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24/06/2026 8:03 pm
TL;DR: AI observability adds behavioral telemetry to logs, metrics, and traces so teams can detect hallucinations, policy violations, and runaway costs in LLM systems, according to Kong. Traditional monitoring alone can show infrastructure health while missing the governance failures that matter most for secure AI operations.
NHIMG editorial — based on content published by Kong: What is AI Observability? Monitoring and Troubleshooting Your LLM Infrastructure
Questions worth separating out
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.
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.
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.
Practitioner guidance
- 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.
What's in the full article
Kong's full blog post covers the operational detail this post intentionally leaves for the source:
- Step-by-step guidance on measuring TTFT, inter-token latency, and end-to-end latency in production LLM systems.
- Examples of retrieval monitoring for RAG, including recall@k, MRR@k, nDCG@k, and grounding checks.
- Operational advice on logging prompts, outputs, and model state while redacting PII.
- Implementation context for OpenTelemetry GenAI conventions and observability pipelines.
👉 Read Kong's analysis of AI observability for LLM infrastructure →
AI observability and LLM telemetry: what IAM teams should watch?
Explore further
25/06/2026 5:01 am
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.
A few things that frame the scale:
- 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.
A question worth separating out:
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.
👉 Read our full editorial: AI observability is exposing the limits of traditional monitoring
