Teams should monitor LLM applications with behavioural telemetry that shows whether outputs are accurate, policy-compliant, grounded, and cost-controlled. Standard uptime and error metrics are necessary but insufficient. The monitoring model should combine traces, logs, performance counters, and AI-specific signals such as hallucination indicators, token usage, and retrieval quality.
Why This Matters for Security Teams
Uptime and HTTP error rates tell security teams whether an LLM application is reachable, not whether it is behaving safely. For agentic and retrieval-augmented systems, the real risk is silent failure: a model can answer confidently, invoke tools incorrectly, leak data, or drift from policy while every infrastructure metric remains green. Current guidance from the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework both point toward behaviour-aware monitoring, not just service health.
That shift matters because LLM applications now sit between users, secrets, internal knowledge, and downstream actions. Monitoring must show whether responses are grounded, whether retrieval is working, whether prompts are being abused, and whether token consumption is signalling runaway cost or abnormal tool use. NHIMG research on the AI agents attack surface shows how often autonomous systems move beyond intended scope, which is exactly why basic availability monitoring is insufficient.
In practice, many security teams discover model misuse only after sensitive data has already been exposed or an expensive workflow has already completed incorrectly.
How It Works in Practice
Effective monitoring treats the LLM application as an observable decision system. Teams should collect traces that connect the user request, retrieved context, model response, tool calls, and post-processing step into a single transaction view. That makes it possible to inspect not only whether the application succeeded, but how it reached the result. The operational question is whether the output was accurate, policy-compliant, grounded in approved sources, and cost-controlled.
A practical baseline is to layer traditional telemetry with AI-specific signals:
- Prompt and response traces, including system prompt changes and guardrail decisions
- Retrieval quality, such as citation coverage, source freshness, and empty or low-relevance context hits
- Hallucination indicators, including unsupported claims and answer-to-source mismatch
- Tool and function-call logs, especially failures, retries, and unusual chaining patterns
- Token usage, latency, and cost per request, with thresholds for abnormal spikes
- Policy violations, sensitive-data exposure, and refusal-rate trends
For governance, this should be mapped to the behaviour controls described in the CSA MAESTRO agentic AI threat modeling framework and the NHI Lifecycle Management Guide, because application telemetry and identity telemetry need to align. A model that repeatedly requests broad retrieval, unexpected tools, or privileged connectors may be functioning exactly as coded but still operating outside acceptable risk. Teams should also monitor access to secrets and high-value data paths, drawing lessons from NHIMG research such as the AI LLM hijack breach.
These controls tend to break down in multi-tenant deployments where prompt, retrieval, and tool logs are fragmented across platforms because the evidence needed to explain a bad answer is no longer in one place.
Common Variations and Edge Cases
Tighter behavioural monitoring often increases storage, engineering effort, and review overhead, so organisations have to balance visibility against noise and cost. Current guidance suggests prioritising high-risk workflows first rather than trying to instrument every low-value interaction equally.
There is no universal standard for hallucination scoring yet, which means teams should avoid treating any single metric as authoritative. Some applications can rely on human review of sampled outputs, while others need automated scoring tied to approved sources or retrieval citations. In regulated workflows, policy-compliance metrics may matter more than stylistic accuracy; in customer support, groundedness and escalation rate may be more important.
Edge cases appear when the LLM is embedded inside longer chains, such as code generation, agentic task execution, or RAG over rapidly changing documents. In those environments, a “good” response can still be operationally wrong if the source corpus is stale or the tool execution failed quietly. OmniGPT breach reporting and the NIST AI Risk Management Framework both support the same operational conclusion: monitor outcomes, not just infrastructure. The strongest programmes define a small set of decision-quality indicators, then expand once alert fatigue and sampling quality are under control.
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 CSA MAESTRO 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 Agentic AI Top 10 | A5 | Covers runtime abuse and unsafe agent behaviour in LLM apps. |
| CSA MAESTRO | D2 | Focuses on observability for agent behaviour and control points. |
| NIST AI RMF | Supports governance and measurement of AI risk beyond system uptime. |
Define AI-specific metrics for accuracy, groundedness, policy compliance, and cost.
