INTELLIGENT CABLING
Mechelle Buys Du Plessis, Managing Director
UAE, Dimension Data.
will see huge security gains through the
presence of deception technologies.
Deception technologies create
thousands of fake, user credential in
conjunction with real user-identities. Once
a threat actor is inside an organisation’s
network, they are unable to distinguish
between real and fake user identity
credentials. Since there are many more
fake user identity credentials distributed,
the probability of engaging with a fake
user identity credential and triggering an
intrusion alert is much higher.
Afterwards an incident response alert
and action are then initiated. The large
number of fake credentials generated
through deception technologies also
facilitate pattern tracking. This allows
internal teams to recreate the pattern of
attack and point of entry.
To further strengthen their cyber
security defences, digitally transformative
organisations will begin to tap the power
of artificial intelligence and machine
learning, to secure their networks. While
these buzzwords are already in place,
they have been defined by programmer-
built algorithms, limiting the amount of
self-learning.
Machine learning applied to
cybersecurity has traditionally been driven
by algorithms that give instructions on
the types of malware and their associated
behaviour inside internal networks. Now
machine learning will be replaced by deep
learning applied to cyber security.
With deep learning techniques, cyber
security applications are aided by self-
learning technologies. User behaviour
is monitored over a period of time using
deep learning technologies, and a user
behaviour profile is established.
This profile is a dynamic one and deep
learning technologies continue to add
usage patterns, till the profile becomes
intrinsic to a particular user. Deep
learning applications develop highly
granular patterns and analysis of end
user activities.
The presence of a threat actor
inside a network using an assumed
credential, will have a deviant user
pattern. This divergent pattern of
accessing the network, monitored by
behavioural analytics, will trigger a
security remediation alert without
delay. Examples of such proactive and
rapid approach to securing convergent
and transformative networks, will take
behavioural analytics applied to cyber
security to a new level.
With these intuitive gains around
the corner, cyber security vendors will
continue to integrate deep learning
technologies into their products in the
year ahead.
Artificial intelligence technologies will
also create a new generation of proactive
and defensive cyber security products
called Robo-hunters. Enabled by artificial
intelligence, Robo-hunters are automated
threat-seekers that scan an organisation’s
environment for potential threats. Since
they are built on predictive behavioural
analytics, they have available a baseline of
normal network activity behaviour.
Robo-hunters scan an organisation’s
environment for any changes that might
indicate a potential threat. As they scan
the environment, they learn from what
they discover, and take remediation action
as required.
Hence, they are built to make decisions
on behalf of humans. Robo-hunters also
help deliver a long-standing expectation of
the cyber security department, which is to
access threat intelligence and to track the
enemy within.
The cyber security stage is set. The
threat landscape is too fast moving, too
complex, and with enormously high
stakes, to rely on present day technologies
alone. Artificial intelligence coupled with
predictive analytics and high degree of
compute, as well as a trusted security
partner, will provide a welcome relief in
the not so distant future.
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