AI observability is exposing the limits of traditional monitoring

At a glance

What this is: This is a practical explainer of AI observability, with the central finding that classic monitoring cannot prove an LLM system is safe, accurate, or cost-controlled.

Why it matters: It matters because identity and access teams now need telemetry that can evaluate AI behaviour, not just system uptime, across NHI, agentic AI, and human workflows.

👉 Read Kong's analysis of AI observability for LLM infrastructure

Context

AI observability is the discipline of measuring how an LLM system behaves in production, not just whether the infrastructure is alive. For identity and access teams, the gap is governance as much as operations: a model can be available, authenticated, and routed correctly while still violating policy, leaking context, or producing untrusted outputs.

Traditional observability was built for deterministic systems, but LLM applications are probabilistic and stateful across prompts, tools, retrieval layers, and model calls. That makes AI observability relevant to NHI governance, agentic AI oversight, and human IAM workflows that now depend on AI-generated decisions or content.

The article’s core point is typical of modern AI adoption: organisations often notice the failure only after users, costs, or security controls have already been affected. The programme implication is that telemetry has to follow behaviour, not just infrastructure.

Key questions

Q: How should teams monitor LLM applications beyond uptime and error rates?

A: 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.

Q: Why do traditional observability tools miss the real risks in AI systems?

A: Traditional observability tools were designed for deterministic systems, so they show whether infrastructure is healthy but not whether the model behaved correctly. In AI systems, a request can succeed technically while still producing unsafe, inaccurate, or expensive output. That is why behavioural signals are now required.

Q: What signals show that a RAG system is not trustworthy in practice?

A: A RAG system is not trustworthy when retrieval quality is weak, citations do not support the answer, document redundancy crowds the context window, or grounding scores fall below acceptable thresholds. Teams should treat those signals as evidence that the model may be generating plausible but unsupported responses.

Q: How do security and compliance teams use AI observability evidence?

A: Security and compliance teams should use AI observability to verify that production behaviour stays within approved policy, privacy, and risk boundaries. The evidence is useful for incident review, control testing, and governance reporting because it shows what the AI system actually did, not just what it was designed to do.

Technical breakdown

Why traditional logs, metrics, and traces are not enough

Logs, metrics, and traces still matter, but they only describe the infrastructure path of an AI request. They do not show whether the model answered truthfully, complied with policy, grounded its output in retrieval evidence, or used tokens efficiently. AI observability adds behavioural signals so teams can correlate request flow with model quality, safety, and cost outcomes. In practice, that means the same request can be technically successful while still being operationally unacceptable. The monitoring stack has to evaluate the content and consequence of the output, not just the service health of the pipeline.

Practical implication: add behavioural telemetry alongside standard observability before you treat LLM uptime as evidence of control.

Time-to-first-token and token usage as operating signals

Time-to-First-Token, inter-token latency, and token consumption are more than performance counters. They reveal whether the model is responding quickly enough for the use case, whether streaming is smooth, and whether the system is burning cost in a way users will feel immediately. In LLM environments, performance and economics are tightly coupled because every extra token changes both latency and spend. That makes token telemetry a governance signal as much as an engineering metric. If token patterns shift sharply, the issue may be prompt design, retrieval quality, or workload abuse rather than raw infrastructure capacity.

Practical implication: monitor token and latency patterns together so cost spikes and degraded user experience are investigated as one problem.

RAG observability depends on grounding and retrieval quality

Retrieval-augmented generation introduces another layer of risk because the model’s answer depends on what the retrieval system surfaces. If retrieval is weak, the model can sound confident while being wrong, incomplete, or uncited. That is why recall, ranking quality, document redundancy, and context window usage matter. AI observability has to confirm that the retrieved material really supports the answer, not just that a search returned documents. For governance teams, this is the difference between an answer that is technically generated and one that is operationally trustworthy.

Practical implication: instrument retrieval quality and citation support, not only the model endpoint, if you rely on RAG for decision support.

NHI Mgmt Group analysis

AI observability is now an identity governance problem, not only an engineering one. LLM systems can be authenticated, routed, and monitored for uptime while still producing unsafe or non-compliant behaviour. That means the governance boundary has moved from service availability to behavioural assurance, especially where AI touches NHI-backed workflows and human decision chains. Practitioners should treat observability as part of access governance for AI-enabled systems.

The missing control is behavioural evidence, not another infrastructure dashboard. Traditional monitoring assumes the operational question is whether a service is up. AI systems require a different question: whether the model behaved within policy, grounding, and cost boundaries. Kong’s framing reinforces a broader market pattern, where AI control planes are converging with observability and governance. The implication is that teams must re-evaluate what counts as proof of control.

Groundedness is becoming the named concept that separates useful AI from merely functioning AI. If a response cannot be tied back to verifiable sources, then success metrics and uptime metrics are misleading. This is especially important where human IAM, NHI workflows, and agentic systems increasingly depend on model output to trigger downstream actions. Practitioners should treat grounding as an operational control objective, not a nice-to-have quality metric.

AI observability validates the shift from static policy to runtime assurance. NIST AI RMF and OWASP-style guidance both point toward ongoing evaluation rather than one-time deployment checks. That matters because AI behaviour changes with prompts, retrieval state, and usage context. The practical conclusion is clear: governance programmes must verify what the model actually does in production, not what the architecture assumes it will do.

Cost, safety, and correctness now share the same telemetry plane. A model that is fast but expensive, or safe but ungrounded, is still failing the programme. That convergence makes AI observability a cross-functional identity control point, because the same telemetry supports security, risk, and operations decisions. Teams that separate those signals will miss the combined risk picture.

From our research:

• 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures, according to the Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which helps explain why identity governance often lags behind runtime reality.
• That visibility gap is why teams should pair AI observability with the NHI Lifecycle Management Guide and broader access governance before they rely on AI output for decisions.

What this signals

Groundedness will become a baseline control objective for AI-enabled programmes. As LLMs move deeper into business workflows, teams will need evidence that the system’s answers can be traced to supported inputs, not just that the service responded quickly. That shift aligns with the direction of the NIST AI Risk Management Framework, where ongoing governance matters more than one-time deployment approval.

Observability for AI will converge with identity and access governance. When a model can trigger retrieval, call tools, or influence downstream decisions, telemetry becomes part of control assurance for both human and machine identities. Kong’s article points in the same direction as the OWASP Agentic AI Top 10, where tool misuse and runtime control are core concerns.

Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them. That gap matters because AI workloads often depend on secrets, APIs, and agent-linked access paths that become invisible once the model is in production. Programmes that cannot govern those credentials will struggle to govern the behaviour they enable.

For practitioners

• Instrument behavioural telemetry for AI workloads Capture policy violations, grounding quality, hallucination signals, and token consumption alongside logs, metrics, and traces so production review goes beyond uptime.
• Define response quality thresholds for each AI use case Set acceptable ranges for accuracy, latency, and token cost by workflow, because a customer-facing assistant, coding tool, and internal agent do not share the same operational target.
• Tie retrieval monitoring to answer validation Track recall, ranking quality, citation accuracy, and document redundancy together so RAG outputs are judged on supportability, not only search success.
• Review AI telemetry through governance and risk teams Use the same operational evidence for security, compliance, and access decisions so behaviour-based monitoring becomes part of control assurance rather than a separate engineering exercise.

Key takeaways

• AI observability changes the question from whether infrastructure is healthy to whether the model behaved safely, accurately, and within cost bounds.
• The scale of the identity problem is already visible, with 91.6% of secrets still valid five days after notification and only 5.7% of organisations fully seeing their service accounts.
• Practitioners should treat behavioural telemetry, grounding checks, and token oversight as core controls for AI governance, not optional diagnostics.

Key terms

• AI Observability: AI observability is the practice of collecting and analysing telemetry that explains how an AI system behaved in production. It extends classic monitoring by adding evidence about quality, safety, retrieval grounding, latency, and cost so teams can judge whether outputs are trustworthy as well as available.
• Groundedness: Groundedness is the degree to which an AI response can be supported by verifiable source material. In practice, it measures whether the model answered from evidence rather than inference, memory, or fabrication, which is critical for RAG systems and any workflow that drives decisions from model output.
• Time-to-First-Token: Time-to-First-Token is the initial delay before an AI model emits its first response token. It is a user-experience and control signal because it reflects how quickly the system begins to respond, and it often reveals latency, routing, or workload issues before broader service failures appear.
• Retrieval-Augmented Generation: Retrieval-Augmented Generation is an AI pattern where a model retrieves external documents before generating an answer. It improves context when retrieval is accurate, but it also introduces governance risk because response quality depends on search relevance, document quality, and the integrity of the retrieved evidence.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM or identity security programme, it is worth exploring.

This post draws on content published by Kong: What is AI Observability? Monitoring and Troubleshooting Your LLM Infrastructure. Read the original.