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AI and Machine Learning: Separating Fact from Fiction

Are AI and ML really changing the game, or are they just fancy buzzwords?

August 9, 2018

3 Min Read
Machine learning

In the cybersecurity space, there’s much ado about artificial intelligence (AI) and machine learning (ML) these days. Competitors are doing everything they can to let their customers know that their AI and ML is, in fact, one step ahead of the other guys’. So, is it all just a marketing stunt? Hyperbole for the purpose of sounding sufficiently cutting edge? Or are these technologies that have the power to transform how we think about cybersecurity?

The answer is yes.

AI and Machine Learning: Defining Our Terms

Before unpacking this confusing answer, we need to agree on some terms. Much of the confusion surrounding the AI/ML debate centers on loosely defined terms being thrown around without much care for accuracy or precision. Simply defining what you mean when you make claims about this topic goes a long way toward clearing it up.

The IT analysis firm 451 Research defines AI and ML as follows:

  • Artificial intelligence is the quest to build software running on machines that can “think” and act like humans.

  • Machine learning is a subset of artificial intelligence focused on using algorithms that learn and improve without being explicitly programmed to do so.

Another way of thinking about the relationship between AI and ML is that ML is the part that’s interfaced with and manipulated in the pursuit of achieving AI.

Real machine learning leverages big data–ideally, terabytes of data from millions of real-world endpoints, to shorten the time from observation to action. It uses contextual analysis of URLs, IPs, apps, files and more to determine the threat they pose in seconds.

As an MSP, What Can ML Do For You?

Now that we’ve defined what we mean when we refer to AI and ML, we’re closer to answering the question of whether they’re really changing the game or just fancy buzzwords.

Many of the small and midsize businesses (SMBs) that make up an MSP’s client base unfortunately sit in a sweet spot for malicious actors. They’re established enough to be worthwhile targets, but lack the security resources of already well-served enterprise-level corporations. Teams of threat researchers, security and network operations centers, and the means to find and recruit tech talent are often beyond them. Hacking, ransomware, untrained users and a litany of other threats are continuously looming.

This is where the legitimate value of ML comes into play. With real ML, MSPs can:

  • Create new cybersecurity capabilities that allow them to expand their offerings

  • Reduce the time it takes to detect and remediate the threats facing clients

  • Alleviate staffing skills shortages by automating repetitive tasks

  • Decrease costs by freeing up man hours

  • Drive value by increasing overall efficiency while strengthening cybersecurity posture

Nothing without Experience

ML is only as good as the data it’s fed. Here, volume and duration matter. Webroot has been feeding and refining its machine learning models for nearly a decade–longer than almost anyone else in the industry. So it’s led to some head scratching here when AI and ML are talked about like the next best thing. That may be true, but it’s been so for a while now.

That’s why we create security products that leverage real threat intelligence–to arm our customers with enterprise-grade security services that enable greater automation and reduce dependence on fallible human resources, so MSPs can provide their clients with the security services they deserve.  

To learn more about how Webroot uses AI and ML in its security offerings, click here.

This guest blog is part of a Channel Futures sponsorship.

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