FEATURE: CYBERSECURITY
Most IT professionals want AI/
ML to bolster defences
According to the Capgemini Research
Institute, nearly two-thirds of senior IT
executives don’t believe they can identify the
evolving threat landscape without the help of
AI/ML. IT executives at three out of five firms
state that AI/ML improves the accuracy and
efficiency of their cybersecurity analysis.
Many enterprises hope to fill the
cybersecurity skills gap with AI/ML.
beyond those hours and/or a different type
of data is suddenly being transmitted, AI/ML
would spot the irregularity in real-time.
In a situation like this, human decisionmaking
may still be needed. The obvious
decision may be to shut down an unusual
data flow right away and potentially thwart
bad actors before they can do any damage.
However, doing so may drastically disrupt
important operations. While AI/ML alerts a
cybersecurity team to the irregularity, one or
beginning to lift, many businesses will
continue to operate remotely until it
is deemed safe for their workforces to
travel. As companies continue to search
for solutions to combat increased
cyberthreats, business leaders must ensure
they are fully informed on the solutions
they choose.
AI and ML can help automate the fight
against large-scale cybersecurity threats
by tracking, uncovering and acting on
EVEN THE SMARTEST HACKER IN
THE UNIVERSE COULDN’T ACHIEVE
VISIBILITY OF ALL NEW THREATS
BECAUSE SO MANY NEW ONES
EMERGE SO FREQUENTLY.
Even the smartest hacker in the universe
couldn’t achieve visibility of all new threats
because so many new ones emerge so
frequently. Hence AI/ML have become so
important in helping enterprises maintain
organisational resilience.
As an example, some Software-as-a-Service
(SaaS) backup solutions for enterprises can
apply ML algorithms to analyse backup
patterns and metrics, which in turn can
help those backup solutions automatically
identify a ransomware or other malware
attack before it’s too late. Backup and
recovery execution can become more
intelligent and automated with AI/ML to
better accommodate unique considerations
for each enterprise’s recovery process.
Organisations still need the
human touch
One way AI/ML can help is by analysing
vast amounts of cybersecurity-related data
to identify patterns and spot irregularities.
Large amounts of threat data can be
collated and parse through it on a constant
basis to see the changing nature of
the threat landscape. For example, an
organisation might routinely transmit data
between China and Romania between
certain hours, but if data is transmitted
more team members may still need to make
a judgement call based on their knowledge
of the enterprise’s priorities, the operations
potentially impacted and the resilience risks.
To be resilient, organisations
need a data-oriented culture
To fully extract the resilience benefits of AI/
ML, organisations must develop a culture
oriented towards business analytics.
As Big Data continues to grow, resilience
threats escalate and AI/ML is increasingly
deployed, it is imperative for teams to
remain on the same page. Having a brilliant
cybersecurity team that can analyse all the
threat data and develop the ideal solutions
for resilience is not enough alone. They still
need to really understand the organisation’s
business imperatives. And other teams need
to understand where the organisation’s
vulnerabilities are. If all teams have a
data analytics mindset, together they can
proactively determine what needs to be
improved in the organisation’s resilience
risks and to prioritise those improvements.
Above all, don’t forget
the basics
COVID-19 is not going away anytime
soon. Even as lockdown measures are
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