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Case study on Risk Management of Big Data
15 Pages3274 Words170 Views
Added on 2019-11-26
Case study on Risk Management of Big Data
Added on 2019-11-26
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2017IT risk managementIT risk management
IT risk managementContentsOverview of the case study:.......................................................................................................2Introduction:...............................................................................................................................2ENISA Big data security infrastructure:....................................................................................3Threats associated with the ENISA Big data:............................................................................6Significance:...............................................................................................................................8Key threat agents:.......................................................................................................................9Steps to minimize their impact on the system:...........................................................................9Trends in the threat probability:...............................................................................................11Improvement in the ETL process:............................................................................................12Evaluation of the current state of IT security on ENISA:........................................................13Recommendation:....................................................................................................................13Conclusion:..............................................................................................................................14References:...............................................................................................................................141
IT risk managementOverview of the case study:The European Union agency for network and information security (ENISA) is the centralised network authority which is perform the function of identifying threats and provides mitigation techniques associated with the information security. This research study focuses on the threats associated with the Big Data assets. Introduction:The threats associated with the Big data are far beyond the threat associated with the ordinarydata. The high level replication strategy should be built for deploying the storage of big data. The outsourcing of big data results into the introduction of new types of breaches and degradation and leakages of the threats associated with the specification of big data. “The significant impact can be seen on the privacy and data protection methods used in storing the big data” (Singh, 2015). The links should be created for specifying the key requirement to impose parallelization for improving the process of data collection. The big data analytics performance can be improved by adding the additional information on the data leakages and increasing rate of breaches. There are different assets owners which are associated with the big data are categorised as data owners, computation providers, data transformers, and storage service providers. The activities and conflicts are aligned in the big data management processes. The complex ecosystem can be created for involving the security measures in the planning and execution phases associated with big data management processes. The overall privacy and security is declining in the management of the big data with the increasing demand of big data on the request of the user. The emerging paradigm should be constructed by making use of security principles to minimize the risks of security and privacy associated with the storage of big data. The gap between the identification of the threats and adopting the mitigation policies can b filled with the construction of big data security infrastructure. From the research, it has been identified that there is a lack of technology which can be used for providing security to the big data environment. The management of the big data involves focus on the identification of the threats, traditional approaches used for handling big data, defining the solutions which are specific to the deployment of big data, planning of the activities, security procedures for big data environment, identification of the big data assets, and mitigation procedures. The ENISA aligns the data protection methods which are convenient for securing the Big data. The critical infrastructure should be developed for 2
IT risk managementaligning the activities. The tools and technologies should be used to develop the mitigation plan for securing the big data and to cope up with the threats associated with the handling andstorage program used for big data. “The potential impact can be seen with the deployment of the security measures in the curriculum of the activities” (Wang, 2014). ENISA Big data security infrastructure:The strategies should be developed for carrying out the process of threat analysis on cyber security. “The emerging risks should be identified by collecting relevant information associated with the development of big data security infrastructure” (Jaseena, 2013). The highlevel conceptual model should be for providing security requirement in the management of big data. “The infrastructure of the big data involves the interrelationship between computational power, analysis, storage, and analytics (Terzi, 2015). The consideration shouldbe given on the security massive data collected on the internet to provide digital information, privacy issues, and data protection methods. The three dimension model can be created by focusing on the 6V’s associated with the deployment of big data over the network which is described below:Volume: The significant amount of data volume should be collected Velocity: The velocity refers to sending and retrieval of data on the demand of the user. The speed should be considered for managing the flow of data packets. Variety: The variety of data types and associated sources should be maintained for storing the big data on the internet platform. Veracity: The authenticity of the data helps in analysing and improving the quality of data. Variability: The variability is the term used for managing proper scheduling between the inconsistencies in the arrival of data. The process can be used for handling the big data effectively. Value: The value should be associated for collecting the potential revenues from the big data. The security infrastructure of the big data constitutes of five layer which are categorised as data source layer, integration process layer, data storage layer, use of analytical and computing model layer, and lastly, the presentation layer. The following table shows the layered infrastructure of the big data management. 3
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