AI Telemetry Explained: Why Logging is the First Step to Security

AI telemetry is the systematic capture and analysis of interaction data between users and Large Language Models (LLMs). Unlike traditional application logging, AI telemetry requires a deep understanding of prompt-response dynamics — and the security risks that live in that space.

Why capture AI telemetry?

Without telemetry, your enterprise is blind to how AI is being used. Are developers pasting PII? Are agents hallucinating into internal databases? Telemetry provides the "Splunk-like" visibility needed to answer these questions with deterministic evidence.

• Discover which LLM endpoints are in active use across your organisation
• Identify prompt patterns that indicate data leakage or policy violation
• Build immutable evidence for compliance and governance frameworks
• Track token spend and cost attribution by team or project

Implementing the proxy layer

At AixSafe, we believe the best place for telemetry is at the proxy level. This ensures that every request is intercepted and logged without requiring developers to change their code. The proxy sits transparently between your applications and the LLM vendor API.

A proxy-level approach gives you complete coverage — including shadow AI usage that developers route to LLMs without IT knowledge. Application-level SDKs only log what developers explicitly instrument.

What to log

• Request timestamp and unique trace ID
• User or service identity (hashed or tokenised)
• LLM vendor endpoint and model version
• Token count (prompt + completion)
• Policy decisions (allowed, blocked, redacted)
• Completion hash for integrity verification