Case Study of ENISA Big Data Threat Landscape 2016 : Assignment

Added on - 22 Jul 2020

  • 13

    pages

  • 3579

    words

  • 5

    views

  • 0

    downloads

Showing pages 1 to 4 of 13 pages
IT RiskManagement
TABLE OF CONTENTSIntroduction......................................................................................................................................11. Overview of the case study.....................................................................................................12. The most significant threat for big data..................................................................................43. Identification of key threat agents and what can be done to reduce its impact on the system54. Discussing the improvement for the ETL process.................................................................65. Summing up the case study....................................................................................................8REFERENCES...............................................................................................................................9
IntroductionBig Data is the accumulation of large data sets that cannot be processed by usingconventional methods. The large and complex data is collected by big data from wide sourcessuch as social sites like Facebook, twitter, e-commerce sites, telecom organization etc . Since theuse of big data has increased over years by organization so the systems of big data are becomingthreat targets by threat agents (ENISA Big Data security infrastructure, 2017). Appropriatepractices need to be adopted in order to reduce the impact of big data threats. The presentassignment will answer various questions based on case study of ENISA Big Data ThreatLandscape 2016.1. Overview of the case studyIn this case study, the European Union Agency for Network and Information Security(ENISA) which is a security expertise for EU, and its citizens as analysed the threats that arebased on with big data. Big data is generally used in organization for collecting and storing oflarge and complex data(Cohen, Krishnamoorthy and Wright, 2017). There are 3 Vs that lead tothe foundation of big data .They are as follows -:Volume which is associated with quantity of dataVariety refers to number of types of dataVelocity refers to the speed of data processing.The big data practitioner makes use of various methods to process all 3 V's. In the recent yearsbig data technology has gained huge name and success and is expected to play an important roleby affecting several prospects of social groups ranging from food security, health and smartcities. The potential impact of big data is acknowledged by the European Commission in a datadriven economy by outlining plans on big data (Waemustafa and Sukri, 2016).In order to facilitate scientific analysis of data and exploitation, big data project will belaunched and planned in various agencies of Europe and around the world. The technologies ofbig data are also used in various military applications such as fighting terrorism, collecting andevaluating intelligence from heterogeneous sources. In the recent years data intensiveenvironments have adopted a big data approach. For an instance Facebook, which is known forstoring the large and complex datasets worldwide(Brustbauer, 2016). In complex organizationsand communities the applications of big data can provide an efficiency and effectiveness in1
decision making. However on the other side, besides the benefits of big data there are number ofsecurity risks and threats that are faced by big data system.The threat agents are increasingly attacking the big data systems. The threats that have beenidentified in the big data can be related to the high replication in the big data storage which mayresult in leakage, breach and degradation threats. There is a significant privacy and dataprotection impact on big data as the extra information which is produced may have an impact ondata leak and failure (Rampi and Viswanathan, 2016). This case study makes use of threatlandscape and good practice guide in order to understand and evaluate the actual state of securityin the area of big data. There is no alignment of interest by asset owners in the area of big dataand this may also lead to conflict. So, this leads to the creation of complete ecosystem where thecountermeasure need to planned and executed. The overall risks in the area of big data can bereduced by applying basic privacy and security best practices. This case study recognizes bigdata assets,examines the influence of these assets to threats, lists agents of these threat ,takes intoconsideration weaknesses and risks and points to future practices and new researches in theorder to cope up with these threats in big data field. A gap analysis is provided in this case study,which will represent the identified threats of big data and countermeasures that have identified2
desklib-logo
You’re reading a preview
card-image

To View Complete Document

Become a Desklib Library Member.
Subscribe to our plans

Download This Document