A common mistake is treating pass or fail rates as the only meaningful metric. That misses whether the same people keep failing, whether risky behaviour concentrates in sensitive roles, and whether simulations lead to better reporting and verification habits. Strong programmes measure behavioural change, not just test outcomes, and compare results with identity and threat signals.
Why This Matters for Security Teams
Vishing simulation results are often reported like a simple exam score, but that framing hides the operational risk. A single failure rate does not show whether the same employees are repeatedly targeted, whether high-trust roles are more exposed, or whether staff are learning to verify requests before acting. That matters because voice phishing is usually a precursor to credential theft, help desk abuse, or account takeover, not an isolated awareness issue. NIST’s control baseline for security awareness and training in NIST SP 800-53 Rev 5 Security and Privacy Controls supports measuring whether training changes behavior, not just whether users can answer a test correctly.
NHIMG’s analysis of Caesars Entertainment Breach 2023 and MGM Resorts Breach 2023 shows how social engineering becomes a foothold for broader identity compromise, which is why simulation outcomes should be tied to reporting speed, escalation quality, and identity verification habits. In practice, many security teams discover the real weakness only after a successful phone-based impersonation has already reached a privileged workflow.
How It Works in Practice
Useful measurement starts by separating exposure, response, and recovery. Exposure tells you who was successfully persuaded to share information or follow an unsafe instruction. Response tells you whether the person reported the attempt, challenged the caller, or asked for out-of-band verification. Recovery tells you how quickly security staff contained the event and whether the interaction was correlated with sensitive access paths.
Security teams should segment results by role, location, call path, and privilege level rather than using one organisation-wide pass rate. If finance, service desk, or executive support staff have a materially different outcome pattern, that is a control signal, not just a training metric. Best practice is evolving toward measuring whether employees used verification steps such as call-backs, approved internal directories, and ticket-based approvals. Those behaviours matter more than whether they “knew the answer” during a simulation.
Telemetry also matters. A simulation is stronger when it is compared with identity and threat signals such as suspicious login attempts, help desk reset requests, or unusual MFA prompts. That makes it easier to see whether voice social engineering is acting as a pretext for account takeover. NHIMG’s Ultimate Guide to Non-Human Identities is relevant here because identity programmes increasingly need to link human response patterns with downstream access risk, not treat awareness in isolation. Where possible, organisations should preserve trends over time and compare repeat failures, reporting latency, and escalation quality across simulations.
- Track repeat susceptibility, not just first-time failures.
- Measure reporting time and escalation accuracy.
- Break results down by privileged and high-contact roles.
- Correlate simulation outcomes with identity events and help desk actions.
These controls tend to break down when simulations are run as one-off campaigns with no linkage to identity telemetry, because the programme can show “learning” on paper while the same social engineering path remains open operationally.
Common Variations and Edge Cases
Tighter measurement often increases analyst workload and reporting complexity, requiring organisations to balance behavioural insight against the cost of collecting and reviewing more detailed data. There is no universal standard for this yet, so programme owners should be clear about what they are optimising: awareness, reporting, or attack resistance.
Call centres, executive support teams, and field operations can distort results because those groups legitimately handle unusual requests and may be trained to be helpful under pressure. In those environments, a “fail” may reflect role ambiguity rather than poor security judgment. The better question is whether the person verified the requester before taking action and whether that verification was proportionate to the request.
Metrics also need careful handling after a real incident or high-profile campaign. A temporary spike in reporting can look like success even if the organisation has not changed its verification habits. Current guidance suggests combining simulation data with control evidence, such as ticketing logs, callback procedures, and privileged access reviews, rather than using a single awareness dashboard to claim maturity. That is especially important when simulations intersect with identity-heavy workflows, where one persuasive call can trigger password resets, MFA rebinds, or account recovery.
Security teams get the most value when they treat vishing as a behaviour-and-identity problem, not a quiz score. The programme should show whether people slow down, verify, and escalate under pressure, because that is what interrupts the attack path.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST SP 800-63 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.AT-1 | Vishing simulations should measure whether training changes user behaviour. |
| NIST SP 800-63 | Identity proofing and authentication flows are often targeted through vishing. | |
| OWASP Non-Human Identity Top 10 | NHI-08 | Voice social engineering often precedes credential abuse and account takeover. |
| OWASP Agentic AI Top 10 | Agentic workflows can amplify social engineering impact through automated actions. | |
| NIST AI RMF | Risk measurement should include behavioural and operational impacts, not only test outcomes. |
Track reporting speed and verification habits as evidence that awareness training is changing actions.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 27, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
