Start by agreeing on the same metric definitions, then tie each metric to a specific business or governance action. For example, a drop in returning users might trigger customer success follow-up, while organisation growth might trigger entitlement review. Shared definitions prevent conflicting interpretations of the same identity data.
Why This Matters for Security Teams
Identity usage reporting is not just a dashboard exercise. It drives access review, entitlement cleanup, incident response, and product decisions about how features are actually used. If security and product teams measure different things, they will optimise for different outcomes and create conflicting narratives about the same environment. That is especially risky for NHI-heavy systems, where service accounts, API keys, and automation often outnumber human users and create more hidden blast radius than a normal app analytics stack. The Ultimate Guide to NHIs notes that NHIs outnumber human identities by 25x to 50x in modern enterprises. Current guidance from NIST Cybersecurity Framework 2.0 also reinforces that identity-related governance only works when organisations can observe, measure, and act on access behaviour consistently.
For reporting to be useful, teams need a shared language for “active”, “returning”, “idle”, “privileged”, and “sensitive” before they debate charts or thresholds. In practice, many security teams only discover metric drift after an access review has failed or a product launch has already inflated entitlement scope.
How It Works in Practice
Start by defining each reportable identity event in operational terms, not just product terms. A “returned identity” may mean a service account that authenticated again within 30 days, while a “new identity” may mean a freshly created API key, workload token, or OAuth grant. Then map each metric to an action owner and a decision rule. That makes the report a control surface instead of a vanity metric.
A practical structure is to align on three layers:
- Usage: authentication frequency, first seen, last seen, token age, and rotation status.
- Risk: privilege scope, secret location, third-party exposure, and whether the identity is tied to a critical workflow.
- Action: revoke, rotate, review, reclassify, or retain.
For NHI programmes, this is where Top 10 NHI Issues is useful as a governance lens, because many reporting problems are really control problems: inconsistent naming, missing owners, weak rotation, and poor visibility. The same principle appears in the 52 NHI Breaches Analysis, where identity exposure is often amplified by weak inventory discipline and unclear accountability. Use NIST CSF 2.0 to tie each metric to a response category such as Protect, Detect, or Respond, and make product teams own usage interpretation while security owns control thresholds.
Where possible, report on workload identities separately from human identities. A service account that calls an API 10,000 times a day is not “engagement” in the product sense if the activity is purely automated. These controls tend to break down when reporting mixes human, machine, and third-party identities in the same view because the same activity pattern can mean adoption in one case and exposure in another.
Common Variations and Edge Cases
Tighter reporting standards often increase governance overhead, requiring organisations to balance comparability against speed of delivery. That tradeoff shows up most clearly when teams want one universal definition for every identity type. Best practice is evolving here, and there is no universal standard for this yet: some organisations split reporting by business unit, others by workload type, and others by trust tier.
Edge cases matter. A returning user metric might be useful for customer success, but the same metric can be misleading for agents, background jobs, or ephemeral JIT credentials that are intentionally short lived. Similarly, a spike in identity creation could indicate healthy product growth or a failed automation loop creating duplicate secrets. Treat anomaly thresholds as context-aware, not absolute.
For governance programmes, pair reporting with the identity lifecycle controls described in the Ultimate Guide to NHIs — What are Non-Human Identities and the market perspective in Ultimate Guide to NHIs — The NHI Market. That keeps reporting tied to owner assignment, rotation, and offboarding rather than to raw volume alone. The strongest programmes treat identity usage reporting as a control input, not a scorecard, because the business question is always what should happen next.
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 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Identity inventory and ownership are prerequisite for reliable usage reporting. |
| NIST CSF 2.0 | PR.AC-1 | Access management depends on shared identity definitions and measurable usage. |
| NIST AI RMF | Context-aware reporting supports governance for autonomous and adaptive systems. |
Define reporting rules that account for runtime context, not just static identity labels.
Related resources from NHI Mgmt Group
- How should security teams govern app identity modernization across multi-cloud environments?
- How should security teams use identity risk signals in access reviews?
- How should security teams govern AI-generated identity workflows in application code?
- How should security teams govern agent-operated identity configuration from the terminal?
