How Machine Learning Makes MSPs Smarter

“An ounce of prevention is worth a pound of cure” may seem like a corny proverb from a bygone era, but it’s remarkably relevant to today’s managed service providers. Customer satisfaction is the cornerstone of every MSP’s success, and there are few things that can undermine client confidence more than an MSP who always seems to be reacting to problems, rather than preventing them in the first place.

The point is that MSPs who are perpetually “putting out fires” probably won’t stay in business very long. As veterans of managed services well know, clients prefer a drama-free experience. They aren’t impressed by the technological heroics required to overcome a particularly challenging IT problem. They want the problem to never occur at all.

Standing in the way of achieving this goal is the massive growth in the malware and viruses that MSPs constantly combat. Thankfully, the increasing adoption of next-generation antimalware technology, such as machine learning, is turning the tables in your favor. By analyzing massive data sets and identifying patterns quickly, machine learning helps endpoint protection vendors create accurate predictions and detect behaviors that may be associated with malware or other attacks—before attacks can occur.

Why Machine Learning is Key to Effective Endpoint Security

As the threat landscape continues to evolve in speed, sophistication and persistence of attacks, it’s clear that traditional security solutions are being overwhelmed by the flood of new attack vectors, new variants of polymorphic malware and crypto ransomware. The vendors of those solutions can’t hope to keep up when armed only with manual identification, classification and pattern matching techniques.

The answer is machine learning. Machine learning applies advanced mathematical algorithms and powerful computing capabilities to interpret the massive quantity of data collected by security solution vendors, then processes it to produce predictable outputs. Machine learning also deciphers the data to identify the patterns, make sense of them, and enable security tools and personnel to take actions. Doing so in real or near-real time, this approach can help prevent breaches from occurring successfully in your clients’ businesses.

Bear in mind: Implementing this methodology does require commitment from security solution vendors. Tuning a machine learning model is complex and challenging work, and there’s no substitute for time and experience when developing an accurate machine learning model. The key is continually refining the algorithms. Data is fed into the model for training while human intelligence and analysis continually fine-tune it.

This process—known as “supervised machine learning”—takes highly skilled machine learning experts, data scientists and statisticians, all working to train and test the algorithms over time. While the refinement process can be very involved, it increases both speed and accuracy, and enables machine learning systems to produce truly meaningful and actionable results.

Protecting Your Clients with Machine Learning

We’ve only scratched the surface of how machine learning works, and how it is revolutionizing the way endpoint security solutions will protect your clients. To learn more we encourage you to download the Webroot white paper Automating Threat Defense: Using Machine Learning to Prevent Modern Cyberattacks.

To learn how Webroot has been breaking ground with this exciting technology, and how our unique combination of cloud-based endpoint security and threat intelligence enables us to maximize the benefits of machine learning, tune in for our next blog post. It will feature excerpts from an interview with Webroot CTO and rocket scientist Hal Lonas, who explains why “machine learning is the only way to solve the growing threat problem.”

Guest blogs such as this one are published monthly and are part of MSPmentor’s annual platinum sponsorship.