A scanning method that installs software on nodes or workloads to observe live behaviour. It can collect process, file, network, and syscall data, which makes it useful for runtime detection and for catching attacks that only appear after deployment.
Expanded Definition
Agent-based scanning is a runtime inspection approach that places an instrumented agent on a host, container, or workload to observe live behaviour. In NHI operations, it helps expose process lineage, file activity, network calls, and syscall patterns that static scanners cannot see.
Unlike image scanning or configuration review, agent-based scanning is about what the workload actually does after deployment. That makes it useful for detecting privilege abuse, unusual credential access, and command execution paths tied to compromised OWASP Agentic AI Top 10 risks or identity misuse. Guidance varies across vendors on how deep the instrumentation should go, so no single standard governs this yet. Practitioners should compare coverage, overhead, and whether the agent can see ephemeral workloads, containers, and short-lived AI agents without breaking release velocity.
The most common misapplication is treating agent-based scanning as a replacement for identity governance, which occurs when teams expect runtime telemetry to compensate for missing NHI inventory, weak secret control, or absent privilege reviews.
Examples and Use Cases
Implementing agent-based scanning rigorously often introduces performance and operational overhead, requiring organisations to weigh deeper visibility against the cost of kernel, CPU, or deployment complexity.
Common use cases include:
- Monitoring a production service account after deployment to detect suspicious file reads or outbound connections that indicate credential theft or lateral movement.
- Watching an AI agent runtime for unexpected tool invocation, especially when the workflow crosses into code execution or secret retrieval paths described in the OWASP NHI Top 10.
- Capturing syscall and process ancestry data during incident response to reconstruct how a compromised workload reached sensitive tokens or certificates.
- Pairing runtime telemetry with policy checks from the NIST AI Risk Management Framework so analysts can separate intended automation from malicious execution.
- Validating that a host agent can still observe short-lived jobs, because ephemeral containers often disappear before traditional scanners finish a pass.
For a practical example of why runtime observability matters, see NHIMG’s Analysis of Claude Code Security, which highlights how security controls must follow actual execution paths rather than assumed ones.
Why It Matters in NHI Security
Agent-based scanning matters because NHI compromise often becomes visible only at runtime. When a service account, API key, or AI agent is abused, the attacker’s behaviour may look legitimate until the workload begins contacting unfamiliar destinations, reading unexpected files, or escalating privileges. That is where runtime agents add value.
NHIMG research shows that Only 5.7% of organisations have full visibility into their service accounts, which is a strong indicator that static inventory alone is not enough. Runtime scanning can help close that visibility gap, especially when paired with controls from the CSA MAESTRO agentic AI threat modeling framework and the MITRE ATLAS adversarial AI threat matrix. It is especially valuable when secrets are embedded in code paths, because runtime evidence can show whether a token was accessed by the intended workload or by a process spawned through compromise.
Organisations typically encounter the need for agent-based scanning only after a suspicious workload has already executed, at which point runtime visibility becomes operationally unavoidable to contain the incident.
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 AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Covers secret handling and runtime exposure of NHIs inside workloads. |
| OWASP Agentic AI Top 10 | A2 | Agentic systems need runtime oversight for unsafe tool use and execution paths. |
| NIST AI RMF | GOVERN | Defines governance for AI systems, including monitoring and risk controls in operation. |
Instrument agents so tool calls, process launches, and data access are observable during execution.
