Topic starter
14/09/2026 10:09 am
TL;DR: AI observability is the practice of tracking LLM performance, cost, behaviour, prompts, responses, and tool calls in production, and Lasso Security’s article argues that continuous monitoring is now necessary to catch hallucinations, prompt injection, shadow AI, and agentic misuse across growing enterprise deployments. The governance gap is no longer theoretical: visibility, logging, and runtime enforcement have become baseline requirements for AI risk management.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “AI Observability: How It Works, Key Challenges & Best Practices”.
By the numbers:
- The share of organisations using AI in at least one business function jumped from 55% in 2023 to 88% in 2025, according to McKinsey's Global Surveys on the State of AI cited by Lasso Security.
- Generative AI use specifically went from 33% in 2023 to 79% in 2025, according to McKinsey's Global Surveys on the State of AI cited by Lasso Security.
Key questions
A: Security teams should treat autonomous AI systems as a distinct access layer, not just another application feature.
Q: Why do successful AI requests still create security risk?
A: A request can succeed technically while still producing hallucinations, unsafe content, or out-of-scope actions.
Q: What are the signs that AI agent observability is failing?
A: The main signs are missing tool metadata, disconnected traces between agent hops, unexplained retries, and an inability to reconcile a successful call with the actual state change it caused.
Practitioner guidance
- Instrument full prompt and tool-call traces Capture prompts, responses, retrieved context, model version, and tool outputs so incident review can reconstruct the exact execution path.
- Tie agent actions to workload identities Map each agent, service account, API key, and token to its task scope so tool usage can be traced back to the identity that executed it.
- Enforce policy at runtime, not after the fact Block or redact unsafe outputs, restrict tool access, and apply scope limits before an agent can continue to the next action.
Bottom line: AI observability closes the gap between model uptime and model behaviour by tracing prompts, outputs, tool calls, and runtime context.
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14/09/2026 10:10 am
AI observability is becoming the operational boundary for AI governance. Once models and agents are embedded in live workflows, the question is no longer whether they are monitored, but whether their actions can be reconstructed and controlled. That shifts the governance burden from static approval to runtime evidence, which is why logging and traceability now matter to both AI security and identity security programmes. Practitioners should treat observability as part of the control plane, not a reporting layer.
A question worth separating out:
Q: How should organisations respond when AI observability reveals shadow AI usage?
A: Treat shadow AI as an unsanctioned access path, not just an inventory issue. First, identify where the model is being used, what data is flowing through it, and whether any service accounts or embedded tools are involved. Then move the workflow under governed policy and runtime enforcement before normal use becomes entrenched.
👉 Read our full editorial: AI observability is exposing the limits of model oversight