CSIA 459 Evaluating Emerging Technologies

Big data analytics is the utilization of improved analytic systems against enormous, varied data sets which constitute various types for instance unstructured/ structured and batch/streaming and diverse sizes from terabytes to zettabytes. Big data is used to describe data sets whose type or size is above the ancient relational databases’ ability to manage, process and capture low-latency data. With big data analytics business and consumers are able to examine large data amounts in order to discover correlations, hidden patterns and other insights. This technology makes it possible to scrutinize data and get answers immediately from it. Big data analytics facilitates firms’ in making of more-informed decisions through the use of disclosed unknown correlations, customer preferences, hidden patterns, market trends and other valuable information. In this technology selection paper, discussion will be on the basis of the evolving use of big data analytics technology and recommending it as one of the security-based Internal Research and Development projects (Ambur, & Langley Research Center, 2016). The recommendation will be through the evaluation of the security implication of big data analytics using the Five Pillars of Information Security.

The big data analytics is multifaceted and more emergent and the IT generalist have a hard time understanding it. The big data analytics development is determined by the web historically, the technology is developing rapidly as well as its application that is taking place in significant vertical industry sectors as well as representing a growing opportunity to vendors a chance worthy of all the hype. Big data analytics is a highly diverse area that is defined by the capacity to congregate information from numerous data sources which are either unstructured or structured (Kitchin, 2014). The analytics technology is one that leverages both the computing and storage capacity of a bunch of computers, it has advanced performance as well as the capacity to handle large data streaming in. Big data analytics utilizes the massively parallel processing technology in order to handle the structured data sources as well as accelerate the processing of structured data after which the structured data is used in minimizing searches to answer a query.

The analytical tools are designed to enable the user to have a high level information discovery which empowering analysts to engage in data conversation enabling them to develop a deeper and sophisticated insight. The process of compiling a huge amount of data to answer questions or find correlations, the big data analytics technology has the ability to prompt and after answer more fundamental and deeper questions (Marr, 2015). The predictive analytics in the big data technology has the capacity to distinguish patterns in apparently random data especially in financial capital markets example it is being used in solving the rogue trading issue. The rogue trading markers are different and numerous hindering the ability to detect suspicious actions in good timing.

The big data analytics is used in supporting security analytics especially in cybersecurity. Emerging technologies for instance the big data analytics have the application of fraud detection through synching of security intelligence tools in real-time dedicated to extracting important information. The big data technologies are have a significant impact to organizations as it enhances development of scalable and affordable infrastructure to monitor security through cloud computing combination (Johnson, 2015). The big data security analytics are essential as they enhance analysis of collected and maintained big data. The complex-event processing, NoSQL databases and the Hadoop ecosystem are examples of big data analytics technologies used in analysis of huge, heterogeneous datasets using unanticipated velocity and scale to analyze security information. The BotCloud2 uses the MapReduce to process data appropriately for security analysis. The application is therefore essential to organizations in the extraction of useful data that concerns detection of threat attacks or tracking of particular behaviors.

The big data analytics like other technologies has its risks and vulnerabilities that IT specialties are dedicated in correcting over time. The vulnerabilities are characterized by logical based, architectural, physical and administrative loopholes. The enterprise-wide big data constitute of multifaceted structural challenges that pose vast risks to many organizations (Atzmueller, Oussena, & Roth-Berghofer, 2016). For example, most developing big data analytics are used along with new software components which have few utilities designed to protect application interfaces and features. The big data applications are developed on service models that are web-based and are vulnerable to big data information and assets attacks. The communication technology infrastructure and information can be easily infiltrated by criminals through the physical security mechanisms and facilitating access to big data assets. Poor configuration and deployment, failure of business process and inappropriate design development enhances illegal access of big data. Logical-based attacks are as a result of vulnerabilities that emerge due to wrong deployment and configuration (Holzinger, 2014). A big data breach is another risk that occurs when employees expose big data assets and information to external agents an act of disregarding the company’s code of conduct.

New development in terrorist attacks all over the world has led to the use of big data in the United States to detect terrorist attacks. The capability of the big data analytics to mining crunch and billion bits of information to discover crucial clues is the reason for the push by the U.S law enforcement and intelligence agencies to couple the big data to forecast social upheaval and terrorist acts before they occur (Strohm, 2016). As more individual data is collected by very powerful computers, it avails huge sets of data to not only genuine uses but also illegal use and abuses. Big data attacks is a threat that is discouraging consumers from exposing their private and companies such as insurances will be disadvantaged therefore they have to ensure that they are transparent to enhance trust from their consumers. Transparency includes exposing the people that access data, the manner data is stored, the manner data is accessed and the give detailed explanations on the security controls organizations are using to prevent data. Organizations have to have continuous information leveraging and improving the intelligence quality on probable attacks and cyberspace attackers.

When forming an information security program it is essential to have a solid foundation. To assess the big data analytics’ adaptability to information security key risk management pillars will be used: protection, reaction, documentation, prevention and detection. Protection is crucial in minimizing information security vulnerability. The big data analytics protection pillar is well designed as the application is aimed at protecting the big data’s communication technology and information from infiltration, the manner that they will protect it and the value of doing it as stated above. The prediction analytics in big data application are a detection pillar as they predict and forecast attacks of the big data systems in company’s example it is being used in detecting the rogue trading information (Simon, 2014). The big data analytics is exposed to various attacks especially cybersecurity attacks and the technology is designed in a manner that it can respond to this attacks. It utilizes the PDR paradigm that is Prevent-Detect-Respond which is a successful attempt by the analytics technology to react to attacks. Documentation is key in big data analytics as the data collected has to be documented and be used to create models that are used in interpreting desired outcomes. Prevention pillar in big data analytics is incorporated in the PDR paradigm and is an important procedure.

In a nutshell, the big data analytics is the appropriate technology to recommend for the security-focused Internal Research & Development project. The reason for recommending it is because it is an emerging technology with a wide range of benefits and high information security as proven in the Five Pillars of Information Security assessment.

References

Ambur, M. Y., & Langley Research Center,. (2016). Big data analytics and machine intelligence capability development at NASA Langley Research Center: strategy, roadmap, and progress.

Atzmueller, M., Oussena, S., & Roth-Berghofer, T. R. (2016). Enterprise big data engineering, analytics, and management.

Holzinger, A. (2014). Biomedical informatics: Discovering knowledge in big data.

Johnson, R. (2015). Security policies and implementation issues, second edition. Burlington, MA: Jones & Bartlett Learning.

Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures & their consequences.

Marr, B. (2015). Big data: Using SMART big data, analytics and metrics to make better decisions and improve performance.

Simon, P. (2014). The visual organization: Data visualization, big data, and the quest for better decisions.

Strohm, C. (2016). Using Big Data to Predict Terrorist Acts Amid Privacy Concerns. Insurance Journal. Retrieved from http://www.insurancejournal.com/news/national/2016/10/13/429217.htm