Because many application actions are carried out by service accounts, tokens, and delegated permissions that function like non-human identities. If those identities are over-privileged or poorly observed, security teams cannot tell whether an application issue is a harmless bug or an active access path. Runtime signals expose that difference.
Why This Matters for Security Teams
Runtime application signals matter because identity governance cannot stop at directory records, entitlement reviews, or quarterly access recertification. Application behaviour reveals which identities are actually active, which permissions are exercised, and which workflows depend on service accounts, tokens, or delegated access. That makes runtime telemetry essential for spotting privilege creep, abnormal access paths, and identity misuse that static inventories miss.
For security teams, the issue is not only visibility but accountability. A service account can look appropriately scoped on paper while still reaching sensitive data through chained permissions, automation, or hidden application calls. Current guidance suggests aligning governance with operational evidence, not just declared ownership. The NIST Cybersecurity Framework 2.0 is useful here because it ties governance, protection, detection, and response together instead of treating access management as a one-time administrative task.
In practice, many security teams encounter excessive application access only after an incident review shows that the “normal” runtime path was never actually understood.
How It Works in Practice
Effective identity governance uses runtime signals to connect an identity to an action, not just to an assigned role. That means observing authentication events, API calls, token use, privileged workflows, peer-to-peer service interactions, and unusual execution patterns. These signals help distinguish expected automation from suspicious activity and show where an application is operating with more authority than intended.
A practical implementation usually combines identity data, application logs, and policy enforcement:
- Inventory human and non-human identities, including service accounts, workload identities, and delegated tokens.
- Map each identity to the specific application actions it performs in production.
- Flag runtime behaviour that diverges from the approved access model, such as new destinations, unusual call frequency, or privilege escalation.
- Use those findings to drive access reviews, remediation, and just-in-time privilege where feasible.
- Preserve evidence for investigations so governance decisions can be based on observed use, not assumption.
That model aligns well with NIST SP 800-53 Rev 5 Security and Privacy Controls, especially where organisations need auditable control over access, logging, and continuous monitoring. It is also consistent with a zero trust approach: trust is continuously evaluated, not implied by network location or static approval.
The value becomes clearer when runtime signals are tied to alerting and response. If a token starts calling sensitive APIs outside its usual pattern, that can indicate misuse, misconfiguration, or an application change that needs review. Security teams can then separate broken automation from genuine exposure and reduce the risk of overreacting to benign noise. These controls tend to break down in heavily distributed environments where application ownership is unclear and logs are incomplete, because no one can reliably tell which runtime action belongs to which identity.
Common Variations and Edge Cases
Tighter runtime monitoring often increases engineering and governance overhead, requiring organisations to balance visibility against application performance, log volume, and operational complexity. That tradeoff is real, especially when a platform contains many microservices, ephemeral workloads, or third-party integrations that generate high-frequency events.
There is no universal standard for how much runtime telemetry is enough. Best practice is evolving, but current guidance suggests focusing on the identities and actions that create the greatest access risk: privileged service accounts, internet-facing workloads, and systems that can reach sensitive data or administrative functions. In mature environments, runtime signals may be supplemented with policy-as-code, secrets management, and conditional controls so that identity decisions reflect actual behaviour.
Edge cases matter. Some applications cannot emit rich logs, some legacy systems cannot support per-action attribution, and some delegated access patterns blur the line between user and machine identity. In those cases, governance teams should document the gap explicitly and compensate with compensating controls such as tighter token lifetimes, stronger approval workflows, and periodic validation of observed usage. The key point is that runtime evidence should inform the control model, even when it cannot fully automate it. This is especially important where identities cross application boundaries or where one account is reused across multiple services without clear separation of duties.
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 |
|---|---|---|
| NIST CSF 2.0 | GV.OC-02 | Runtime signals help define how identities actually operate in production. |
| NIST AI RMF | AI systems that act through services need runtime accountability and monitoring. | |
| OWASP Non-Human Identity Top 10 | Non-human identities are often exposed through token use and service-to-service activity. |
Record AI and automation actions so governance can verify who or what executed them.
