Companies are increasingly using big data technologies like Hadoop to store and analyze large amounts of customer data to gain insights. This raises security issues as more data is collected and needs to be properly classified and owned. Big data is also being used for fraud detection and security event management to replace traditional SIEM systems that are difficult for IT departments to manage. While big data can process structured and unstructured data at large scales, specialized skills are required like expertise in Hadoop, data mining, and analyzing various data types.

1. How To Tackle Big Data From A
Security Point Of View

2. Securing Your Big Data

3. A growing number of companies are using the technology to
store and analyze petabytes of data including web logs, click
stream data and social media content to gain better insights
about their customers and their business.
As a result, information classification becomes even more
critical; and information ownership must be addressed to
facilitate any reasonable classification.

4. Deploying Big Data for Security

5. The deployment of Big Data for fraud detection, and in place
of security incident and event management (SIEM) systems, is
attractive to many organisations. The overheads of managing
the output of traditional SIEM and logging systems are
proving too much for most IT departments and Big Data is
seen as a potential saviour. There are commercial
replacements available for existing log management systems,
or the technology can be deployed to provide a single data
store for security event management and enrichment.

6. Big Data Technologies and Risks

7. If you research the term Big Data, you will invariably
encounter Hadoop. Traditional data warehouses and relational
databases process structured data and can store massive
amounts of data, but the requirement for structure restricts the
type of data that can be processed. Hadoop is designed to
process large amounts of data, regardless of its structure.
The core of Hadoop is the MapReduce framework, which was
created at Google in response to the problem of creating web
search indexes. MapReduce distributes a computation over
multiple nodes, thus solving the problem of data that is too
large to fit onto a single machine.

8. Specialist Skills

9. In reality, Big Data is more about the processing techniques
and outputs than the size of the data set itself, so specific skills
are required to use Big Data effectively. There is a general
shortage of specialist skills for Big Data analysis, in particular
when it comes to using some of the less mature technologies.
The growing use of Hadoop and related technologies is
driving demand for staff with very specific skills. People with
backgrounds in multivariate statistical analysis, data mining,
predictive modelling, natural language processing, content
analysis, text analysis and social network analysis are all in
demand. These analysts and scientists work with structured
and unstructured data to deliver new insights and intelligence
to the business.

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Source:
http://www.computerweekly.com/feature/How-to-tackle-big-data-from-a-
security-point-of-view