What is Drift Detection? — Plain-Language AI Definition

What is Drift Detection?

Drift detection is how teams notice when an AI system is starting to behave differently from expected. It looks for changes in data, predictions, or user outcomes that suggest the model may be losing quality.

In practice, drift detection is an early warning system.

Why It Matters

Without drift detection, teams often discover problems too late. By the time users report quality issues, the model may already be hurting trust, support costs, or business performance.

Good drift detection shortens the time between "something changed" and "we know what to fix."

What Teams Monitor

Common signals include:

• changes in input distributions
• drops in accuracy or acceptance rate
• increases in fallback usage or manual correction
• shifts in safety failure rates
• changes in latency or cost patterns tied to new behavior

How It Works

Teams usually compare recent production behavior against a baseline. If the gap crosses a threshold, the system triggers an alert, runs deeper evals, or routes traffic into a safer fallback flow.

Key Takeaway

Drift detection is not just for data scientists. It is a product operations discipline. If the model matters to the user experience, you need a way to know when that experience starts slipping.