How to Cut Through Vendor Claims & Marketing Hype When Evaluating New Security Tools

As we’ve pointed out in a couple of recent blog posts, Machine Learning (ML) has been billed as a savior for short-staffed security teams — a silver bullet that can single handedly identify and mitigate every security threat automatically. As we usually do with silver bullet solutions, we’ve cautioned readers to distinguish between the hype and reality. While ML has many strengths and is here to stay, it’s only a part of the solution in the world of cybersecurity — not the solution itself. Human input is still essential to draw meaningful conclusions and define appropriate action.

In today’s post, we’re continuing to advise readers that it’s essential to go below the surface, to distinguish between the hype and reality, when evaluating a cybersecurity solution. Remember: A beautiful package may open up to reveal a beautiful can of worms. Keep your eyes open, investigate below the surface, and avoid nasty surprises. Read more “How to Cut Through Vendor Claims & Marketing Hype When Evaluating New Security Tools”

The Promise of Machine Learning vs. The Reality of Human Assisted Learning

Machine Learning (ML) has been around in one form or another for a long time. Arthur Samuel, started working in the field in 1949 and coined the term in 1959 while working at IBM. Over the years, ML applications have been developed in practically every industry sector.

Recently, we’ve been hearing a lot about “silver bullet” ML-based cybersecurity solutions that can single handedly and automatically enable short-staffed security teams to identify and mitigate every kind of security threat imaginable. Of course, silver bullet solutions are as old as security itself, and by definition, they’re almost always too good to be true. So is the current crop of ML-driven cybersecurity solutions real or hype?

Given that a lot of hype has a few grains of truth in it, let’s use this post to look at the promise, the marketing hype, and the reality — at what ML can do and cannot do in its current state (with a peek at what it might be able to do sometime down the road). (Spoiler Alert: The operative word in this blog’s title is “promise.”) Read more “The Promise of Machine Learning vs. The Reality of Human Assisted Learning”

The Difference Between Security Trick Plays and Security Fundamentals

I like watching great football plays on YouTube, but I especially like watching trick plays where players sell some sort of deception so their opponents take their eyes off the ball. Trick plays make great video clips and can win a football game if deployed at the right moment, but there’s a reason “blocking and tackling” are the fundamental skills, tasks, and roles necessary to function. Trick plays might be able to help a team win a football game, but if you show up without “blocking and tackling,” you’re definitely going to have a bad day. I bring this up because sometimes we confuse the trick plays with the fundamentals, and we do so at our own peril. That does not mean trick plays are bad or not helpful; it just means we can’t forget about the “blocking and tackling.”

These days we hear a lot of hullabaloo about machine learning (ML), and with good reason. However, it’s quickly becoming the “trick play” of security, the flashy new toy that leads people to overlook the “blocking and tackling” fundamentals. Read more “The Difference Between Security Trick Plays and Security Fundamentals”