Significant Benefits Organizations Business-Myassignmenthelp.Com

Question: Discuss About The Significant Benefits Organizations Business? Answer: Introductions The case study discusses the threats and the security issues associated with the process of data mining and use of big data. Big data refers to the storage of large amount of Data that can be mined for significant benefits in organizations and business (Wu et al., 2014). These data can be a very useful for business related decision marketing and therefore holds utmost importance. Thus, there are certain risks associated with the mining of big data. The case study discusses the different threats associated with ENISA and the various processes that can be implemented in elimination of the threats. The different threats agents and the threat mitigation processes are discussed in the following paragraphs (Inukollu, Arsi Ravuri, 2014)- Overview of the case study and illustration of the big data security Infrastructure The case study focuses on the big data landscape and the ENISA big data threat. The content of the case study puts a light to the different use of big data and the associated risk with the process of data mining (Wright De Hert, 2012). The key threat agents associated with the data breaches due the use of big data is clearly depicted in the case study. It further elaborates the impact of the big data, which is huge in thriving of the data driven economy. The use of big data is wide in fields such as military applications, fighting terrorism and research work (Gonzalez et al., 2012). Therefore, protecting the business process of data mining and ensuring a secure data mining is essential. The case study discusses the process of ETL ( ENISA threat landscape) and suggests different risk management and mitigation processes for mitigating the risks associated with the big data landscape ( ENISA 2017). The case study aims at giving providing a clear picture of the risks and threats associ ated with the big data landscape. The risk is widespread due to the involvement of cloud storage in order to store the big data. The threat infrastructure diagram of ENISA is illustrated below- Top and most significant threats The storage and the access of huge amount of data is subjected to different types of risks. The top threats associated with ENISA are elaborated below ( ENISA, 2017)- 1) Malicious code/ software activity: One of the top threats associated with the big data is the use of malicious code and software in order to extract information unethically. These are doe by infusing different threat agents into the system, which includes, viruses, Trojan horses, trapdoors, backdoors, ransomware and so on. These threats are infused with the system with the help of certain malicious codes and software. The attacker installs these programs into the system and gains access to the entire system by running these codes (Theoharidou, Tsalis Gritzalis, 2013). The risk from these malicious activity is high since the malware can easily spread to different systems. The assets that are mainly targeted by this threat include database and computing infrastructure model. 2) Data leak due to unsecure API: Big data is based on cloud storage as it helps in easy access of the data. however, cloud storage is a very unsecure platform and the use of unsecure API further leads to significant data breaches and data loss. Different types of injection attacks can be launched making use of unsecure API and therefore this can be considered as a significant threat agent. The assets that are targeted by this threat includes software and computing models of the information system. 3) Denial of service attack: Denial of service attack freezes the system thus making the resources unavailable for the legitimate users. A severe denial of service attack may lead to the permanent unavailability of the resources. These attacks can however be controlled by implying effective measures (Tan et al., 2014). The assets that are mainly targeted by this attack includes networks and servers of the system. 4) Rogue Certificate usage: This is other threat agent associated with the illegal usage of data and data breach. Rogue or false certificate can be used unethically to gain access to the systems the attackers are unauthorized to access (Pearson, 2013). This may result in severe data loss, data leakage and data modification and misuse of data The assets that are mainly targeted by this threat include hardware , software and its associated data. 5) Improper designing of the security systems: This is another major reason of the data security issues associated with the big data. Improper designing of security system or using an out of date security may lead to severe data loss. An inadequate system may further lead to improper data update thus giving rise to data redundancy (Theoharidou et al., 2013). The assets that are targeted by this threat include data and applications. 6) Identity fraud: Accessing the data by unauthorized person, by impersonating someone one else can be termed as identity theft. This is a significant threat as it might result in loss of confidential data and information (Roberts, Indermaur Spiranovic, 2013). The assets that are mainly targeted by the threat identity fraud includes personal identifiable information , back end services and the servers associated with the system. Most significant threat identified Out of the top threats identified in the previous section of the report, the top threat is definitely the threat associated with the usage or infusion of malicious codes and programs into the system. This is because, with the help of this threat, the attacker may easily gain access to the system and manipulate the data stored in them (Chen Zhao, 2012). This risk associated with this type of threat is very high and therefore, this threat agent is the most significant threat agent in the big data landscape (Pavlyushchik, 2014). Threat Agents, Their Impact and threat probability The top threat agents associated with the big data landscape, identified from the case study are elaborated below- 1) Corporation: The corporation or the organization that uses big data for its business benefits is a major threat agent associated with the security concerns related to the big data. This is because it is easier for them to manipulate and misuse these data fro their business benefits and gain competitive advantage in the market. 2)Cyber criminals: This is one of the most significant threat agents associated with the data breach and data loss. The main objective of cyber criminals is financial benefit by making the use of the mined data and therefore the impact of the attack by these threat agents is very high. 3) Cyber Terrorist: Cyber terrorists are more dangerous than cyber criminals as the methods used in the launching the attacks are more sophisticated in case of cyber terrorists. These threat agents mainly target large organizations, as impact over these organization effects a large part of the society as well (Taylor, Fritsch Liederbach, 2014). 4) Employees: One of the major threat agents is employees of the organization. They can be termed as threat insiders as well. Employee posses a sound knowledge of the data and security system of a particular organization and therefore manipulation of data by the threat insider is easier and sometimes unrecognizable. 5) Nation States: Nation States is one of the most significant threat agent associated with the security issues of big data landscape. Nation states are the most sophisticated cyber criminals and have high-level skill and expertise. 6) Script Kiddies: This threat agents uses ready-made code and programs in order to launch an attack. Therefore, this type of attack and the threat agent is less dangerous and can be eliminated by implementing proper security measures. Minimizing the Impact of the Threat In order to minimize the threats associated with the big data, the recommended measures that can be taken are elaborated below- 1) Using an effective security system coupled with the cryptographic methods of encryption limits the use and access of the data thus preventing the data loss and data breaches (Stallings Tahiliani, 2014). 2) Access control can be implied to limit the access of data only to authorized person. These may reduce the data breach considerably (Brucker et al., 2012). Access control only enables a registered person to access and data. 3) Training the staffs and employees in order to build awareness among them can be an effective method of preventing any sorts of threats by threat insider. Probability Trend of the Threats The probability of the threat is high as the attacker is coming up with different methods of implementing an attack. In order to prevent these attacks, proper security measures are needed to be taken. The associated threats are increasing in number and therefore, it becomes essential to eliminate it as soon as possible. ETL process Improvement ETL or the ENISA Threat landscape investigates and reports about the threats associated with different organizations. The document or report by ETL mainly deals with the threats associated with the information and communication technology assets (ENISA, 2017). The major loophole in the process of ETL is that, I only focuses on the technology issue and not the issues cause by the threat agents. The process of ETL can be improved by including a detailed and a structured report of the all types of threats associated with the big data and their possible effects. ENISA threat landscape or ETL provides a structure and the overview of the threats associated with the merging trends. It is mainly based on the publicly available data and reports the identified threats, and threat agents with the threats prioritized according to the frequency of appearance. Now this process can be improved by prioritizing the threat according to the impact caused and not by the frequency of appearance as minor threats such as denial of service attack can appear a several number of times but can cause less damage than some other threats whose frequency of appearance is less (Cherdantseva et al,. 2016). Current State of IT security in ENISA ENISA is not satisfied with the current security state of the organization as the organization is still exposed to the several cyber threats. The security essentials are needed to be updated in order to ensure the security of the big data. The threat agents and the attackers are growing stronger day by day and therefore updating the security systems becomes essential (Von Solms Van Niekerk, 2013). A stronger security system and proper supervision of the system is essential. The report identifies and discusses the risk associated with the information system of ENSA, which proves that there are certain loopholes associated with the structure of the security essentials in the organization. This is a major reason of ENISA being unsatisfied with the current security state. Different security measures can be undertaken by ENISA in order to remove the risks associated with the security system, which includes, using a proper intrusion detection system in order to prevent the data loss and d ata manipulation. Furthermore, the process of ETL can be improved by prioritizing the risks according to their impact in order to detect and eliminate several threats associated with the system (Albakri et al., 2014). Conclusion Therefore, from the above discussion, it can be concluded that ENISA is exposed to number of threats and the report discusses the different threat agents responsible for data breaches, data loss and data manipulation. The report suggests the different procedures by which the risk associated with the process of mining the big data can be eliminated. The ENISA Threat landscape deals with identifying and reporting different threats associated with various organizations. Use of big data is very significant in todays world and therefore, ensuring various security measures for the same is essential as well. The report concludes with the current state of IT security in ENISA and recommends few ways to address the issues. References Albakri, S. H., Shanmugam, B., Samy, G. N., Idris, N. B., Ahmed, A. (2014). Security risk assessment framework for cloud computing environments. Security and Communication Networks, 7(11), 2114-2124. Big Data Threat Landscape ENISA. (2017). Accounting. Retrieved 6 September 2017, from https://www.enisa.europa.eu/publications/bigdata-threat-landscape Brucker, A. D., Hang, I., Lckemeyer, G., Ruparel, R. (2012, June). SecureBPMN: Modeling and enforcing access control requirements in business processes. In Proceedings of the 17th ACM symposium on Access Control Models and Technologies (pp. 123-126). ACM. Chen, D., Zhao, H. (2012, March). Data security and privacy protection issues in cloud computing. 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