Machine Learning monitoring can cure alert fatigue – but are SOCs ready for the side effects?
MSPs are increasingly turning to Machine Learning-based monitoring to cut through the noise of traditional alert systems and improve detection of genuine threats. Ziad Nasr, General Manager, Acronis Middle East, tells us how organisations can harness Machine Learning( ML) to enhance Security Operations Centre( SOC) efficiency while ensuring analysts remain engaged and vigilant.
For years, managed service providers( MSPs) have battled with alert fatigue, that endless deluge of red flags from traditional monitoring systems that often turn out to be noise rather than meaningful threats. At best, it’ s a distraction. At worst, it numbs response teams to real incidents, turning security operations centres( SOCs) into environments where everything blares and nothing gets prioritised.
Enter Machine Learning( ML)-based monitoring, and with it, the promise of clarity. Smarter systems, fewer false positives, quicker detection of anomalies. It’ s a long-overdue evolution in how MSPs manage risk and uptime. But as MSPs embrace this new monitoring paradigm, it’ s worth asking a harder question: what are we giving up in exchange for this new precision?
Because while ML is solving one problem, it may be quietly introducing another.
From too much noise to not enough sound
Traditional monitoring tools don’ t discriminate. A spike in CPU usage, an unusually timed login or a failed update patch,
Ziad Nasr, General Manager, Acronis Middle East even when harmless, can trigger an alert. Now multiply that across multiple clouds and thousands of endpoints, and it’ s easy to see how a Monday morning can spiral into a fog of notifications, dashboards and fire drills. For many MSPs, that’ s not a hypothetical, it’ s just another start to the week.
ML-based monitoring promises something radically different. These systems learn over time, filtering out background noise, clustering related events, and flagging anomalies that actually warrant attention.
The result is not just fewer alerts, but better ones, and a clearer picture of what’ s going on beneath the surface.
But herein lies the catch: what happens when the alerts stop coming?
For SOC engineers, and especially junior staff, the constant stream of notifications wasn’ t just a nuisance, it was much needed hands-on education. Each false positive was a chance to explore, to ask why something flagged, to get under the hood of the system. With fewer alerts, we may be losing a key training ground. And worse, teams may start to assume the system has it all under control. That’ s when silence becomes dangerous.
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