AST April 2018 Magazine AST April Magazine (3.30.18) | Page 14

GRA
applies a risk-based
Volume 22
approach for certifica-
tions, access requests and
approvals to identify and
remove excess access, ac-
cess outliers, and orphan
and dormant accounts.
April 2018 Edition
By uniquely combining
UEBA with IdA, GRA iden-
tifies with precision the
compromise and misuse
of identity, which is the
root of most modern cyber
threats.
Taking machine learn-
ing to the next level,
GRA includes 300+
ready-to-use machine learning models for
on-premises, cloud or hybrid environments.
Gurucul STUDIO, a unique part of GRA, enables orga-
nizations in high security industries like government,
intelligence, law enforcement, etc. to define custom
machine learning models to meet their specific re-
quirements, customize risk weightings and develop
their own machine learning models without any cod-
ing.
An industry-first, GRA’s Self-Audit capabilities em-
power government agency end users to monitor
their own accounts for anomalous and suspicious
access and activity.
Another area where GRA excels over the compe-
tition is in privileged access management (PAM).
Traditional PAM solutions perform discovery at the
account level.
However, many organizations assign high privi-
lege entitlements to “normal” accounts as well.
Manually discovering high risk entitlements that ex-
ist outside of privileged accounts is impossible.
Consider an organization with 10,000 identities,
where each identity has 10 accounts with 10 enti-
tlements.
That would equal 1 million entitlements.
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