Telemetry captured from a layer beneath the application process, such as kernel-level instrumentation, where compromised application code cannot easily disable or alter the sensor. For AI agent security, this is often the difference between resilient detection and easily evaded tracing.
Expanded Definition
Below-process telemetry refers to observability data collected from a substrate below the application layer, most commonly the kernel, hypervisor, or other trusted instrumentation points. That placement matters because the process being monitored cannot simply call an API, stop an agent, or redact its own traces. In security operations, this makes the data materially more resilient than in-process logging, especially when the monitored workload may itself be compromised.
The term is increasingly relevant in AI security and agentic environments, where autonomous software entities can execute tools, spawn subprocesses, and interact with secrets. A below-process signal can preserve evidence of command execution, network access, file activity, and privilege use even when an application tries to obscure its behaviour. This aligns with the governance orientation of the NIST Cybersecurity Framework 2.0, which emphasises continuous visibility, detection, and response across attack paths. Definitions vary across vendors on whether eBPF, kernel modules, hypervisor hooks, or sideband collectors all qualify, so the practical boundary is usually functional rather than doctrinal.
The most common misapplication is treating any centrally collected log stream as below-process telemetry, which occurs when the sensor can still be disabled, filtered, or manipulated by the same runtime it is meant to observe.
Examples and Use Cases
Implementing below-process telemetry rigorously often introduces kernel and platform dependency, requiring organisations to weigh stronger tamper resistance against added deployment, compatibility, and performance constraints.
- Kernel-level process execution tracing captures child process creation, argument use, and exit codes even if the application suppresses its own audit output.
- Sideband network visibility records outbound connections from a workload, helping identify command-and-control traffic or unexpected data transfer from an AI agent container.
- File and registry monitoring from a trusted sensor detects secret access or configuration tampering after an attacker has obtained application-level control.
- Hypervisor-backed inspection observes guest behaviour without relying on instrumentation inside the guest, which is useful for high-value or untrusted workloads.
- For identity-heavy services, it can show how a service account or NHI is actually used at runtime, complementing IAM records and secret lifecycle controls.
For implementation patterns and adversary context, teams often cross-check telemetry design against guidance from NIST Cybersecurity Framework 2.0 and operational monitoring practices that prioritise integrity of evidence over application cooperation. Where AI agents are allowed to invoke tools, below-process telemetry is especially valuable because it can preserve the record of what the agent executed, not just what the application reported.
Why It Matters for Security Teams
Security teams rely on below-process telemetry when they need evidence they can trust after compromise, not just routine observability during healthy operation. If the sensor sits above the trust boundary, an attacker who controls the workload may be able to blind alerts, forge audit trails, or selectively suppress events. That failure mode is especially serious for NHI estates and agentic systems, where workloads often hold long-lived secrets, delegated permissions, and tool access that can be abused quietly.
This concept also strengthens detection engineering and incident response because it improves the chain of custody around runtime events. In environments with privileged automation, kernel- or hypervisor-level visibility can help distinguish normal agent activity from abuse of the same execution path. The NIST Cybersecurity Framework 2.0 reinforces the need for reliable monitoring and response capabilities, while identity governance teams can use below-process data to verify whether a service identity, token, or secret was actually exercised in ways policy allowed.
Organisations typically encounter the limits of ordinary logging only after a stealthy intrusion, at which point below-process telemetry becomes operationally unavoidable to reconstruct what really happened.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0 provides the primary governance reference for this term.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | DE.CM-1 | Defines continuous monitoring of networks and systems relevant to trusted telemetry. |
Use below-process sensors to keep monitoring intact even when the workload is hostile.
Related resources from NHI Mgmt Group
- How do teams decide whether to process telemetry at the edge or in the cloud?
- What breaks when AI agent observability relies only on in-process telemetry?
- Why do NHI programmes need stronger process ownership than many human identity programmes?
- When should organisations treat runtime telemetry as a primary control?
