AI-assisted monitoring: catching problems before users do

Servers, network devices, applications and security tools all produce logs and metrics. Traditional monitoring relies on fixed thresholds — alert if disk usage exceeds a set percentage — which catches obvious problems but misses gradual or unusual ones.

What anomaly detection adds

AI-assisted tools learn what normal looks like for each system: typical traffic at different times, normal login patterns, usual error rates. They flag deviations, such as:

• A server whose memory use is climbing steadily week after week.
• Logins at unusual times or from unusual places.
• A sudden change in network traffic from a single device.

Keep engineers in charge

Anomaly detection suggests where to look; it does not decide what to do. Alerts should go to an engineer with context, and their feedback improves future alerts.

Start with good basics

AI does not replace fundamentals: complete inventories, centralised logs, sensible thresholds and someone responsible for responding.

Watch alert fatigue

Tune alerts so they are worth reading. Too many false alarms train people to ignore all of them.

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