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Case Study on System Risk Management at ENISA

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Added on  2019-11-26

Case Study on System Risk Management at ENISA

   Added on 2019-11-26

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Running head: SYSTEM RISK MANAGEMENTSystem Risk ManagementName of the StudentStudent IDName of the UniversityAuthor’s note:
Case Study on System Risk Management at ENISA_1
1SYSTEM RISK MANAGEMENTTable of ContentsIntroduction......................................................................................................................................21. Overview of ENISA Case Study and Illustration of ENISA Big Data Security Infrastructure.22. Top Threats in ENISA.................................................................................................................43. Key Threat Agents and Ways to Minimize their Impact.............................................................84. ETL Process Improvement........................................................................................................105. IT Security of ENISA...............................................................................................................10Conclusion.....................................................................................................................................11References......................................................................................................................................12
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2SYSTEM RISK MANAGEMENTIntroductionBig data consists of large amount of data for the purpose of analyzing the data set anddetermine patterns. Data assets are identified followed by a process of exposure analysis. Risksand vulnerabilities are shown in this case study (Mahajan, Gaba & Chauhan, 2016). The assetclasses of big data are identified and then level of risk exposure of the assets is assessed.This report talks about the different threats as well as the key threat agents (Kao et al.2014). The methods that can be used to minimize the impact of the threats are discussed in thisreport. It also explains how ETL process can be improved. 1. Overview of ENISA Case Study and Illustration of ENISA Big DataSecurity InfrastructureBig data consists of large amount of data for the purpose of analyzing the data set anddetermine patterns. Human behavior and preferences can be identified by analyzing big data. Theusage of big data is gaining importance with time. This case study provides information aboutthe security threats. Attackers are mostly targeting big data systems. Data assets are identifiedfollowed by a process of exposure analysis. Risks and vulnerabilities are shown n this case study.The asset classes of big data are identified and then level of risk exposure of the assets isassessed (Enisa.europa.eu, 2017). The security threats as well as their agents are also classified inthis case study. The threats of the big data are all the ordinary data threats but are not limited tothese threats (Patil & Seshadri, 2014). There are also new kinds of breach like degradation andleakage of data that are specific in case of Big Data. There is significant impact of the dataprotection as well as privacy. There can be conflict among the several asset owners because their
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3SYSTEM RISK MANAGEMENTchoices might not be aligned with everyone. The use of information and communicationtechnology will lead to several privacy and security threat issues. There is a presentation of gapanalysis that compares between the threats of the big data along with the countermeasures thatcan be taken in order to overcome and avoid these threats. This case study shows that there isgap in the countermeasure of big data (Vatsalan et al., 2017). The trend of the recentcountermeasures is explained. Data threats that are traditional in nature are mainly data oriented.Recommendations and suggestions are given for the countermeasures that can be taken in thenext generation. Current system and data should be replaced by big data so that there are specificsolutions of it. The loopholes in the existing system must be checked and resolved. This casestudy talks about the environment of big data, its architecture, assets of the big data and theirtaxonomy. It also describes the threats and its agents. Good practices are also given along withgap analysis.
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