It breaks the link between behaviour and business exposure. A click rate can show that an employee interacted with a lure, but it cannot show whether that identity has privileged access, which systems are at stake, or whether the event meaningfully changes risk. Benchmarking becomes credible only when behaviour is joined to identity and threat context.
Why phishing-only benchmarking breaks the metric
Phishing metrics are useful as one signal, but they are too narrow to stand in for employee cyber risk. A click rate measures interaction with a lure, not the employee’s authority, the systems they can affect, or the business loss that could follow. Once benchmarking is reduced to that single number, the organisation stops measuring exposure and starts measuring a training exercise.
The core problem is that phishing outcomes are event-level, while cyber risk is exposure-level. Two employees can produce the same click result and still represent very different risk profiles if one has access to finance systems, source code, customer data, or administrative tooling. A benchmark that ignores that difference will systematically understate some risks and overstate others.
Phishing-only scoring also hides the distinction between curiosity, error, and actual compromise. An interaction with a lure may matter, but its significance depends on whether it led to credential submission, token theft, session abuse, or access to a privileged account. Without that context, the benchmark cannot support an accurate security decision.
What a credible employee cyber risk benchmark must include
Credible benchmarking joins behaviour to identity and threat context. That means the metric needs to reflect who the person is, what they can reach, and what would happen if the interaction became a real incident. In practice, that often means weighting phishing outcomes alongside privilege level, role criticality, data access, and the sensitivity of connected systems.
This is where identity-aware analysis matters. Insider Threat and Identity Guide is relevant because privilege misuse and leaver risk are part of the exposure picture, not an afterthought. A low click rate does not make a high-privilege identity safe, and a higher click rate does not automatically mean the same level of business danger across the workforce.
For organisations that want a stronger benchmark, the useful question is not “who clicked?” but “which identities created the most credible path to material impact?” That shifts the measure from awareness performance to risk relevance. It also makes benchmarking comparable across teams without pretending that every employee has the same blast radius.
The same logic applies when phishing is used as a route to secret or token theft. Mailchimp breach 2022 shows how social engineering against staff can become a business exposure problem once support tooling and customer-facing secrets are involved. A benchmark that stops at the click misses the control failure that actually matters, which is access enabling impact.
How to interpret phishing metrics without over-trusting them
Phishing data still has value, but only as one input into a broader risk model. It is best used to identify behavioural weakness, test training effectiveness, and spot patterns that may deserve deeper review. It should not be treated as a universal ranking of employee danger, because it does not capture role sensitivity, access paths, or whether the account is tied to high-value systems.
Benchmarking improves when the organisation separates exposure from behaviour. Behaviour tells you who may be susceptible; exposure tells you what that susceptibility could affect. Once those are combined, leaders can distinguish an employee who is a training priority from an identity that is a genuine business-risk priority.
Phishing metrics also need careful interpretation at scale. In a large organisation, a small change in click rate may be less important than a single click by a user with privileged access or access to sensitive credentials. The benchmark should therefore be calibrated to impact potential, not just raw volume of mistakes.
For practitioners, a better benchmark is one that can answer whether the organisation is reducing the chance of meaningful compromise, not merely reducing the number of people who click on simulations. That distinction is what makes the measure operationally useful rather than cosmetically reassuring.
Risk and Threat Considerations
Phishing-only benchmarking creates a false sense of precision. It can make a workforce look safer than it is by ignoring privilege, session impact, downstream system access, and whether a lure interaction actually creates an attack path.
Failure mechanism: A single click metric collapses different identities, roles, and access levels into one behavioural score, so the organisation cannot distinguish nuisance exposure from material compromise risk.
Impact: Teams may prioritise training changes over access controls, miss high-value identities that need closer protection, and underreact to the employee groups most likely to turn a phishing event into a real incident.
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 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-2 — Identification and Authentication (Organizational Users) | Phishing risk changes when employee identity and access matter to impact. |
| AC-6 — Least Privilege | Privilege level determines whether a phishing event can become material exposure. | |
| AU-6 — Audit Review, Analysis, and Reporting | Benchmarking needs analysis that connects events to business-relevant exposure. | |
| Recommendation — Map phishing results to user access levels before judging organizational risk. Prioritise least-privilege reductions for accounts whose compromise would matter most. Correlate phishing events with audit data to distinguish noise from real risk. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | The answer centers on overprivilege and why raw interaction metrics miss impact. |
| Recommendation — Reduce standing privilege for identities that can turn phishing into material exposure. | ||
| NIST CSF 2.0 | ID.AM-01 — Physical devices and systems inventoried | Accurate benchmarking depends on knowing which systems and identities are exposed. |
| Recommendation — Inventory high-value systems and map phishing exposure to the identities that reach them. | ||
Practitioner Guidance
What to prioritise: Tie phishing results to identity context first, then decide whether the issue is awareness, privilege, or downstream system exposure. If a low click rate coexists with privileged access, treat that as a governance signal, not proof of low risk.
What to measure: Track whether phishing outcomes correlate with access to sensitive systems, credential reuse, token exposure, or privileged workflows. That gives you a benchmark that can distinguish behavioural weakness from actual organisational exposure.
Common mistake: Treating the simulation score as the risk score. The score tells you who interacted with the lure; it does not tell you whether the interaction mattered to the business.
Practitioner takeaway: Use phishing metrics as evidence of susceptibility, not as a substitute for cyber risk benchmarking. The benchmark becomes credible only when it reflects who can cause harm, not just who can be tricked.