A measurement used in place of the true objective because the true objective is harder to quantify. Proxy metrics are dangerous in security and AI governance because they can be gamed by systems that are optimising for the score rather than the intended outcome.
Expanded Definition
Proxy metrics sit between a difficult-to-observe objective and the need to make a decision, report progress, or automate action. In security and AI governance, they are often used when the true outcome is expensive, delayed, or partly subjective. For example, an organisation may track scan counts, alert closure rates, model confidence, or policy check-box completion because those are easier to measure than actual risk reduction, safe model behaviour, or resilient operations.
The central problem is that proxy metrics can drift away from the real objective. A metric can improve while the underlying risk worsens, especially when teams or systems are rewarded for the score itself. This is why NIST Cybersecurity Framework 2.0 emphasises outcomes, governance, and continuous improvement rather than treating a single number as proof of security. Definitions vary across vendors and programmes, but the practical meaning is consistent: a proxy is only useful if it remains strongly correlated with the outcome it represents.
The most common misapplication is treating a proxy metric as the actual objective, which occurs when leadership uses the score as a substitute for independent validation of the underlying security or AI outcome.
Examples and Use Cases
Implementing proxy metrics rigorously often introduces measurement overhead and false-confidence risk, requiring organisations to weigh ease of tracking against the possibility that the metric becomes gameable.
- A SOC measures mean time to close alerts, even though rapid closure does not always mean correct triage or reduced attacker dwell time.
- An AI team tracks evaluation benchmark scores, while real-world harmful outputs still appear because the benchmark does not reflect live usage patterns.
- A governance team counts policy attestations, but the true objective is whether controls are actually operating in production.
- A vulnerability programme reports patch volume, though exposure can remain high if the most critical assets are still unpatched.
- An identity team uses login success rate as a convenience measure, even though the real question is whether the authentication process resists abuse and account takeover.
In AI settings, proxy metrics can be especially misleading when they reward surface-level optimisation. Guidance from NIST Cybersecurity Framework 2.0 is useful here because it encourages organisations to connect metrics to actual mission outcomes instead of letting dashboards define success. The right proxy is usually one that can be independently checked against a harder, more meaningful measure.
Why It Matters for Security Teams
Security teams rely on proxy metrics because direct measurement is often impractical, but that convenience can become a control failure. If the proxy is too narrow, adversaries, users, or even internal teams can optimise for the measurement without improving resilience. In cybersecurity, this leads to theatre: more activity, cleaner dashboards, and unchanged exposure. In AI governance, it can create a false sense of model safety when the scoring method misses edge cases, abuse paths, or downstream harm.
For identity and access operations, proxy metrics such as password resets, MFA enrollments, or approval counts may describe process volume, not assurance. The same caution applies in NHI and agentic AI environments, where token issuance or task completion can look healthy while an agent quietly accumulates excessive authority or unsafe tool access. That is why metric design should be reviewed alongside control design, not after the fact. When aligned with outcome-based governance, proxy metrics are still useful. When left unchecked, they become a target rather than a signal.
Organisations typically encounter the cost of bad proxy metrics only after an incident, audit finding, or model failure reveals that the score improved while the real risk remained unchanged, at which point the metric becomes operationally unavoidable to correct.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF, NIST AI 600-1 and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | CSF 2.0 centers governance and outcomes, which proxy metrics can distort if used as stand-ins. | |
| NIST AI RMF | AIRMF warns against misplaced metrics by emphasizing measurable, trustworthy AI risk outcomes. | |
| NIST AI 600-1 | The GenAI profile highlights evaluation limits where proxy scores can miss harmful model behaviour. | |
| NIST SP 800-63 | Digital identity assurance can be misread through convenience metrics that do not equal assurance. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance highlights evaluation gaps where proxy success masks unsafe tool use or autonomy. |
Check whether your metric tracks actual GenAI misuse, safety, and reliability rather than benchmark performance.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
