Big Data and Information Security Risks

This assignment delves into the intersection of big data and information security. It examines the unique challenges posed by big data to traditional security measures, including increased attack surface, complex data breaches, and evolving threat landscapes. The assignment requires an in-depth analysis of these risks, exploration of mitigation strategies such as encryption, access control, and data anonymization, and the evaluation of best practices for secure big data management.

Running head: ENISA BIG DATA THREAT LANDSCAPE
ENISA Big Data Threat Landscape
Name of the Student:
Name of the University:
Author Note:

1ENISA BIG DATA THREAT LANDSCAPE
Executive Summary
The accompanying report examines about the dangers related with the security of Big Data.
The chosen association is the European Union Agency for Network and Information Security
(ENISA). The report additionally incorporates a format of the security design of ENISA. The
report also talks about the best risk assessments related with the association. Likewise, the
report examines about the present infrastructure of the concerned organisation and the
procedures that ought to be executed to give better security benefits and improved highlights.

2ENISA BIG DATA THREAT LANDSCAPE
Table of Contents
1. Introduction............................................................................................................................3
2. Overview on ENISA and the Big Data Threat Landscape.....................................................3
3. Most Significant Threat to Big Data, Threat Probability and Possible Remedies.................6
4. Discussion on Principle Threat Agents..................................................................................6
5. Improvements to the ETL Process.........................................................................................9
6. Response of ENISA to its current state of IT security.........................................................11
7. Conclusion............................................................................................................................12
8. References............................................................................................................................13

3ENISA BIG DATA THREAT LANDSCAPE
1. Introduction
Data is the most important resource behind the proper and continuous functionality of
an institution irrespective of its dimension and field of operation (Chen, Chiang & Storey,
2012). The institution can be as small as a business or as large as a whole nation. Information
is essential to operate any kind of business, governmental or national operations. Not all data
carry importance, however, most information that is used for the performance of critical
operations in any institution consist of higher level of confidentiality (Ifinedo, 2012). The
security of such data is necessary to ensure that it does not fall in the wrong hands. Cyber
criminals are always on the look out to retrieve such kind of information that can provide
them financial benefit or aid them in carrying out other criminal activities of larger proportion
against any target institution.
The advent of Big Data concept has undoubtedly brought huge benefit to every
institution. However, its popularity has attracted various cyber threats and the lack of a proper
infrastructure and ignorance of the common mass to the gravity of the situation has helped
the threats to evolve rapidly (Peltier, 2013). The purpose of the report is to provide the
current scenario of cyber crime and its impacts on Big Data in the light of the threat
assessment report created by the European Union Agency for Network and Information
Security (ENISA).
2. Overview on ENISA and the Big Data Threat Landscape
The European Union Agency for Network and Information Security (ENISA) is
formed to house network and information security experts for the European Union, its
member states, the private sector and the inhabitants of Europe (Marx, 2013). ENISA is in
collaboration with these groups to obtain advice and suggestions on good practice in

4ENISA BIG DATA THREAT LANDSCAPE
information security. It provides support to the states that are in membership with EU in
implementing relevant EU legislation and works to enhance the flexibility of the critical
information infrastructure and networks of Europe (Big Data Threat Landscape and Good
Practice Guide, 2016). ENISA is trying to improve the current expertise in EU member states
by assisting in the improvement of cross-border communities that is devoted to enhancing
information security and network across the EU.
The concept of Big Data includes the digital analysis of extremely large data sets to
identify patterns, associations and trends that are related to interactions and human behaviour.
The data sets can be obtained from multiple sources (Chen, Chiang & Storey, 2012). For
example, mobile telecommunication devices and networks, distributed multimedia sensors on
the Internet of Things, web-based applications, distributed business processes are some of the
sources that facilitate the resources of Big Data (Boyd & Crawford, 2012). The applications
of Big Data are useful in providing exponential development in the efficiency and
effectiveness of decision-making in organisations and communities during complicated
situations. Due to its beneficial characteristics, it is often subjected to security risks (Dumbill,
2013). The report identifies the existing landscape of security in the field of Big Data.
The diagram provided below shows the infrastructure of ENISA Big Data security.

5ENISA BIG DATA THREAT LANDSCAPE
ENISA
BIG DATA CATEGORIES
Partially Configured
Information
Configured
Information
In configured
Information
Sensor Streaming
Information
INFORMATION WAREHOUSE
SQL Database RDF Stores Separate File Systems
Firewall
Encryption
Desktop Internet Services Mobile Devices Web Browsers
Figure 1: ENISA Big Data Security Infrastructure
(Source: Created by the author)

6ENISA BIG DATA THREAT LANDSCAPE
3. Most Significant Threat to Big Data, Threat Probability and Possible Remedies
Among all the threats that concern the security of Big Data, the most significant threat
is interception of data by methods such as eavesdropping, interception and hijacking. This
threat is considered as the most essential enemy to Big Data as the operation of Big Data
deals with a large amount of big data sets (Fenz et al., 2014). The data sets that are obtained
for analysis often contain sensitive information that is not for general use. Interception of
such data may cause great issues for the organisation. The intercepted data may contain
government and military secrets that will be of great interest to other nations and criminals
and hence can fuel future criminal activities. An example can be provided in support of the
scenario (Ahmed & Matulevicius, 2014). Suppose a highly confidential data containing a top-
secret military project of a nation is being analysed. Due to poor security, the data is
intercepted by a hacker. Now the data will be of great importance to the rivals of the nation
and in case the hacker exposes the data to those individuals for financial benefits, the military
organisation will be heavily affected, which in turn will affect the whole nation (McAfee,
Brynjolfsson & Davenport, 2012). In case of organisations, the damage is not nation-wide,
although the organisation may be fatally affected; this may even lead to shutting down of the
same (Chen, Chiang & Storey, 2012). Therefore, it is evident that data plays a vital role for
almost all the operations in every organisation. Hence, a large amount of damage can be
caused to an organisation or a government by exposing sensitive data related to the same.
4. Discussion on Principle Threat Agents
The key threat agents of Big Data are as summarised below:
 Cyber Criminals – These individual carry out hostile activities for financial profit. They
have high skill sets in this respect and can be organised on a local, national and
international level (Gantz & Reinsel, 2012).

7ENISA BIG DATA THREAT LANDSCAPE
 Script kiddies – These individuals are not hacking experts themselves but use tools that
are developed by others to carry out attacks on networks and systems.
 Corporations – some organisations conduct cyber crimes to achieve their goal. These
organisations generally perform such actions for achieving competitive advantage against
rival organisations by causing harm to the competitor rather than developing its own
business model (John Walker, 2014).
 Cyber Terrorists – These individuals or group of individuals carry out terrorist activities
through cyber attacks specifically with political or religious motivation. The prime targets
of these groups are the most essential parts in a society such as the healthcare centres,
energy production industries and the telecommunication services (Boyd & Crawford,
2012). This is because these are the sectors whose damage will directly affect the society,
which in turn will affect the government as well.
 Employees – The employees of an organisation can be considered as threat agents in both
hostile and non-hostile terms. The hostile agents carry out cyber attacks on the
organisation intentionally due to any grudge or dissatisfaction that the employee
possesses against the company (Labrinidis & Jagadish, 2012). The non-hostile agents are
referred to those employees that carry out cyber attacks against their organisation
unintentionally. These attacks are generally caused due to various reasons such as
distraction and such.
 Online Social Hackers – These individuals or groups are generally disgruntled due to
some injustice that has been done to them by the society or the government. They use the
cyber-world to bring their grudge and demands to the public. Their target is generally
intelligence agencies, high profile websites, organisations and military institutions (Lazer
et al., 2014).

8ENISA BIG DATA THREAT LANDSCAPE
 Nation states – There are whole nation states that can be involved in cyber crimes. Such
actions are performed generally against another rival nation (Lohr, 2012). This threat
agent is considered to be the most critical threat agent at present as a whole nation state
will contain a great deal of resources at its disposal to carry out a global cyber attack that
can affect a great deal of nations and its people.
To minimise the cyber threats mentioned above, the organisations, governments and
even a nation needs to bring certain reforms as well as adopt new strategies to their system
(McAfee, Brynjolfsson & Davenport, 2012). General awareness is necessary among every
individual in a society regarding the growing threats to the cyber world and the things
connected to it (Marx, 2013). Every individual in the current world situation needs to be
properly trained to fight the growing cyber crime. Additionally, the infrastructure of the
present security services needs to be upgraded considerably so that the cyber attacks can be
prevented before it is conducted. The most vital step to mitigating cyber threats is to be able
to predict the time, type and location of attack that is going to be conducted (Marz & Warren,
2015). Relevant analysis tools that can predict these three parameters needs to be developed
and implemented so that it will reduce cyber threats to near extinction.
The figure provided below shows the trends in threat probability. Three symbols
denote the various aspects in the threat probability. The darkened dots denote the main threat
agents that are exploiting the mentioned threats. The white dots denote potential secondary
threats that are exploiting the mentioned threats. The last symbol denotes the agents that are
affected by the mentioned threats (Big Data Threat Landscape and Good Practice Guide,
2016).

9ENISA BIG DATA THREAT LANDSCAPE
Figure 2: Trends of Threat Probability
(Source: Big Data Threat Landscape and Good Practice Guide, 2016)
5. Improvements to the ETL Process
Newer technologies need to be implemented to the system that will help in the
improvement of the security of the same. Various governing institutes like the COBIT 5 or
ISO provide technical reports that suggest changes and improvements that need to be applied
in the infrastructure (McAfee, Brynjolfsson & Davenport, 2012). Big Data technology

10ENISA BIG DATA THREAT LANDSCAPE
involves the collection of large data sets from multiple sources and analyse the same. Due to
this reason, the security of the data needs to be ensured with utmost priority (Murdoch &
Detsky, 2013). The various threats that are discussed in the report can be solved by the
following measures.
Exposure of sensitive information due to human errors as well as the leakage of data
in the web applications can be solved by using encryption methods that not only will ensure
the access of authentic users only but also protect the data from other cyber attacks. Along
with the proposed method, the systems need to have an improved security infrastructure as
well (Provost & Fawcett, 2013).
In case of eavesdropping and intercepting risk, the use of cryptography along with
strong network security system and firewall will ensure the reduction in occurrence of such
attacks (Swan, 2013).
To minimise nefarious activities like identity fraud, user authentication protocols
needs to be implemented at different levels that will accurately judge a fraud activity and
differentiate a false user from a genuine user (John Walker, 2014). The DDoS attacks can be
prevented by using tools that can detect the anomalies in the network traffic using methods
like traffic monitoring, ingress filtering and rate limiting (Marz & Warren, 2015).
In addition to this, there are legal threats that involve the availability of a data to all
the departments in a business. This can create issues, which can be controlled by segregated
data based on its operational area (Wu et al., 2014). This provides for doing business as per
the particular area. Skill shortage can be resolved by providing proper training to the
concerned personnel related to information security.

11ENISA BIG DATA THREAT LANDSCAPE
6. Response of ENISA to its current state of IT security
The present state of IT security is not satisfying enough for ENISA. This is mainly
due to the presence of numerous gaps in the system. The primary gap that is identified within
the system is the presence of vulnerability in data security that can be easily exploited by the
cyber criminals. Big Data uses an enormous amount of both normal and highly sensitive data
for its operations (Fenz et al., 2014). Therefore, the security of data is highly essential. A
window within the security of such data can cause an exponential amount of damage to an
organisation, government and even a nation (Peltier, 2013). Sophisticated infrastructure
comprising of latest hardware and software is essential for ensuring that no vulnerabilities
exist within the software or tool that are responsible for protecting the data.
The next gap is the use of cryptography. Although it ensures a secure environment to
the stored data, however, it also serves a complex system environment that causes problems
for those who are not used to such complicated methods of data protection (Boyd &
Crawford, 2012). Loss of the encryption and decryption keys due to the careless activities of
any organisational personnel can cause disaster for the organisation, as the secured data no
more stays secure. On the other hand, the availability of the data becomes a problem for those
who are authenticated to use the data.
The final gap to the security is the lack of computing framework as the big data
vendors provide different versions of tools that are poorly secured, which offers with various
risks to the users (Ahmed & Matulevicius, 2014). These tools can be easily exploited by the
cyber criminals to obtain resources of their interest. In addition to the gaps mentioned above,
the most important gap that is the foundation of an insecure infrastructure is the lack of
awareness among the general mass. Lack of sufficient training and campaigns in projecting
the gravity of the cyber threats and their implications is helping maintain a casual approach to
one of the most threatening issues at present (Gantz & Reinsel, 2012). The continuity of

12ENISA BIG DATA THREAT LANDSCAPE
ignorance of common people to the necessity of using a stable and secure infrastructure is the
most active factor that fuels the rapid growth in cyber crime.
7. Conclusion
The report concludes with the insight that Big Data is a very important aspect to the
improvement of technology and data analysis. It has become the main element for the growth
of many industries. Data is the most essential resource for the operation of an institution, be it
an organisation, a governing body or a whole nation. Without the availability of proper data,
it is impossible to carry out any operation in any institution. The advent of Big Data has
revolutionised data mining and made decision making easier for the management of any
institute. However, with the rise in popularity of the concept, it has been subject to some
serious threats as well. The threats seek to gather and expose sensitive data to achieve
different goals that depends solely on the intention of the attacker. These cyber threats to Big
Data need to be minimised by employing certain measures in terms of innovative strategies as
well as technical advancement in the field of cyber security. In addition, a global awareness is
extremely necessary to resolve the rising threat to cyber incrimination as it is practically
evident from various instances that ignorance brings chaos.

13ENISA BIG DATA THREAT LANDSCAPE
8. References
Ahmed, N., & Matulevičius, R. (2014). Securing business processes using security risk-
oriented patterns. Computer Standards & Interfaces, 36(4), 723-733.
Big Data Threat Landscape and Good Practice Guide. (2016). Retrieved from http://Big
%20Data%20Threat%20Landscape.pdf
Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural,
technological, and scholarly phenomenon. Information, communication & society,
15(5), 662-679.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From
big data to big impact. MIS quarterly, 36(4).
Dumbill, E. (2013). Making sense of big data.
Fenz, S., Heurix, J., Neubauer, T., & Pechstein, F. (2014). Current challenges in information
security risk management. Information Management & Computer Security, 22(5),
410-430.
Gantz, J., & Reinsel, D. (2012). The digital universe in 2020: Big data, bigger digital
shadows, and biggest growth in the far east. IDC iView: IDC Analyze the future,
2007(2012), 1-16.
Ifinedo, P. (2012). Understanding information systems security policy compliance: An
integration of the theory of planned behavior and the protection motivation theory.
Computers & Security, 31(1), 83-95.
John Walker, S. (2014). Big data: A revolution that will transform how we live, work, and
think.

14ENISA BIG DATA THREAT LANDSCAPE
Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data.
Proceedings of the VLDB Endowment, 5(12), 2032-2033.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps
in big data analysis. Science, 343(6176), 1203-1205.
Lohr, S. (2012). The age of big data. New York Times, 11(2012).
Marx, V. (2013). Biology: The big challenges of big data. Nature, 498(7453), 255-260.
Marz, N., & Warren, J. (2015). Big Data: Principles and best practices of scalable realtime
data systems. Manning Publications Co..
McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: the management
revolution. Harvard business review, 90(10), 60-68.
Murdoch, T. B., & Detsky, A. S. (2013). The inevitable application of big data to health care.
Jama, 309(13), 1351-1352.
Peltier, T. R. (2013). Information security fundamentals. CRC Press.
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-
driven decision making. Big Data, 1(1), 51-59.
Swan, M. (2013). The quantified self: Fundamental disruption in big data science and
biological discovery. Big Data, 1(2), 85-99.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2014). Data mining with big data. IEEE
transactions on knowledge and data engineering, 26(1), 97-107.