Artificial Intelligence, Machine Learning
and Deep Learning are phrases which
are being referred to more and more
DEREK LIN, CHIEF DATA SCIENTIST
AT EXABEAM, looks past the hype and
outlines the benefits of what each of
these technologies have to offer.
When it comes to Artificial Intelligence
(AI) and Machine Learning (ML),
there’s no shortage of buzz and hype.
Often referred to interchangeably,
Artificial Intelligence and Machine
Learning are part of our daily reality
and technology lexicon – whether it’s
in a product marketing pitch or a Netflix
recommendation for which film to see.
In cybersecurity, as these and other
emerging technologies like Deep
Learning (DL) evolve, their capabilities
right questions and demanding to know
what constitutes reality.
In order to ask the right questions, let’s
start with a correct understanding of the
terminology. Despite all the marketing
messaging, for many of us it isn’t always
clear what some terms may mean.
When it comes to
(AI) and Machine
there’s no shortage
of buzz and hype.
AI is often misunderstood and not
everyone agrees on its meaning. The
term Artificial Intelligence first appeared
in the 1950s to describe systems
comprising a set of human-defined, if/
then decision rules – which have always
been easily broken and hard to maintain.
have become a driving force shaping
modern cybersecurity solutions.
At the same time, security practitioners,
fatigued by the barrage of AI and ML
messaging, are raising suspicions about
At the InteropITX conference in 2018,
panellists echoed the same sentiment
about the hype, asking what can be
legitimately claimed as AI. The audience
was encouraged to look beyond the
marketing spin and find out what’s really
I’m glad to see the hype cycle has
reached its peak. It’s a healthy sign that
security practitioners are asking the
For example, static correlation rules that
raise alerts – used in traditional security
information and event management
(SIEM) – cannot learn and adapt. This
results in a high number of false positives.
Such AI systems appear to be intelligent
in their decision-making because they
make decisions. But in reality, they’re
100% predetermined (based on static
rules) and are drafted by humans.
But the word ‘intelligence’ has stuck
with the public since AI’s introduction.
Why not? It sounds cool. Yet today AI is
often little more than a catchy marketing
label, liberally applied to any system that
performs tasks having some semblance
of automated decision-making.
This is all the rage today. As with
AI, Deep Learning evokes an air of
sophistication, but it’s also subject to
misunderstandings. As a tool within