IDG Contributor Network: Machine learning: It’s all about the use cases [baby]!

There’s no question that we’re poised at the dawn of a very exciting time as it relates to the application of machine learning within the context of IT security. That said, without enabling end users to focus these capabilities on concrete use cases, the overall impact of this revolution may be compromised.

Whether or not that concession represents more an issue of perception, versus an impact against the underlying value proposition of this technology is open to debate. However, with machine learning – and, of course, AI – currently infecting the marketing language of so many technology pundits and providers, we’re already awash in the hype cycle.

The truth is that machine learning will ultimately help practitioners either way, at least I believe so; but we also have an important opportunity to ensure the horse doesn’t get too far out of the barn in terms of inflated or misplaced expectations.

Separating the machine learning ‘wheat from the chaff’

A recent conversation with an industry analyst regarding some research he’s undertaken around artificial intelligence and machine learning drove this point home in spades. Experts such as this to whom practitioners turn to differentiate the wheat from the chaff [polite version] are already getting lots of call from security leaders to help understand what messages they should take seriously or take a pass on. Loosely, the question seems to be: “Where do I really need this stuff and how will it help?” Even worse, some have bought into the hype and are looking for the AI that will allow them to offload some human decision making. They are looking for “AI for the SOC.”