LATEST INTELLIGENCE
CISOS INVESTIGATE: USER
BEHAVIOR ANALYTICS
U
ser Behavior Analytics (UBA) was
born in part due to identity and
access management’s (IAM)
inability to provide thorough data analysis.
The earlier generation IAM solutions
were unable to parse the data effectively
across complex networks and provide
normalisation. As IAM matured, UBA
evolved, increasing its ability to provide
normalisation and more importantly to
identify anomalies.
A great example of the evolution of User
Behavior Analytics comes from the retail
space. Initially used for marketing to identify
users’ interests and spending patterns, it
became clear that the intelligence garnered
could be utilised for other purposes within
the business.
As losses mounted from fraud and abuse, the
industry put a focus on and became smart
about transaction analysis. A purchase in
New York City and then, seconds later, one
from Paris, France, is an abnormal spending
pattern. The developers of UBA took note of
this capability and the opportunity to pinpoint
discrepancies and applied it increasingly to
other needs within the business, in particular
security and compliance.
Online banking is another fantastic
example of how UBA was able to learn
from previous challenges. The mid-
2000s saw the FFIEC (Federal Financial
Institutions Examination Council) require
adaptive authentication for online banking
transactions. For example: someone logging
into their account may normally check their
balances and then transfer funds. However,
an attacker may log in, edit account records
and wire money. UBA technology alerts
generated based on the observation that
these are two entirely different behaviour
patterns is a result of the technology being
applied to new challenges. n
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