Traffic generated by software that can act on behalf of a user or process with some degree of independent decision-making. In fraud prevention, it includes both legitimate assistants and malicious automation, so the control question becomes intent and behaviour, not automation alone.
Expanded Definition
Agentic traffic is best understood as request flow generated by software that can choose actions, call tools, and adapt its behaviour without a human clicking each step. In NHI and IAM contexts, that matters because the same traffic pattern can come from a legitimate AI assistant, an internal automation pipeline, or an attacker operating compromised credentials. Definitions vary across vendors, but the practical control question is not whether the traffic is automated. It is whether the automation is authorised, bounded, and behaving within its intended scope.
This term overlaps with agent identity, workload identity, and API client activity, yet it is narrower than general machine traffic because decision-making is part of the risk signal. A request stream that suddenly changes destination, rate, or sequence may indicate a benign agent completing a task, or it may indicate a hijacked NHI exfiltrating data. NHI Management Group treats that distinction as central to governance, and the broader agentic risk landscape is reflected in OWASP NHI Top 10 and the NIST AI Risk Management Framework.
The most common misapplication is treating all agent-generated requests as low-risk automation, which occurs when teams ignore context, permissions, and behavioural drift.
Examples and Use Cases
Implementing agentic traffic controls rigorously often introduces investigation overhead, requiring organisations to weigh better attribution and containment against added logging, policy checks, and review friction.
- A customer-support assistant pulls account history, drafts a response, and opens a ticket. The traffic is legitimate agentic traffic because the workflow is authorised and constrained by scope.
- An internal code agent queries repositories, creates pull requests, and invokes CI tools. Its requests should be mapped to the relevant service identity and reviewed against the guidance in the OWASP Agentic AI Top 10.
- A compromised API key begins calling LLM endpoints, enumerating data stores, and exporting payloads. This is malicious agentic traffic even if the requests look machine-like, which is why LLMjacking: How Attackers Hijack AI Using Compromised NHIs is relevant evidence.
- An AI scheduling agent changes meeting times and sends messages on behalf of a user. The traffic is acceptable only if the identity, delegation, and action boundaries are explicit and auditable.
- Threat hunting teams can compare traffic patterns against the MITRE ATLAS adversarial AI threat matrix and the AI LLM hijack breach research when agent behaviour shifts unexpectedly.
Why It Matters in NHI Security
Agentic traffic becomes a security problem when defenders cannot tell whether a request stream is a sanctioned agent, a misconfigured automation, or an attacker using stolen secrets. That ambiguity is dangerous because NHIs often have broad API access, minimal human visibility, and few natural behavioural anchors. In the AI Agents: The New Attack Surface report, 80% of organisations said their AI agents had already performed actions beyond intended scope, and 52% could track and audit the data their AI agents access, leaving 48% with a blind spot for investigation.
That is why agentic traffic must be governed with identity boundaries, request-level observability, and decision-aware policy enforcement. The same traffic should be interpreted differently depending on whether it is advancing an approved task, escalating privilege, or exfiltrating data. The control challenge also aligns with CSA MAESTRO agentic AI threat modeling framework, which emphasises action boundaries and agent lifecycle risk, and the NIST AI Risk Management Framework, which supports mapping and managing operational AI risk.
Organisations typically encounter the consequences only after a breach alert, a suspicious transaction, or a compliance review exposes unexplained agent behaviour, at which point agentic traffic 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 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 | Agentic traffic often exposes secret misuse, scope drift, and compromised NHI activity. |
| OWASP Agentic AI Top 10 | A1 | Defines risks from agent autonomy, tool use, and unintended action paths. |
| NIST AI RMF | Frames AI operational risk management around mapping, measuring, and governing AI system behavior. |
Bind agent requests to approved NHI scopes and inspect traffic for secret abuse or abnormal tool use.
