accurate view. It frees up analysts to focus on genuine threats, supports faster root cause analysis and enables a more proactive security posture. For stretched SOC teams, it’ s a welcome evolution.
But these systems aren’ t‘ set-andforget’. Models drift. Contexts shift. And clients’ needs evolve. If no one is validating the outputs, tuning the thresholds or questioning the results, the quality of protection can quietly degrade.
Regular audits of model performance are therefore essential, not just in terms of false positives, but false negatives too. Teams should be manually reviewing anomalies that the system didn’ t flag, running routine spot-checks, and understanding the logic behind the decisions being made. Crosstraining staff in how these models work will help them question and interpret outputs with a critical eye, rather than treating alerts( or the absence of them) as infallible truth.
Honing the human edge
So how can MSPs keep their teams engaged and sharp in an environment that feels increasingly automated?
The trick is to actively design opportunities for engineers to stay sharp, even in a world with fewer alerts. Start with something as simple as post-incident reviews. When the system catches something, don’ t just thank the algorithm and move on. Walk through the detection journey. What signal tipped it off?
What data did it correlate? Would a human have caught it, or missed it?
Tabletop exercises are another useful strategy. Simulate events that the system isn’ t designed to detect – such as a partner API going rogue, or a rogue insider gradually escalating privileges. Challenge engineers to spot the signals the system might miss, such as a shift in ticket tone from a customer, or a repeated pattern of minor access anomalies across unrelated endpoints.
There’ s also value in encouraging a broader view. Some threats don’ t leave neat logs or signatures. Socio-technical factors such as a stressed insider, a business partner behaving oddly, a sudden shift in external communication might not trigger the system, but they matter. These are the kinds of patterns humans are particularly skilled at detecting.
These exercises don’ t need to result in a major discovery. They’ re about maintaining the mindset that not everything worth noticing will appear in an alert.
A smarter SOC is still a human one
Ultimately, ML-based monitoring should be seen for what it truly is: a force multiplier. It doesn’ t replace your SOC analysts, it amplifies them. It gives them time back. It removes the drudgery. It lets them focus on high-impact work.
The smartest MSPs won’ t be the ones who lean back and let the platform run the show. They’ ll be the ones who lean in, using ML to elevate their teams, not sideline them. They’ ll invest in both the tech and the talent. And they’ ll build SOCs that are not only more efficient, but more resilient, more curious and better prepared for the threats the models haven’ t seen yet.
Because at the end of the day, it’ s not about reducing alerts, it’ s about staying alert. •
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