Big Data Security

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Big data security
There are many advantages to be gained
through harnessing big data (see Figure
1), of which the most compelling
is increased operational efficiency.
According to the McKinsey Global
Institute, companies embracing big data
are able to outperform their peers.
2
It
estimates that a retailer that properly
harnesses big data has the potential to
increase its operating margins by more
than 60% by gaining market share over
its competitors by taking advantage of
detailed customer data. McKinsey states
that the prime advantages of big data
analysis are:
· Creating transparency by making
relevant data more accessible, such as by
integrating data from R&D, engineering
and manufacturing departments to
enable concurrent engineering to cut
time to market and improve quality.
· Enabling experimentation to discover
needs, expose variability and improve
performance by collecting more
accurate and detailed performance data.
For example, such data can be used to
analyse variability in performance in
order to understand the root causes so
that performance can be managed at
higher levels.
· Segmenting populations to customise
actions so that products and services
can be better tailored to meet actual
needs. For example, consumer goods
and services companies can use big
data analysis techniques to better
target promotions and advertising.
· Replacing/supporting human
decisionmaking with automated
algorithms to improve decisionmaking
and minimise risks by unearthing valuable
insights that would otherwise remain
hidden. McKinsey provides the example
of a retailer using big data analytics to
automatically fine-tune inventories in
response to real-time sales.
· Innovating new business models,
products and services. For example,
a manufacturer can use data obtained
from actual use of its products to
improve the development of the next
generation of products.
Beyond commercial organisations,
big data presents a number of other
opportunities, such as improving threat
detection capabilities for governments.
The US Department of Homeland
Security states that there has been an
explosion of data recently, helped by
expanding use of the Internet and social
networks, that can be mined to help
defend against growing threats from
foreign countries, terrorists, online
hacktivists and criminal elements, both
in the real world and in cyberspace. It
states that the Arab Spring revolutions
in the Middle East could have been
predicted by monitoring what people
were searching for and how they were
communicating online. By analysing big
data, governments will be better able to
understand the various threats that they
face, the likely vectors of attack and the
actors that might perpetrate them.
Security issues with big
data
One of the key security issues involved
with big data aggregation and analysis
is that organisations collect and process
a great deal of sensitive information
regarding customers and employees,
as well as intellectual property, trade
secrets and financial information. As
organisations look to gain value from
such information, they are increasingly
FEATURE
July 2012 Network Security
5
Colin Tankard
Colin Tankard, Digital Pathways
The term big data has come into use recently to refer to the ever-increasing
amount of information that organisations are storing, processing and analysing,
owing to the growing number of information sources in use. According to
research conducted by IDC, there were 1.8 zettabytes (1.8 trillion gigabytes) of
information created and replicated in 2011 alone and that amount is doubling
every two years.
1
Within the next decade, the amount of information managed
by enterprise datacentres will grow by 50 times, whereas the number of IT
professionals will expand by just 1.5 times.
Figure 1: What new opportunities does big data present?
FEATURE
6
Network Security July 2012
seeking to aggregate data from a
wider range of stores and applications
to provide more context in order to
increase the value of the data – for
example, to provide a clearer picture of
customer preferences in order to better
target them.
By centralising data in one place, it
becomes a valuable target for attackers,
which can potentially leave huge swathes
of information exposed, which could
undermine trust in the organisation and
damage its reputation. This makes it
essential that big data stores are properly
controlled and protected.
Another potential problem relates to
regulatory compliance, especially with
data protection laws. Such laws are
more stringent in some jurisdictions
than others, particularly with regard to
where data can be stored or processed.
Organisations need to carefully consider
the legal ramifications of where they
store and process data to ensure that
they remain in compliance with the
regulations that they face.
However, there are also security
advantages to big data projects. When
centralising data stores, organisations
should first classify the information
and apply appropriate controls to it,
such as imposing retention periods as
specified by the regulations that they
face. This will allow organisations to
weed out data that has little value or
that no longer needs to be kept so
that it can be disposed of and is no
longer available for theft or subject to
litigation demanding presentation of
records. Another security advantage is
that large swathes of data can be mined
for security events, such as malware,
spear phishing attempts or fraud, such
as account takeovers.
“Organisations should first
classify the information and
apply appropriate controls to
it, such as imposing retention
periods as specified by the
regulations that they face”
The data in Figure 2 illustrate the
security advantages of big data for
protecting data according to more than
180 IT security practitioners who were
surveyed by technology vendor Varonis
during the InfoSecurity exhibition in
London in April 2012.
3

Developing a holistic
approach
For most organisations, the volume of
big data generated and stored can be a
major challenge, with searching such
vast amounts of data – most of which
is unstructured – often taking weeks or
more using traditional tools. MeriTalk,
an online community for the US
Government IT community, recently
surveyed 151 federal IT professionals
regarding big data and found that nine
out of ten see challenges on the path
to harnessing big data.
4
When asked
what they have in place today compared
to what they believe will be needed
for successful big data management,
respondents stated that they had, on
average, 49% of the data storage and
access technology that they will need,
46% of the computational power
Figure 2: Use of big data for managing structured and unstructured information. Source: Varonis.
Figure 3: Most significant challenges in managing large volumes of information. Source: ‘The big
data gap’, MeriTalk, May 2012.
and 44% of the personnel. The most
significant challenges that they see in
managing such large amounts of data are
shown in Figure 3.
Prior to the start of any big data
management project, organisations need
to locate and identify all of the data
sources in their network, from where
they originate, who created them and
who can access them. This should be
an enterprise-wide effort, with input
from security and risk managers, as well
as legal and policy teams, that involves
locating and indexing data. This also
needs to be a continuous process so that
not just existing data is uncovered, but
also new data as it is created throughout
the network.
“Data classification can be a
complex, long and arduous
process – a factor that has been
a significant struggle for many”
The next step is to classify the data
that has been discovered according to
its sensitivity and business criticality.
However, data classification can be a
complex, long and arduous process
– a factor that has been a significant
struggle for many when attempting to
implement technologies that rely on
data classification, such as data leakage
prevention systems. Organisations also
need to take into account industry
standards and government regulations
to which they must adhere, ensuring
that records are retained and archived
for the time periods specified and
that data is protected according to
the guidelines contained in some
standards (such as PCI DSS, which
specifies that payment cardholder data
is held in a secure manner). To ease
the classification process, organisations
should look for automated database and
network discovery tools, which can be
used to scan networks to identify all
data assets.
As they go through the data
classification process, organisations
should also look to develop or update
policies regarding data handling, such
as defining what types of data must
be stored and for how long, where
they should be stored and how data
will be accessed when they are needed.
Enforcement of such policies will
prevent users from creating their own
data stores that are outside the control of
the IT department.
Data warehouses are popular
technologies for managing large
volumes of data. However, most rely
on a relational format for storing
data, which works fine for structured
data, but less so for unstructured
data. And unstructured data make up
a high proportion of data contained
in big data stores, as information is
increasingly drawn from a wide range
of sources beyond traditional enterprise
applications. For example, relational
databases are good at handling discrete
packets of information, such as credit
card numbers and employee identifiers,
but are less able to handle content
such as video or emails, which do not
necessarily conform to a rigid structure.
An alternative for organisations
looking to get a handle on big data
is to use an open source software
framework that supports data-intensive
distributed applications and can work
with thousands of systems in a network,
and petabytes of data. Currently,
Hadoop is one of the most popular such
choices among organisations. Hadoop is
particularly suited for storing the large
amounts of unstructured data contained
in big data stores and provides a large
set of tools and technologies that can aid
organisations in tackling the problems
involved in analysing massive swathes of
information, including enterprise search,
log analysis and data mining. Such
capabilities are critical to allowing data
to be retrieved quickly across structured
and unstructured sources.
“Separate silos of data
control and protection – such
as archiving, data leakage
prevention and access controls –
should be brought together”
According to a recent survey
undertaken by InformationWeek
among 431 respondents involved with
information management technologies,
there is a number of factors driving
interest in the use of Hadoop or
other NoSQL data management and
processing platforms, as shown in
Figure 4.
5
Big data security
controls
Research firm Forrester recommends
that in order to provide better control
over big data sets, controls should be
moved so that they are closer to the
data store and the data itself, rather
FEATURE
July 2012 Network Security
7
Figure 4: Factors driving use of Hadoop and other NoSQL platforms. Source: ‘How Hadoop tames
enterprises’ big data’, InformationWeek, February 2012.
FEATURE
8
Network Security July 2012
than being placed at the edge of the
network, in order to provide a more
effective line of defence. It also states
that separate silos of data control and
protection – such as archiving, data
leakage prevention and access controls –
should be brought together. In terms of
access controls, they should be granular
enough to ensure that only those
authorised to access data can do so, in
order to prevent sensitive information
from being compromised.
Controls should also be set using the
principle of least privilege, especially
for those with greater access rights,
such as administrators. Products
such as Vormetric bring together
data encryption and its related policy
management and key storage elements
and link access control to the data.
Therefore companies can decide who
can view the data or in the case of
an administrator allow them physical
access: but should they try to read
the data it would be useless because
the process would not have allowed
decryption. Such an approach is highly
effective in any multi-silo environment
where any form of electronic data is
stored.
“It is important that the legal
department be involved in
the development of policies
related to data retention
and disposal to ensure that
they are in compliance with
the requirements of industry
standards”
To ensure that access controls are
effective, they should be continuously
monitored and should be modified
as employees change role in the
organisation so that they do not
accumulate excessive rights and
privileges that could be abused. This
can be done using existing technologies
in use in many organisations such as
database activity monitoring tools,
the capabilities of which are being
expanded by many vendors to deal
with unstructured data in big data
environments. Other useful tools
include Security Information and Event
Management (SIEM) technologies,
which gather log information from
a wide variety of applications on the
network. To make SIEM tools more
effective and manageable, many
vendors, such as AlienVault, are
expanding their solutions to provide
capabilities called Network Analysis
and Visibility (NAV), which capture
and analyse network traffic to look for
potential attacks and malicious insider
abuse and are highly scalable across
large networks. NAV tools provide
useful add-ons to SIEM tools, such
as metadata analysis, packet capture
analysis and flow analysis. In the case
of AlienVault, further steps have been
taken in order to link the analysed
data and make proactive decisions in
preventing or stopping the breach.
Ensuring that data is archived as
required and disposed of when no
longer needed is another important
security consideration so that the
organisation is not managing overly
large volumes of data, and so the
risk of sensitive data being breached
is reduced. This can also be reduced
through use of techniques that make
sensitive data unreadable, such as
encryption, tokenisation and data
masking, so that only those with the
keys to unlock the data can do so. This
is a much easier task once data has been
properly classified, but it is important
that the legal department be involved
in the development of policies related
to data retention and disposal to ensure
that they are in compliance with the
requirements of industry standards and
government regulations.
Conclusions
As data volumes continue to expand,
as they take in an ever wider range
of data sources, much of which is
in unstructured form, organisations
are increasingly looking to extract
value from that data to uncover the
opportunities for the business that it
contains. However, traditional data
storage and analysis tools are not, on
their own, up to the task of processing
and analysing the information the data
contains, owing not just to the volume
of data, but also to the unstructured,
ad hoc nature of much of the content.
In addition, the centralised nature of
big data stores creates new security
challenges to which organisations must
respond, which require that controls
are placed around the data itself, rather
than the applications and systems that
store the data.
About the author
Colin Tankard is managing director of
data security company Digital Pathways,
specialists in the design, implementation
and management of systems that ensure
the security of all data whether at rest
within the network, mobile device, in
storage or data in transit across public
or private networks.
Resources
· 'Big data: harnessing a game-changing
asset’. Economist Intelligence Unit
(EIU), 2011. Accessed June 2012.
www.sas.com/resources/asset/SAS_
BigData_final.pdf.
References
1. ‘The 2011 IDC digital universe’.
IDC, 2011. Accessed June 2012.
www.emc.com/collateral/about/
news/idc-emc-digital-universe-
2011-infographic.pdf.
2. ‘Big data: the next frontier for
innovation, competition and
productivity’. McKinsey Global
Institute, 2011. Accessed June
2012. www.mckinsey.com/Insights/
MGI/Research/Technology_and_
Innovation/Big_data_The_next_
frontier_for_innovation.
3. ‘Big data and infosecurity’. Varonis,
2012. Accessed June 2012. http://
blog.varonis.com/big-data-
security/.
4. ‘Big data gap’. MeriTalk, 2012.
Accessed June 2012. www.meritalk.
com/bigdatagap.
5. Kajeepeta, Sreedhar. ‘Strategy:
Hadoop and big data’.
InformationWeek, 2012.
Accessed June 2012. http://
reports.informationweek.com/
abstract/81/8670/Business-
Intelligence-and-Information-
Management/strategy-hadoop-and-
big-data*.html.

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