E R T N
P
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E INIO
OP
more and more challenging. And if you
can’t figure out what something is in
order to label it good or bad, how can
you create a reliable profile and keep
operations moving?
The answer is to increase our focus on
context and Machine Learning.
If we can’t rely on being able to identify
exactly what is using our network, we
need to look at the behaviour of the
device instead. In many scenarios a
combination of what protocols a device
is using and what data, applications or
URLs it is accessing is the only way to
build up an accurate picture of what
the device actually is and whether the
device is malicious.
Step two: Build in Artificial
Intelligence to enforce
policy automatically
AI is also important in the next stage of
securing IoT – enforcing policy. Today’s
IT teams need closed-loop, end-to-
end access control from the moment
The answer is to
increase our focus
on context and
Machine Learning.
a device joins the network. Given the
sheer quantities of IoT devices, however,
manual intervention is no longer
practical. IoT devices are likely to be
operating around the clock, or with some
devices connecting at non-specific times
to carry out a task before returning to
sleep mode.
If a heart monitor on ward B begins to
transmit its data to a network across
the country at 3am, the reality is that
a manual monitoring process is highly
unlikely to catch the transfer in time
for the device to be quarantined and
investigated. Instead, deploying AI allows
teams to develop policies that leverage
context, such as the user role, device
AI is also important
in the next stage
of securing IoT –
enforcing policy.
type, certificate status, and location or
day of week, to make quick and accurate
decisions each and every time.
When an IoT device joins a network
or starts to act suspiciously, it can be
automatically segmented, keeping traffic
separate and secure, with the policy
consistently enforced across wired and
wireless networks.
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Issue 22
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