durable.

4. Recalibrate After Material Change

A maturity score expires when the organization changes. Reassess after a confirmed incident, merger, acquisition, major workforce reduction, rapid hiring cycle, return-to-office shift, new administrator population, technology migration, or change in threat model.

AI-generated spear phishing, deepfake video, vishing, and smishing can create new exposure even when the underlying email program has not changed.

After an incident, preserve the relevant evidence, reconstruct the timeline, and identify which control failed, which signal was missed, and which intervention was unavailable.

Do not automatically lower every department's score. Lower the specific dimensions affected, such as executive verification, reporting workflow, onboarding, or phishing simulation coverage, and assign corrective owners and deadlines.

After a merger or acquisition, score the incoming workforce independently until identity, policy, training, reporting, and privacy controls are integrated. After major workforce change, verify that contractors, remote workers, and new managers remain covered.

After a threat-model change, redesign scenarios before treating the old maturity score as valid.

Repeat the assessment on a defined cadence and trigger interim reviews when risk changes. A higher score is not the objective. An accurate view matters more because it shows where employees have the skills, support, and reporting pathways to stop cyberattacks, and where they do not.

How to Move From End User Security Awareness Training to Behavior Change

An end user security awareness training program should measure safer decisions and treat completed courses as a secondary record. Replace annual cybersecurity awareness training with a continuous cycle of baseline testing, role-based practice, targeted intervention, manager reinforcement, and delayed retesting.

Keep compliance records as evidence of participation. They do not prove that employees can recognize and report an actual cyberattack.

1. Redesign the Learning Experience Around Repeated Practice

Replace the annual training event with a learning cycle tied to employee behavior. Begin with a baseline assessment that tests the channels and decisions most relevant to the organization.

Use controlled phishing simulation tests for email, followed by vishing simulation, smishing simulation, deepfake video scenarios, and scenarios involving AI-generated phishing emails. Ranking employees is not the goal. The exercise identifies where the organization's human defenses need practice.

The baseline should test observable actions:

• Can an employee verify an urgent payment request through a trusted channel?
• Can a manager identify a suspicious MFA push prompt and refuse to approve it reflexively?
• Can a new hire use the approved password manager and avoid reusing a personal password?
• Can a product team recognize when a generative AI request asks for confidential source code or customer information?

Build the curriculum around three behavior-science conditions: motivation, ability, and prompts. Motivation explains why a decision matters to the employee's role and the organization's mission.

Ability means the employee has a simple, practiced action available, such as a one-click phishing report button, a known number to call for verification, or an approved generative AI environment for sensitive work. Prompts deliver the reminder at the moment of risk, when it can still change the decision.

NIST's 2024 SP 800-50 revision recommends a life-cycle learning program that uses metrics and evaluation to encourage behavior change as part of risk management. Apply that model through a practical cadence: establish a baseline, assign targeted learning, test the behavior, review the signal, and adjust the intervention.

Risk-based enrollment keeps the cycle manageable. Assign finance employees to BEC and vendor-payment scenarios. Give executives and assistants impersonation and deepfake exercises. Train help-desk teams against credential-reset and MFA fatigue cyberattacks, and give developers practice with secure generative AI use.

New hires need foundational training during onboarding, while employees who handle sensitive data or approve transactions need more frequent practice.

Microlearning keeps each intervention usable. A two-minute lesson immediately after a risky action can explain why an AI-generated phishing email succeeded, show how to inspect sender context, and require the employee to make a safer choice.

A short module after an unsafe MFA approval should rehearse number matching, push-fatigue resistance, and out-of-band verification. A password-manager lesson should end with the employee storing a unique credential in the approved vault, which a video alone cannot accomplish.

Manager reinforcement makes the behavior visible in daily work. Managers should receive a short briefing when their team begins a campaign, a conversation guide after a pattern appears, and a team-level result that does not expose individual mistakes.

Their role is to normalize verification, reporting, and deliberate pauses during urgent requests. Employees also need to hear that quickly reporting a suspicious message is a successful security action, even when the message proves harmless.

2. Turn Phishing Simulation Results Into Targeted Interventions

Phishing simulation results become useful when each outcome triggers a specific action. A click, reply, credential submission, unsafe MFA approval, or failure to report requires a different intervention from a correctly reported message. Completion data alone cannot show whether an employee can perform the behavior under pressure.

Create intervention rules before launching the campaign. An employee who clicks an AI-generated phishing email should receive a short lesson on synthetic personalization and a retest within two weeks. An employee who responds to a vishing simulation should rehearse callback verification and executive impersonation cues.

An employee who interacts with a smishing simulation should practice checking shortened links, confirming unexpected delivery notices, and reporting suspicious texts through the approved channel.

Deepfake awareness training should focus on verification because employees cannot be expected to detect every deepfake. They can learn to slow down when a request involves money, credentials, confidential data, or unusual secrecy.

A deepfake video of an executive should trigger a known-channel callback and a second approver. A debate over whether the face appears natural wastes the window for action.

Use the same principle for AI-generated phishing emails. Generative AI removes traditional warning signs such as poor grammar, awkward tone, and obvious spelling errors. Employees should inspect the request, context, destination, and consequences. A polished message that asks for an unusual action remains high risk.

Interventions should be private, immediate, and proportionate. Do not send a public message that calls out an employee who failed a phishing simulation. Explain which signal the employee missed, demonstrate the correct action, and provide another opportunity to practice.

This approach protects psychological safety and increases the chance that employees will report mistakes and suspicious activity before a small error becomes a larger incident.

A 2023 case study co-authored by a NIST researcher, From Compliance to Impact: Tracing the Transformation of an Organizational Security Awareness Program, found that transformation requires organizational practices focused on workforce attitudes and behaviors, with completion rates playing a smaller part. Treat every phishing simulation result as diagnostic data, and keep routine failures out of disciplinary decisions.

Track behavior-level measures such as reporting rate, time to report, unsafe action rate, repeat failure rate, correct verification rate, and completion of just-in-time training. Segment results by role, department, attack channel, and risk context.

A lower click rate matters, but a higher reporting rate and faster escalation show that employees are actively helping the security team. That is why mature programs track phishing simulation metrics that go beyond click rates.

Phishing simulations across email, voice, SMS, and deepfake video allow security leaders to test the full human attack surface, which email measurement alone cannot cover. Rotate channels so employees build transferable judgment, because memorizing one campaign format teaches little.

3. Test Whether Improvement Persists After Reminders Pause

A mature program does not declare success immediately after employees complete a lesson. It tests whether the behavior survives when prompts, phishing simulations, and manager reminders pause. Schedule a delayed retest four to eight weeks after an intervention, using a new scenario built around the same underlying decision.

Change the surface details while preserving the behavior. After a finance employee practices invoice verification, test a vendor bank-account change. After a help-desk employee practices resisting MFA manipulation, test a caller claiming to be a senior executive.

After a developer learns secure generative AI use, test whether the employee recognizes sensitive data in a seemingly routine prompt.

Compare three signals across the baseline, immediate retest, and delayed retest:

• Unsafe action: Did the employee's risky behavior decline?
• Protective action: Did the employee select the correct response?
• Reporting speed: Did the employee report the event quickly?

Improvement that appears only immediately after training indicates recall. It does not establish durable behavior change.

Pause reminders for a defined period and observe whether performance decays. If reporting drops when prompts disappear, redesign the workflow so reporting is easier and more visible.

If one department retains the behavior while another regresses, examine manager reinforcement, workload, role exposure, and access to the correct tools before blaming employees.

Use periodic retesting to update risk-based enrollment. Employees who retain safe behavior can move to a lower-frequency maintenance path. Employees who repeat the same risky action should receive a different scenario, manager-supported coaching, and a workflow review.

Repeating identical modules teaches test recognition. Varied scenarios teach judgment.

Psychological safety must remain part of measurement. Report team-level trends to managers, protect individual results, and recognize useful reporting even when the event began with an employee mistake.

Leaders should ask, "What made the safe action difficult?" before asking, "Why did this happen?" That question surfaces confusing processes, excessive approval pressure, poor tool usability, and unclear escalation paths.

The final maturity test is operational. When phishing simulations and reminders pause, do employees still verify unusual requests, use MFA carefully, store passwords in the approved password manager, protect data in generative AI tools, and report suspicious activity?

If so, the organization is measuring behavior change. If not, the next cycle should address motivation, ability, or prompts before adding another annual training module.

Which Metrics Should a Mature End User Security Awareness Training Maturity Model Track?

An end user security awareness training maturity model compares participation with safer decisions at every stage. Completion rates show whether assigned training was finished, while behavior metrics show whether employees recognized and resisted realistic cyberattacks.

Completion measures delivery discipline, but click rate, credential-submission rate, report quality, and time to report measure defensive behavior. Risk, culture, and business-impact metrics add context through access criticality, incident involvement, remediation speed, and department-level risk movement.

Both categories matter. A mature program treats completion as a baseline control and draws evidence of changed behavior from what employees do across channels.

What Should a Balanced Security Awareness Scorecard Measure?

A balanced scorecard prevents security leaders from declaring success because one number improved while exposure remained unchanged. Organize the scorecard across five measurement layers, then review trends by department, role, attack channel, and risk tier. An organization-wide average hides those distinctions.

• Activity metrics measure program reach. Track assigned-training completion, overdue training, enrollment accuracy, phishing simulation frequency, module views, refresher attendance, and the percentage of employees receiving role-specific content. Completion provides evidence that required training was assigned and delivered, especially when the organization must produce training records for an audit. It does not measure effectiveness because an employee can finish a module without recognizing a convincing spear phishing message the next day.
• Behavior metrics measure decisions under pressure. Track phishing click rate, repeat-click rate, credential-submission rate, report rate, report quality, time to report, vishing response, smishing response, MFA adoption, password-manager usage, and risky AI-tool behavior. A falling click rate is useful, but a falling repeat-click rate provides stronger evidence that coaching reached employees who previously struggled. Report quality separates a useful malicious-email report from a high-volume stream of spam or harmless newsletters.
• Risk metrics measure exposure and consequence. Combine credential exposure, OSINT exposure, prior phishing simulation behavior, incident history, access criticality, privileged access, external-facing responsibilities, and risky browser or AI-tool activity into a privacy-aware employee or group risk indicator. Use the score to prioritize coaching and defensive review, and never to label an employee as inherently unsafe.
• Culture metrics measure whether employees participate in defense. Track reporting confidence, voluntary reporting volume, manager participation, repeat engagement, training retention, and the proportion of employees who report suspicious activity before a security team alert. A strong culture produces earlier, higher-quality signals without punishing good-faith reports.
• Business-impact metrics connect human behavior to operational outcomes. Track time to remediate reported cyberthreats, time to disable exposed credentials, incident involvement, cyberattacker dwell time, fraud attempts interrupted, privileged-account exposure, and department-level risk movement. CISA's 2025 Cybersecurity Performance Goals 2.0 adds a Govern function that underscores the role of organizational leadership in cybersecurity. The same executive accountability applies to human risk reporting.

The scorecard should show direction as well as a snapshot. A department with a 12% click rate that fell from 28% (illustrative figures) is moving faster than a department at 8% that has not improved in six months.

Report rate must be read alongside report quality and time to report, because more reports do not automatically mean better detection.

How Do Leading and Lagging Indicators Differ?

Leading indicators show whether the program is building safer habits before an incident occurs. They include module retention, report rate, report quality, time to report, MFA adoption, password-manager usage, and successful responses to vishing and smishing simulations.

Test retention through delayed scenario questions or repeated phishing simulations, because a completion certificate cannot demonstrate it. Risky AI-tool behavior, such as pasting sensitive data into an unauthorized generative AI service, is also a leading indicator because it exposes a control gap before a confirmed incident.

Lagging indicators show whether those habits translated into reduced harm. Track confirmed credential submissions, incident involvement, exposed-account remediation time, cyberattacker dwell time, BEC attempts involving employees, fraud escalation, and incidents requiring executive or legal response.

These measures deserve attention even when the numbers are low. Zero reported incidents can indicate strong controls, incomplete detection, or a culture in which employees do not report mistakes.

The most useful analysis connects both types. If report rates rise while time to report falls and credential-submission rates decline, the program is producing earlier defensive action.

If completion reaches 99% while repeat-click rate, risky AI-tool behavior, or incident involvement remains flat, the program is delivering content without closing the behavioral gap.

If one department's risk indicator improves after targeted training while another remains unchanged, adjust the scenarios, manager support, access controls, or training cadence before assigning more generic modules.

Phishing behavior should also be separated by channel. Email click rate does not necessarily reflect how an employee responds to a cloned executive voice, an urgent SMS request, or a deepfake video meeting.

A mature program measures email, vishing, and smishing response independently, then examines whether employees use an approved second-channel verification process. That distinction exposes blind spots an email-only scorecard hides.

How Should Organizations Combine Signals Into Privacy-Aware Risk Indicators?

An employee or group risk indicator should represent observable exposure and business context, and subjective judgments about the individual have no place in it. A practical model for human risk scoring combines six signal families: phishing behavior, training completion and retention, credential exposure, OSINT exposure, incident history, and access criticality.

Phishing behavior includes click, submission, repeat-failure, reporting, and response-verification patterns. Credential exposure identifies whether a work identity appears in a verified breach record or whether a simulated credential was entered.

OSINT exposure measures publicly available information that enables targeted impersonation, such as executive roles, direct contact details, travel schedules, or voice and video samples.

Access criticality changes the meaning of every other signal. A repeat click by an employee with no sensitive access requires coaching. The same behavior involving a payroll administrator, cloud administrator, treasury employee, or executive assistant demands faster review and tighter verification controls.

Group-level reporting reduces unnecessary individual scrutiny by showing trends for finance, human resources, engineering, executives, or contractors when that level is sufficient for action.

Scores should be normalized, time-bound, and explainable. Give recent behavior more weight than an old phishing simulation result, record which signals increased the score, and allow the score to decline after verified improvement.

Do not use sensitive personal characteristics, private communications, medical information, or unrelated browsing history. Employees should know which behavior categories are measured, why the data is collected, how long it is retained, and how they can challenge an inaccurate record.

A unified human risk reporting framework can present individual details to authorized security staff while giving managers aggregated department trends. That separation protects privacy while helping leaders identify where access reviews, targeted training, or stronger verification procedures will reduce exposure.

What Data Governance Safeguards Should Apply to Awareness Metrics?

Data governance determines whether measurement builds trust or creates surveillance risk. Collect only the fields required to answer a defined security question, restrict individual-level access through role-based permissions, encrypt data in transit and at rest, and establish retention periods for phishing simulation results, credential exposure, and incident records.

A security awareness manager may need a person's training status, while a business leader often needs only department-level risk movement.

Acceptable-use safeguards must cover AI and monitoring data. The organization should state whether risky AI-tool behavior is measured, which data categories trigger an alert, and whether the system records content, destination, metadata, or only a policy event.

The purpose is to stop sensitive-data exposure and route employees to coaching. Monitoring stays limited to security events and excludes unrelated personal activity. Separate security coaching from punitive performance management, except when documented misconduct or policy violations require an established investigation process.

Access controls should limit who can view raw phishing simulation events, credential exposure, OSINT findings, and incident history. Audit every access to sensitive reports, review permissions regularly, and aggregate dashboards by default.

Report trends with minimum group sizes where practicable so a small team cannot be identified through a supposedly anonymous chart.

Publish a measurement charter that defines each metric, its owner, collection method, review cadence, and action threshold. A mature program does not ask whether training was completed and stop there.

It asks whether employees recognize cyberthreats across channels, report them quickly and accurately, avoid unsafe data handling, and reduce the organization's measurable human risk over time.

How Leadership and Cross-Functional Partnerships Advance End User Security Awareness Training Maturity

End user security awareness training maturity advances when security becomes an operating practice owned by leaders, managers, and enabling functions. A campaign delivered by the security team alone produces a narrower result.

Executive sponsorship creates authority, while cross-functional ownership connects training to hiring, workflows, supplier access, communications, and business decisions. NIST's 2024 implementation examples make the same point: workforce cybersecurity and privacy learning prepares people to respond effectively while the organization governs the conditions that shape those decisions.

What Operating Model and Staffing Move a Program Forward?

A mature operating model gives every function a defined responsibility for human risk. The CISO sets risk appetite, sponsors the program with the executive team, and reports behavioral outcomes to the board.

The security awareness manager turns that direction into role-based curriculum, phishing simulations, measurement, and improvement cycles. IT provides identity, HRIS, collaboration, and access data, and keeps reporting channels and automated interventions operational.

HR owns the employee lifecycle. It brings security training into onboarding, role changes, leave, and offboarding, and helps define fair treatment when someone repeatedly fails a phishing simulation.

Learning and Development applies instructional design, accessibility standards, and learning measurement so modules work across abilities, devices, and learning preferences. Communications adapts messages to the organization's tone, translates material into employees' working languages, and explains the reason behind each behavior so warnings are understood and acted on.

Legal, Privacy, and Procurement keep the program defensible. Legal approves language for monitoring, phishing simulations, employee notices, and performance-review boundaries. Privacy reviews data collection, OSINT exposure, risk scoring, retention, and cross-border processing.

Procurement includes security awareness expectations in supplier and contractor agreements, then confirms that third parties receive training appropriate to the access they hold.

Finance validates payment-change controls and participates in BEC exercises. Operations identifies where verification steps could delay payroll, purchasing, customer service, or production, then helps design controls that protect those workflows without creating workarounds.

Business-unit leaders convert policy into local practice. They nominate security champions, approve realistic scenarios, and explain why a safe action matters to their teams. Managers reinforce the message during team meetings, review suspicious requests without blame, and make time for short learning activities.

Managers should not grade employees on phishing simulation results. Performance reviews should assess adherence to approved processes and participation in required learning only when HR and Legal approve that boundary.

Individual phishing simulation outcomes should guide coaching and access-risk decisions, without becoming a shortcut for punishment.

A clear governance forum prevents inconsistent changes. Representatives from Security, IT, HR, Learning and Development, Communications, Legal, Privacy, Procurement, Finance, Operations, and business units should approve material changes to content, phishing simulation themes, risk thresholds, intervention triggers, data use, and escalation paths.

NIST's 2024 Cybersecurity Framework implementation examples include employees, contractors, partners, suppliers, and other users in awareness and training activities. That scope means awareness training covers contractors, partners, and suppliers as well as employees.

Staffing must match the maturity target. A program manager cannot indefinitely write multilingual content, analyze risk signals, coordinate phishing simulations, manage vendors, and brief executives alone.

Mature teams assign ownership for curriculum, analytics, communications, privacy review, platform administration, and business engagement. Smaller organizations can combine duties, but they still need named deputies, documented approval paths, and protected time for the work.

Security champions extend reach into departments without turning employees into unpaid security analysts. A security awareness training program becomes sustainable when those ownership assignments are visible in operating plans. Responsibilities carried informally by one team do not survive growth.

Why Do Psychological Safety and Employee Trust Determine Adoption?

Psychological safety determines whether employees report suspicious messages early or conceal mistakes until damage spreads. A mature program treats employee reporting as an early warning capability and makes it the desired outcome, even when someone clicked first.

Security leaders should publish a simple promise: report quickly, receive help, and use the event to improve the process.

Trust requires visible consistency. If a reported email triggers immediate coaching and remediation without public criticism, employees learn that the reporting channel protects them and the organization.

If a deepfake or vishing simulation impersonates an executive, the follow-up should explain the verification behavior being practiced and treat everyone who responded with respect. Managers should model the same behavior by reporting suspicious requests and following out-of-band verification procedures themselves.

Accessibility and language coverage are trust controls. Treating them as cosmetic improvements understates their effect. HR, Learning and Development, and Communications should test whether employees with disabilities, limited connectivity, varied shifts, or different primary languages can complete and understand the material.

Contractors and suppliers need equivalent reporting instructions when their access creates comparable risk. The goal is consistent decision quality, which identical delivery does not guarantee.

How Should Culture Surveys and Maturity Indicators Guide Improvement?

Completion rates describe exposure to content. Decisions made under pressure are a separate measure. Culture measurement should combine survey responses with behavioral signals and operational evidence.

Ask whether employees know how to report a suspected phish, believe reporting is safe, understand when to verify an urgent request, and see managers practicing the expected behavior.

A useful quarterly dashboard tracks reporting confidence, time to report, repeat phishing simulation failure, manager reinforcement, language and accessibility gaps, contractor coverage, training completion by role, and changes in risk thresholds.

Segment results by business unit and job context, and avoid ranking individuals publicly. A finance team facing invoice fraud needs different indicators from a customer-support team handling account-recovery requests.

Survey results need an owner and an action loop. If employees say reporting takes too long, Operations and IT should reduce friction. If contractors lack access to training, Procurement should revise onboarding terms.

If managers rarely reinforce the process, business-unit leaders should add a short security behavior check to existing team routines. Each of those responses closes the loop the survey opened, which is what NIST's learning-program guidance means by planning, delivery, evaluation, and adjustment.

The strongest end user security awareness training maturity model treats culture as an operating capability and measures it. Leadership sets the expectation, cross-functional teams make it practical, managers make it visible, and employees receive the trust and tools needed to act. When those conditions are in place, human risk becomes measurable enough to direct investment where exposure is highest.

How End User Security Awareness Training Maturity Models Relate to NIST, ISO, CMMC, and Other Frameworks

An end user security awareness training maturity model shows how consistently an organization builds, measures, and improves employee security behavior. Control frameworks such as NIST, ISO 27001, CMMC, SOC 2, HIPAA, PCI DSS, and GDPR describe expected practices, safeguards, or outcomes. None of them prescribes a complete maturity journey.

NIST Cybersecurity Framework 2.0 Implementation Tiers classify broader cybersecurity risk management, while an awareness maturity model evaluates the human layer across governance, reach, realism, measurement, and improvement.

Compliance requirements establish what training and documentation must exist. Maturity determines whether the program is repeatable, risk-based, and capable of proving behavioral change.

How Do Awareness Maturity Stages Differ From Control Frameworks?

Awareness maturity stages and control frameworks answer different management questions. A framework asks whether the organization has a required practice or safeguard. A maturity model asks how consistently that practice operates, how well it is measured, and what happens when results expose a gap.

NIST Cybersecurity Framework 2.0 organizes cybersecurity risk management around Govern, Identify, Protect, Detect, Respond, and Recover. Its Implementation Tiers describe the rigor of enterprise-wide risk governance and management.

Those tiers answer a different question from whether an employee awareness program has progressed from annual training to continuous, role-based behavioral measurement. NIST's 2024 revision of SP 800-50 provides more specific guidance for building and managing a cybersecurity and privacy learning program, making it a stronger reference for workforce education than CSF Tiers alone.

The same distinction applies across other frameworks. The Department of Energy Cybersecurity Capability Maturity Model (C2M2) treats workforce management and situational awareness as separate domains, so an organization can document security roles and threat intelligence while still delivering inconsistent training.

ISO 27001 requires suitable competence and awareness activities within an information security management system, without defining a five-stage awareness maturity path. CMMC requires security awareness and training practices appropriate to the applicable level. SOC 2 generally requires evidence that controls operate as designed.

HIPAA requires workforce security awareness and training activities. The U.S. Department of Health and Human Services' 2024 guidance also identifies emerging social engineering techniques as relevant to HIPAA security programs.

PCI DSS Requirement 12.6 requires a formal security awareness program for all personnel, while GDPR Article 39(1)(b) places awareness-raising and staff training within the data protection officer's compliance-monitoring tasks. These requirements establish obligations. They do not prescribe how an organization should progress from basic delivery to measurable behavioral change.

A maturity model turns those obligations into an improvement sequence:

• Initial: Annual, identical courses with manual spreadsheets and limited risk analysis.
• Developing: Role-based assignments, completion tracking, onboarding workflows, and phishing simulations.
• Managed: Measurement of reporting behavior, repeat susceptibility, high-risk roles, and time to remediate.
• Optimized: Current cyberthreat signals, targeted refreshers, executive reporting, and documented program adjustments.

The labels can differ, but the central test remains consistent. Evidence must show movement from activity completion toward measurable risk reduction.

How Should Organizations Map Awareness Evidence to Compliance?

Evidence mapping should connect each requirement to an artifact, an accountable owner, a review cadence, and a measurable result. A training completion percentage proves enrollment or completion. It does not prove that employees received relevant instruction, that high-risk roles were covered, or that the organization acted on poor performance.

A practical evidence set includes:

• Governance: Approved policy, program scope, assigned ownership, risk assessment, and documented training frequency.
• Coverage: Workforce population, contractor treatment, role-based assignments, exclusions, language support, and onboarding records.
• Delivery: Course versions, assignment dates, completion records, attestations, and exception approvals.
• Validation: Phishing simulation results, reporting rates, repeat-failure trends, and corrective training.
• Improvement: Management reviews, risk-based changes, incident lessons, and evidence that overdue or high-risk users received follow-up.

Map these artifacts to the applicable control or requirement, and avoid claiming that a maturity score itself satisfies compliance. Training content mapped to security awareness training practices can support an audit-ready program.

The organization remains responsible for selecting applicable controls, retaining records, and demonstrating that procedures operate in practice. The strongest mapping also records limitations, such as a requirement that extends beyond awareness into access control, incident response, vendor management, or technical safeguards.

How Should Organizations Choose Among Vendor, NIST-Aligned, and Internal Maturity Models?

The choice should begin with evaluation criteria. A model's label or a vendor's reputation is a weaker starting point. Vendor models can provide recognizable security education concepts and can be assessed for reporting depth, content coverage, phishing simulation methods, integrations, and risk measurement.

A NIST-aligned model can improve terminology and traceability for organizations already using NIST publications. An internally developed model can fit unique roles, policies, and regulatory obligations, but it requires sustained ownership, calibration, and documentation.

Compare each option against five questions:

1. 1Does it distinguish completion from behavior?
2. 2Can it measure different channels, including email, vishing, smishing, and deepfake scenarios?
3. 3Does it connect findings to corrective action?
4. 4Can it preserve historical evidence and show trend lines?
5. 5Can governance, privacy, HR, and audit teams understand the scoring logic?

Avoid treating a vendor's maturity scale as an industry standard. A model is useful when its definitions are stable enough to support year-over-year comparison and specific enough to guide investment.

Organizations can use NIST-aligned terminology while retaining an internal scoring method. They can also use a commercial dashboard while documenting an independent crosswalk to ISO 27001, CMMC, HIPAA, PCI DSS, SOC 2, and GDPR obligations. That separation prevents marketing terminology from becoming the organization's risk methodology.

What Minimum Evidence Is Generally Needed to Document Required Training?

Minimum evidence should demonstrate that the organization identified the audience, delivered relevant training, recorded participation, handled exceptions, and reviewed whether the program addressed risk.

Keep records by person or workforce group, course and version, assignment and completion date, policy acknowledgment where required, manager or administrator ownership, and remediation for overdue or failed activities.

The evidence threshold rises when a requirement is risk-based or when an audit examines operating effectiveness. Add role-based rationale, onboarding and termination processes, contractor coverage, language or accessibility decisions, phishing simulation results, incident-linked retraining, and management review records.

Retain underlying reports, because a dashboard snapshot cannot explain how the result was calculated.

Maturity begins when those records become a management feedback loop. Leaders compare completion with reporting behavior, repeat susceptibility, high-risk role exposure, and time to corrective action, then change assignments or phishing simulations based on the findings.

That progression allows one evidence system to support compliance today while building the consistency and measurement required to advance on the end user security awareness training maturity model.

How Mature End User Security Awareness Training Programs Address AI-Powered and Non-Email Cyberthreats

An end user security awareness training maturity model reaches optimization only when it measures how employees respond across the channels cyberattackers use. Counting completions of an annual email course measures delivery only, which leaves response to voice, SMS, and video cyberthreats untested.

A program that tests email once a year leaves voice cloning, deepfake video, vishing, smishing, QR phishing, autonomous AI agents, and unsafe generative AI use outside its risk picture. CISA guidance on phishing emphasizes ongoing education, clear reporting paths, and exercises that prepare employees to act during a cyberattack.

How Does a Mature Program Build Multi-Channel Readiness?

Multi-channel readiness begins with the organization's threat model, and a fixed training calendar cannot substitute for it. Security leaders should identify which roles can approve payments, change bank details, access sensitive records, publish public content, or issue urgent instructions.

Finance teams and executives deserve priority because cyberattackers can combine public executive footage, supplier details, calendars, and organizational charts into a credible BEC sequence. The Arup deepfake fraud described earlier is the clearest example, and the World Economic Forum's 2025 account draws the same lesson about verification.

A September 2025 Gartner survey of 302 cybersecurity leaders found that 62% of organizations experienced a deepfake attack involving social engineering or exploiting automated processes.

Training should rehearse a verification rule: pause high-impact requests, independently call a known number, require a second approver, and report the attempt through a defined channel.

A mature program tests each relevant path with controlled scenarios:

• Voice and video: AI voice cloning, deepfake video, executive impersonation, and vishing simulations tied to payment or credential requests.
• Mobile and digital lures: Smishing, QR phishing, malicious calendar invitations, and prompts that send employees to unfamiliar login pages.
• Human interaction: Social engineering through help desks, recruiters, vendors, contractors, messaging platforms, and personal accounts.
• Work location: Scenarios designed for remote and hybrid employees who cannot confirm a request by walking to a colleague's desk.

Every phishing simulation should produce an action signal, such as whether the employee paused, verified, reported, or shared information. A security awareness training program with multi-channel phishing simulations can connect those signals to targeted follow-up training, because employees differ in exposure and need different practice.

How Should Programs Govern Generative AI and Shadow-AI Behavior?

Generative AI creates a second maturity test because employees can introduce risk without receiving a phishing message. An employee who pastes a customer record, source code, contract, résumé, medical detail, or acquisition plan into an unauthorized AI tool can expose sensitive information through ordinary work activity.

Autonomous AI agents add another concern. An agent with access to email, files, calendars, or purchasing systems can act at machine speed when a user grants excessive permissions or accepts an unsafe instruction. Organizations can also assess agent permission, review, and approval controls with an AI governance maturity model, which scores them on the same stage-by-stage basis.

The response must combine governance with skill-building. Organizations should publish approved-tool rules, classify data employees must never paste into public AI services, require review before AI agents send messages or alter records, and provide a simple reporting route for suspected exposure.

Training should use safe demonstrations that show how a harmless-looking prompt can reveal confidential data. Simulations can test whether employees recognize an unapproved tool, refuse to upload restricted information, and report the event without fear of blame.

Clear rules turn an unfamiliar AI risk into a repeatable decision employees can apply under pressure.

Maturity also requires visibility into shadow-AI behavior without making surveillance the program's central message. Security teams should measure policy-relevant signals, such as use of an unapproved application, attempted upload of restricted data, or repeated acceptance of high-risk permissions.

Those signals should trigger a short explanation, a corrective module, or a manager-supported coaching path. Employees contribute most to defense when the organization gives them clear boundaries and a safe way to ask for help.

What Makes Human-Risk Signals Privacy-Preserving?

Privacy-preserving measurement separates useful risk intelligence from intrusive employee monitoring. Programs should collect only the information needed to identify exposure, restrict access by role, define retention periods, and report trends at team or department level whenever individual identification is unnecessary.

A risk score should explain which behavior produced it, what action lowers it, and when the signal expires.

Optimization also requires coverage that a completion dashboard cannot provide. The model should account for OSINT exposure, credential exposure, role sensitivity, access to critical assets, phishing simulation behavior, reporting speed, and unsafe AI activity.

It should also include contractors, temporary workers, new hires, remote employees, workers with disabilities, and employees who need training in languages other than English.

If an employee cannot hear a vishing example, read a phishing simulation, or understand the reporting instruction, the measurement reflects program design failure, and employee capability remains untested.

Accessible training gives every employee a fair chance to recognize, verify, and report a cyberthreat.

This approach produces a maturity model that prioritizes the cyberthreats most likely to cause business impact. An executive with extensive public video, a finance employee authorized to release funds, and a contractor with access to production data should not receive the same sequence as an employee with limited system access.

Mature programs map those differences to scenario frequency, phishing simulation realism, reporting expectations, and governance controls. They stop treating annual completion as optimization and start proving that employees can recognize, verify, report, and contain cyberthreats across the channels that matter.

How to Build an End User Security Awareness Training Maturity Model Roadmap and Business Case

Build an end user security awareness training maturity model roadmap by establishing a measurable baseline, choosing the next achievable stage, and sequencing improvements across 90 days, six months, and 12 months.

Convert each stage transition into evidence that connects human risk to reduced exposure, faster reporting, lower analyst effort, stronger compliance evidence, and defined return-on-security-investment assumptions. Advance one stage at a time, and recalibrate the roadmap quarterly as cyberthreats, staffing, and risk appetite change.

1. Establish the Baseline and Select the Next Maturity Stage

Document how the current program operates before requesting more budget. Assess training completion, phishing simulation performance, reporting behavior, incident response time, content coverage, department-level exposure, ownership, integrations, and the time analysts spend reviewing employee-reported messages.

Separate activity metrics, such as completion rates, from outcome metrics, such as faster reporting and reduced manual investigation time.

Use the first 90 days to create the baseline and test it with a defined pilot group. Select employees from finance, executive support, IT, sales, and one operational department because their threat profiles differ.

Include email, spear phishing, BEC, vishing, smishing, and deepfake scenarios where those channels exist in the organization's threat model. The purpose is to identify which behaviors and workflows require investment.

Choose the next maturity stage based on risk appetite, and avoid selecting a stage merely to appear advanced. An organization facing frequent payment fraud attempts should prioritize finance approvals, executive impersonation, and out-of-band verification.

A regulated healthcare organization may prioritize data handling, incident reporting, and compliance evidence. The 2024 NIST Cybersecurity Framework 2.0 places governance and enterprise risk context at the center of cybersecurity decision-making, giving leaders a defensible structure for connecting awareness priorities to business exposure.

2. Sequence the 90-Day, Six-Month, and 12-Month Roadmap

A credible roadmap turns maturity goals into time-bound operating changes, and a general promise to improve awareness is not such a change. Assign an executive sponsor, program owner, technical administrator, HR or learning partner, and measurement owner before implementation begins.

Estimate staffing and cost from the work required for content creation, phishing simulation design, administration, employee support, reporting, and analyst workflow changes.

• First 90 days: Run the baseline assessment, define the target stage, select pilot groups, configure identity and HR data, and establish the initial dashboard. Prioritize the two or three attack paths most likely to create material loss. Deliver short, role-based training, run controlled phishing simulations, and document reporting and response times. Hold a 30-day operating review and a 90-day governance checkpoint before scaling.
• By six months: Expand the program to additional departments, connect relevant HRIS, identity, email, ticketing, and GRC data, and introduce automated enrollment for higher-risk employees. Add content for voice, SMS, deepfake, vendor impersonation, and executive fraud where the threat model supports it. Compare results with the baseline, calculate avoided analyst effort, and present the first board-ready progress report.
• By 12 months: Operate continuous phishing simulations and targeted training across the organization, maintain documented ownership, and integrate evidence into quarterly risk reviews. Reassess staffing, content priorities, and technology costs using observed behavior. Set the next maturity target only after the organization demonstrates sustained improvement across multiple reporting periods.

Technology should reduce measurement friction. Another isolated dashboard adds to it. A platform that connects training, phishing simulation, reporting, risk scoring, and remediation can support board-ready security awareness reporting without forcing analysts to reconcile separate spreadsheets.

3. Prioritize Investments by Threat Model and Risk Appetite

Threat modeling determines what the program must rehearse first. Map each high-impact business process to the people, channels, decisions, and controls involved.

A payment process, for example, might require invoice verification, callback procedures, dual approval, and training for finance staff and executive assistants. A sensitive product-development process might require data-handling instruction, insider threat awareness, and controls for unauthorized tools that accept company data.

Risk appetite determines how much residual exposure leadership will accept. A low-tolerance organization might require near-term verification controls for all high-value transfers, while an organization with higher tolerance may accept a longer remediation window for lower-impact departments.

State these decisions in measurable terms, such as maximum time to report, minimum participation in verification drills, or a target reduction in risky phishing simulation actions. This gives the board a clear choice between funding a control and accepting a defined level of exposure.

4. Compare Departments Without Creating Unhealthy Competition

Department comparisons should show where support is needed. Ranking departments publicly turns reporting into a competition. Normalize results by role, channel, scenario difficulty, and exposure to phishing simulations.

A finance team that reports more suspicious messages than another department might be encountering more targeted tests, so a higher volume of reports does not indicate worse performance. Conversely, a low click rate combined with very low reporting can indicate passive behavior that resembles resilience.

Use consistent measures across departments, including reporting rate, time to report, repeat-risk rate, training completion, incident response time, and analyst handling time. Share aggregate trends with managers, and restrict individual-level information to people with a legitimate operational need.

Investigate unusual improvements before celebrating them because underreporting, incomplete integrations, or reduced phishing simulation frequency can create false progress.

5. Define Stage-Transition Criteria and Build the Board Case

A maturity stage should change only when the organization meets pre-agreed criteria over a sustained period. Define transition gates for operating consistency, behavioral outcomes, coverage, governance, and evidence quality.

Advancement might require stable reporting workflows, measurable improvement in time to report, coverage of priority attack channels, quarterly executive review, and reliable data from HR and identity integrations.

The business case should translate those gates into financial and operational terms. Present current exposure, the proposed investment, expected risk reduction, reporting behavior improvements, faster incident response, avoided analyst hours, compliance evidence produced, and the assumptions behind security awareness training ROI.

Do not claim that training prevents every breach. State how the program reduces exposure to defined attack paths, increases the probability of early reporting, and lowers the effort required to investigate employee-reported cyberthreats.

Include a sensitivity range for every major assumption. Show conservative, expected, and optimistic outcomes for analyst time saved, incident response acceleration, and reduction in repeat risky behavior. The board can evaluate the investment against accepted risk without any promise of complete protection.

Revisit the model quarterly, promote the next stage only when evidence supports it, and use each review to set priorities that employees and security teams can act on during the following 90 days.

How the End User Security Awareness Training Maturity Model Connects to Human Risk Management

An end user security awareness training maturity model connects training activity to human risk management because completion rates cannot show whether people, identities, and business processes remain exposed.

The NIST Cybersecurity Framework 2.0, published in 2024, treats cybersecurity as an organization-wide risk management discipline, and its scope is broader than that of a narrow technical function. Mature programs combine learning activity with observed behavior, access context, and business impact, then adapt those measures to the organization's threat profile.

Why Do Mature Programs Need Unified Risk Signals?

Human risk becomes actionable when security teams connect signals that are often separated across awareness, identity, incident response, and governance systems. A failed spear phishing simulation means something different for a contractor with read-only access than for a finance executive who can approve payments. The intervention should reflect that difference.

A mature program links awareness content and observed behavior with identity and access context, credential exposure, OSINT exposure, incident history, threat intelligence, and business criticality.

An employee who repeatedly enters credentials into phishing simulations, appears in a credential breach, has a highly visible public profile, and holds privileged access requires faster, more targeted attention than an employee with one isolated training lapse.

Security teams should not treat awareness as a yearly content assignment.

They should use a human risk management program to connect signals, prioritize people and roles, assign focused practice, and measure whether exposure declines.

Ten human risk dimensions provide a useful starting point for risk scoring:

• Susceptibility to phishing and social engineering
• Reporting quality and speed
• Credential hygiene
• Access sensitivity
• Public and OSINT exposure
• Policy adherence
• Response speed
• Resilience across email, voice, and SMS channels
• AI-tool behavior, including unsafe data sharing
• Recovery behavior after a mistake or incident

Organizations should adjust these dimensions to their threat model. A healthcare provider may emphasize patient-data handling and recovery behavior, while a financial institution may assign greater weight to payment authority, BEC exposure, and approval workflows.

Which Downstream Measures Show Whether Human Risk Is Falling?

Maturity becomes visible in operational outcomes, and quiz scores or phishing simulation completion capture only part of it. Security leaders should track whether employees report suspicious messages faster, whether analysts spend less time validating routine reports, whether compromised credentials are revoked sooner, and whether high-risk access is reviewed before an incident escalates.

Cyberattacker dwell time is one useful downstream measure. If an employee clicks a malicious link but reports it within minutes, the organization has a narrower window for credential theft, lateral movement, or data loss than if the event remains undiscovered for days.

Other measures include time from report to containment, repeat susceptibility by channel, privileged-account exposure, recovery completion, and the number of incidents in which an employee report enabled early detection.

These measures expose operating-model gaps without assigning blame. A high reporting rate with slow triage points to a response bottleneck. Fast reporting with repeated credential reuse points to an authentication or workflow problem. Strong email performance with poor vishing resilience shows that the program concentrates on one channel.

How Can Organizations Avoid Punitive Human-Risk Scoring?

Human-risk scoring should guide support. A permanent employee reputation score is a different instrument. Scores should be transparent, limited to legitimate security purposes, protected by strict access controls, and reviewed for accuracy.

They should also expire or improve as behavior changes because a current risk signal is more useful than an old failure preserved indefinitely.

Security leaders should score behaviors and exposure conditions, and leave personal traits out of the model. A failed phishing simulation should trigger coaching, a safer workflow, or additional verification practice, and never public ranking or automatic disciplinary action.

Employees need clear explanations of what is measured, why it matters, and how to reduce their exposure.

The strongest maturity models combine accountability with recovery. They recognize employees as active sensors who can interrupt cyberattacks, report anomalies, and improve through practice. That shift gives security leaders a defensible way to connect daily behavior with business exposure and direct resources where they reduce risk fastest.

End User Security Awareness Training Maturity Model FAQs

What Does an End User Security Awareness Training Maturity Model Measure?

An end user security awareness training maturity model is a framework for assessing how consistently an organization prepares, measures, and improves secure employee behavior. It evaluates governance, audience coverage, training design, phishing simulations, reporting, intervention, culture, and evidence, and it treats completion as one input among several.

The model applies to employees, contractors, temporary workers, and relevant suppliers across office, remote, and hybrid environments. NIST describes security awareness and training as an ongoing lifecycle in SP 800-50 Revision 1.

Use the model directionally to identify gaps, set a practical target, and document progress without assigning blame to employees or presenting one score as a certification.

Which Five Stages Does an End User Security Awareness Training Maturity Model Define?

The five stages are nonexistent, compliance-focused, behavior-change, culture-change, and optimized. Nonexistent programs lack ownership and baseline evidence. Compliance-focused programs assign periodic training and track completion, but rarely measure behavior.

Behavior-change programs use role-based content, phishing simulations, reporting data, and targeted reinforcement. Culture-change programs add leadership sponsorship, psychological safety, manager participation, and secure behavior embedded in workflows. Optimized programs continuously combine threat intelligence, multi-channel testing, human-risk signals, automation, and experimentation.

NIST SP 800-50 Revision 1 describes an iterative learning lifecycle. Organizations adopting this model can score dimensions separately and prioritize the weakest risk-relevant capability without averaging away material gaps.

How Often Should Security Awareness Training and Phishing Simulations Occur at Each Maturity Level?

Security awareness training should be continuous, with cadence increasing as the program develops. Provide baseline onboarding and essential annual training at the nonexistent stage, and add annual or quarterly training plus occasional phishing simulations at the compliance-focused stage.

Deliver monthly or continuous learning and regular phishing simulations at the behavior-change stage, reinforce behavior continuously with quarterly reviews at the culture-change stage, and continuously test channels and roles at the optimized stage.

These are operating targets, and no universal regulatory frequency applies. NIST's SP 800-50 Revision 1 frames awareness as an iterative lifecycle. Adjust frequency for threat exposure, workforce turnover, job role, phishing simulation results, and employee trust.

What Evidence Is Needed to Validate an End User Security Awareness Training Maturity Score During an Audit?

An audit-ready maturity score requires documented program ownership, scope, policies, training assignments, completion records, content versions, onboarding evidence, phishing simulation plans, results, reporting workflows, intervention records, and trend analysis.

Add role and department coverage, contractor treatment, exception approvals, accessibility and language controls, privacy safeguards, incident links, and management review. The NIST Cybersecurity Framework 2.0 provides governance and risk-management context for organizing this evidence.

Validate self-assessments with interviews, sampled records, help-desk data, incident tickets, and observed reporting behavior. Preserve dates, responsible owners, decision rules, and remediation status so an auditor can reproduce the score and distinguish activity from outcomes.

How Long Does It Take to Move From Compliance-Focused Training to Behavior-Focused Security Awareness?

Moving from compliance-focused training to behavior-focused security awareness typically takes roughly 12 months in total: a baseline and pilot in the first 90 days, targeted reinforcement through month six, and sustained improvement demonstrated by month 12 across roles and departments.

The timeline depends on audience size, data quality, ownership, integration, workforce turnover, and the starting level of trust. Establish a baseline, test role-specific scenarios, measure reporting and repeat-risk behavior, deliver just-in-time interventions, and retest after reminders pause.

NIST's SP 800-50 Revision 1 emphasizes continuous improvement over a one-time program launch. A documented roadmap converts completion data into measurable behavior change and gives leaders a defensible basis for sustained investment.

Turn Awareness Maturity Into Measurable Behavior Change

Completion tracking alone cannot show whether employees can recognize, report, and resist evolving social-engineering cyberthreats. A modern human-risk program connects targeted learning and observed behavior to clearer priorities, faster intervention, and stronger audit evidence. Assess awareness maturity with Adaptive Security.