Observability data generated and handled inside the same runtime as the application or agent being watched. It is useful for debugging, but once that runtime is compromised, the same code path that creates the signal can also suppress or alter it before defenders see it.
Expanded Definition
In-process telemetry is observability data created, buffered, or analysed inside the same application, service, or agent runtime that is being measured. That makes it different from external monitoring, sidecar collection, or host-level instrumentation, because the signal is produced by code that already shares the target’s trust boundary. In security operations, that design can be useful for low-latency debugging and local diagnostics, especially in distributed systems where external visibility is incomplete.
The tradeoff is that in-process collection reduces separation between the observer and the observed. If the runtime is altered, the telemetry path can be tampered with, delayed, filtered, or disabled before defenders receive it. That risk becomes sharper in agentic AI and other software with tool access, where the same process may both generate actions and report on them. For governance purposes, in-process telemetry should be treated as a potentially untrusted signal source, not as a guaranteed source of truth. The NIST Cybersecurity Framework 2.0 is useful here because it reinforces the need for visibility, detection, and resilience across trusted and untrusted system boundaries.
The most common misapplication is assuming in-process logs are authoritative evidence, which occurs when defenders treat runtime self-reporting as immune to manipulation after a code injection or agent compromise.
Examples and Use Cases
Implementing in-process telemetry rigorously often introduces a trust-boundary constraint, requiring organisations to weigh richer internal visibility against the risk that the telemetry channel can be altered by the workload itself.
- An AI agent records prompt, tool, and decision traces from inside its own runtime for troubleshooting, but those traces may be incomplete if the agent process is manipulated.
- A microservice emits structured logs directly from application code so developers can trace request paths without relying on network mirroring or host agents.
- A security control library inside a workload captures API calls, auth events, and state changes to support incident reconstruction after an outage.
- An attacker with code execution disables or filters the telemetry module so specific commands never appear in the internal audit trail.
- A development team uses in-process metrics during testing, then shifts to external collection and immutable storage for production-grade assurance.
For teams working on AI or autonomous systems, it is often necessary to compare in-process traces with independent monitoring sources such as host telemetry, gateway logs, or platform events. That approach aligns with guidance from NIST Cybersecurity Framework 2.0 on layered detection and recovery rather than single-source trust.
Why It Matters for Security Teams
Security teams care about in-process telemetry because it can create a false sense of observability. If the same runtime both generates and controls the signal, compromise can erase evidence, distort timelines, or create selective blind spots. That matters in incident response, fraud detection, and AI governance, where defenders need to know not only what the system reported, but whether the report itself is trustworthy.
The issue is especially important for agentic AI and NHI-heavy environments, where software entities can act, call tools, and self-report from within the same execution context. In those settings, in-process telemetry should be paired with independent controls such as external logging, tamper-evident storage, and privilege separation. Broader resilience principles in the NIST Cybersecurity Framework 2.0 support that layered approach, even when the telemetry itself is generated locally.
Organisations typically encounter the limits of in-process telemetry only after a compromise or post-incident review, at which point the missing or altered signal becomes operationally unavoidable to address.
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 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM-01 | Telemetry supports continuous monitoring, but in-process sources may be compromised. |
| NIST AI RMF | GOVERN | AI RMF governance covers trustworthy logging and oversight of AI system behaviour. |
| OWASP Agentic AI Top 10 | Agentic AI guidance highlights the risk of self-reporting controls inside the agent runtime. | |
| OWASP Non-Human Identity Top 10 | NHI guidance emphasizes protecting machine identities and their runtime evidence trails. | |
| NIST SP 800-53 Rev 5 | AU-9 | Audit information protection addresses tamper resistance and integrity of records. |
Cross-check local telemetry with independent monitoring before treating it as evidence.
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
