Lack of visibility is the primary
reason why organisations
struggle to understand the
scope and impact of attacks.
The investigation process can be a
slow and painful one. This of course
assumes an investigation occurs at
all. Incident response traditionally
relies heavily on highly-skilled
human analysts. Most EDR tools
also rely heavily on analysts to know
which questions to ask and how to
interpret the answers.
However, with Deep Learning
enabled EDR, security teams of all
skill levels can quickly respond to
security incidents thanks to guided
investigations that offer suggested
next steps, clear visual attack
representations and built-in expertise.
It adds expertise without
adding headcount
By a large margin, organisations
looking to add endpoint detection
and response capabilities cite ‘staff
knowledge’ as the top impediment to
EDR adoption.
To combat the staff knowledge
gap Deep Learning enabled EDR
replicates the capabilities associated
with hard-to-find analysts. It
leverages Machine Learning to
integrate deep security insight, so
organisations can add expertise
without having to add staff.
It helps in understanding
how an attack happened
and how to stop it from
happening again
Threat cases, included with EDR,
spotlight all the events that led up
to a detection, making it easy to
understand which files, processes
and registry keys were touched by
the malware to determine the impact
of an attack. More importantly, by
understanding the root cause of
an attack, the IT team will be much
more likely to prevent it from ever
happening again. u
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Issue 11
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