Monitoring and logging AI systems

By Sam Rivera, Founder, SentinelPanda · June 19, 2026 · 1 min read · AI Governance

A model that was fine at launch can quietly degrade as the world changes. Monitoring is how AI governance survives contact with production.

Why AI needs more than uptime monitoring

Traditional monitoring watches whether the service is up. AI systems can be fully available and still failing — drifting as the data distribution changes, producing degraded or biased outputs, or being misused. AI monitoring has to watch behaviour and quality, not just availability.

What to watch

• Performance and drift: is accuracy or behaviour degrading over time as inputs change.
• Anomalous inputs and outputs: signs of misuse, attack (prompt injection), or out-of-distribution use.
• Outcomes across groups, for systems where fairness matters.

Logging for accountability

Log inputs, outputs, and decisions (respecting privacy and minimisation) so behaviour can be reviewed, decisions explained or contested, and incidents scoped. This is the AI analogue of audit logging — and the foundation for responding when something goes wrong.

A regulatory obligation too

For high-risk systems, the EU AI Act requires post-market monitoring — ongoing watch over how the system performs in the real world. So monitoring is not just good practice but, for some systems, a legal requirement. SentinelPanda tracks the AI monitoring controls and their evidence per system.