The effective identity a system presents while it is actually running, including loaded tools, live credentials, and active scope. For AI agents, this can differ from the manifest because the runtime may fall back, expand, or adapt its behaviour after deployment.
Expanded Definition
Runtime-derived identity is the identity state that exists at execution time, not just what was approved in a manifest, policy document, or deployment record. It reflects the tools an AI agent can actually invoke, the credentials it has loaded, the scopes it can exercise, and any fallback behaviour that appears after startup. For NHI and agentic AI governance, this matters because the live operating identity can widen or narrow depending on configuration drift, secret rotation, delegated access, or dynamic tool selection.
Usage in the industry is still evolving, and definitions vary across vendors when they describe runtime identity, effective identity, execution identity, or agent session identity. At NHI Management Group, the key distinction is that runtime-derived identity is observed, not merely intended. It is the identity surface that security teams must assess when deciding whether an agent, service, or workload is operating within approved bounds. The concept aligns well with NIST Cybersecurity Framework 2.0 because identity risk only becomes meaningful when mapped to actual system behaviour.
The most common misapplication is treating the deployed manifest as the authoritative identity, which occurs when teams ignore fallback credentials, inherited permissions, or runtime tool changes after launch.
Examples and Use Cases
Implementing runtime-derived identity rigorously often introduces monitoring and governance overhead, requiring organisations to weigh adaptive execution against stronger control over what the system can do in production.
- An AI agent is deployed with a narrow tool set, but at runtime it loads a broader connector through an orchestration layer, changing its effective access profile.
- A service account rotates credentials successfully, yet an older cached token remains active in memory, so the runtime identity is still more permissive than the approved state.
- A workflow agent begins with read-only access, then escalates through delegated permissions during task handoff, creating a different live identity than the one documented in the deployment manifest.
- A cloud workload falls back to a default identity when its preferred role assumption fails, which is a classic case where runtime behaviour diverges from design intent.
- Security teams compare live execution telemetry with NIST Cybersecurity Framework 2.0 expectations to spot when active scope exceeds approved access.
These examples are especially important for non-human identities because the runtime state can change without a traditional login event, making the effective identity hard to infer from static configuration alone.
Why It Matters for Security Teams
Security teams need to understand runtime-derived identity because control failures often happen at execution time, not at provisioning time. If a system’s live identity is broader than intended, least-privilege reviews, segregation-of-duties checks, and incident response assumptions can all become unreliable. This is particularly relevant for AI agents, where tool access, retrieved context, and fallback logic can reshape the operating identity after deployment.
For identity security programs, the practical question is not only who or what was assigned access, but what identity was actually exercised when the action occurred. That distinction affects auditability, containment, and recovery, especially when secrets, tokens, or delegated credentials are involved. Runtime-derived identity also intersects with governance expectations in NIST Cybersecurity Framework 2.0, where asset visibility, access control, and continuous monitoring depend on accurate operational state.
Organisations typically encounter the consequences only after an agent misuses an unexpected tool, at which point runtime-derived identity becomes operationally unavoidable to investigate and contain.
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 AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI guidance focuses on live identity, secrets, and runtime access for non-human systems. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance addresses tool access and runtime behaviour that can alter effective identity. | |
| NIST CSF 2.0 | PR.AC | NIST CSF covers access control and continuous monitoring of operational identity states. |
| NIST AI RMF | AI RMF supports governance of deployed AI behaviour, including runtime identity shifts. | |
| NIST Zero Trust (SP 800-207) | 3.1 | Zero Trust requires continuous verification of the active subject and its permissions. |
Map runtime identity to access-control and monitoring controls, then detect drift continuously.
