Runtime AI detection is the practice of identifying AI use while it is happening in production systems. It links model invocation to the workload, process, and identity that initiated it, so governance is based on observed behaviour rather than declarations or static inventory.
Expanded Definition
Runtime AI detection focuses on observable AI activity as it occurs in production, not on what an application claims to be doing in design documents or inventory records. In NHI security, that means correlating model calls, tool use, API requests, and downstream actions to the workload, process, and identity that initiated them. The point is to establish operational evidence of AI behaviour, especially when AI access is embedded in services, agents, or automated workflows that can change dynamically.
This distinction matters because static discovery often misses ephemeral containers, delegated service accounts, and tool-using agents that appear only under load. The concept is closely related to runtime telemetry, but it is narrower: the question is not just whether a system is running, but whether AI is being invoked and by whom. Guidance across vendors is still evolving, so runtime AI detection should be treated as an operational control plane rather than a single product feature. For identity and governance context, see NHI Lifecycle Management Guide and the NIST Cybersecurity Framework 2.0.
The most common misapplication is relying on CMDB entries or model registries alone, which occurs when teams assume declared AI usage matches the identity and process actually making calls in production.
Examples and Use Cases
Implementing runtime AI detection rigorously often introduces telemetry and correlation overhead, requiring organisations to weigh real-time visibility against added logging cost, privacy review, and engineering effort.
- Detecting when a customer support service account begins calling an LLM endpoint from an unexpected container image, then tracing the invocation back to the workload identity that triggered it.
- Flagging an AI agent that starts using a new tool or API at runtime, even though the approved architecture only allowed read-only model prompts during development.
- Correlating spikes in inference requests with a compromised NHI, which helps distinguish normal automation from credential abuse patterns described in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research.
- Comparing live AI activity against expected control boundaries using the NIST Cyber AI Profile (IR 8596), especially where model invocation must be constrained to authorised contexts.
- Identifying production AI use in workloads that were not tagged as AI-enabled, then updating governance records and access reviews after the fact.
For example, the Top 10 NHI Issues highlights how identity sprawl and unmanaged machine access make runtime visibility necessary, not optional.
Why It Matters in NHI Security
Runtime AI detection is essential because attack paths increasingly rely on compromised NHIs, hidden model usage, and unauthorised tool execution rather than obvious interactive logins. Without runtime evidence, defenders can miss the moment an AI workload crosses from approved automation into risky behaviour. That creates blind spots in access reviews, incident response, and policy enforcement, especially when secrets, tokens, or service credentials are reused across multiple AI-enabled services. In practice, runtime signals help prove whether a model call came from an expected identity, an ephemeral process, or a tampered workflow.
This is not a theoretical problem. NHIMG research on secrets exposure shows that when AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and sometimes in as little as 9 minutes, underscoring how quickly runtime misuse can begin after compromise. The same pattern appears in AI-enabled environments where the issue is only discovered once anomalous calls, spend spikes, or data leakage have already occurred. For broader risk framing, the Ultimate Guide to NHIs — Key Challenges and Risks and DeepSeek breach illustrate how quickly exposed AI-adjacent assets can become governance failures.
Organisations typically encounter this term only after an AI workload is implicated in a breach, at which point runtime AI detection 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 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Agentic systems need runtime monitoring to detect unexpected tool use and model actions. | |
| OWASP Non-Human Identity Top 10 | NHI-02 | Runtime detection supports spotting secret abuse and unauthorized NHI-driven AI activity. |
| NIST CSF 2.0 | DE.CM-8 | Continuous monitoring covers anomalous process and workload behavior in production. |
Monitor production AI activity continuously and escalate deviations from approved identity and workload patterns.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
