Big Data: Technologies Capable of Addressing Security Issues
With Big Data, All businesses should take the security issues threatening big data seriously and take appropriate measures and precautions to ensure that no data breaches happen, some of which could be potentially disastrous.
Anything associated with big data, businesses have an opportunity to take advantage of one of the most valuable and rewarding enablers of their everyday success in the marketplace. Large volumes of different types of data enable them to have a better insight into their customers, something that is as lucrative as pure gold to their daily success. Data analysts estimate that up to 2.5 quintillion bytes of raw data are being generated every single day in the world, with about 90% of the data in existence right now having been generated in the past two years alone.
When businesses and corporations have such levels of data about their customers, they are at a better position to get ahead of their competition by offering consumers the kind of highly customized services and products that most of them want. If you look at this from a marketing point of view, this scenario is beneficial to both the businesses and the consumers. While businesses enjoy a rise in their income and increased customer loyalty, consumers get a chance to have services and products customized to their liking and of better quality.
The problem is that big data can also be very valuable to cybercriminals. A high profile hack into a large corporation’s data depository, which will often be having enormous amounts of data, can be very lucrative for cybercriminals while at the same time being a major setback for the affected organization. For instance, the data breach that affected Target in late 2013 was reported to have cost the company over US$1.1 billion, while 2011’s PlayStation breach set Sony back by over $171 million.
Due to the difference in the level of security threats associated with big data compared to those affecting traditional data, even their protection varies. It is therefore important that corporations understand these differences early enough so that they are able to handle the security issues successfully.
Why big data security threats need to be approached differently
Due to the fact that the nature of traditional data is different from that of big data, and since most hackers and other cybercriminals today are going for big data, protecting traditional data is much easier than protecting big data. Since big data is also much more complex and enormous in volume, managing its security needs a multifaceted approach that also has the ability to evolve constantly. Managing big data security is a science and art that is still at its budding stage.
The following are some of the reasons why management of big data security is different:
Many sources of data
Typically, the big data found in an organization’s repository is sourced from a variety of places, with each of the multiple data sources having its own security constraints and access limitations. When this data is brought together in one big data depository, an organization will struggle to come up with a consistent security policy that balances all the requirements of the various sources of data involved. All this data also has to be aggregated together and its meaning extracted. With multiple sources, attackers also have a wider target to direct their attacks.
Most big data environments will naturally be distributed, making them much more complex. Relative to single high-end database servers, this also raises their vulnerability to cyber-attacks. Having a consistent configuration and security policy across the geographical distribution of big data is much more difficult than most people think. The mere involvement of many servers makes it even harder for a consistent configuration across all of them to be achieved, increasing the vulnerability of the entire system.
Big data technologies are not secure
Programmers who designed some of the most crucial tools used in big data programming, including NoSQL and Hadoop databases, did not take enough consideration into the security of big data. For instance, NoSQL databases do not provide their users with role-based access control like traditional databases do, thus easing the process of unauthorized access to their data. Both tools also make the process of defining security policies much harder. Big data security strategies must therefore keep evolving with both the nature and intensity of security threats.
Application software security
We have already highlighted the fact that the designers of big data programming tools overlooked so many security issues when they created them. It is for this reason that choosing secure versions is recommended whenever you are using open-source software. Examples of such include Apache Accumulo or Hadoop’s 20.20x version. It is also possible to implement security measures at the application layer using technologies such as Cloudera Sentry or DataStax Enterprise. In addition, both Sentry and Accumulo provide extra features to enable role-based access control for NoSQL databases.
Tracking and monitoring big data accounts
Any serious organization that is worth its salt must implement a robust account policy for its big data. This includes, but is not limited to: Requiring that all users of its systems set very strong passwords and renew them often Deactivating all inactive user accounts after a specified period of time Setting a limit for the number of attempts that a user should try accessing an account.
Another important thing that corporations should note is that cyber-attacks can also be perpetrated from within their organizations.
Securing both software and hardware configurations
An organization’s big data architecture must always have secure images for all its computer systems servers. Any necessary patches to those servers should also be applied uniformly and consistently across all of them. The number of individuals possessing administrative privileges within any enterprise should also be very restricted. Among the most efficient automation frameworks to use in the implementation of system configuration automation and make sure that all the big data servers in an organization are uniformly secure is a tool known as Puppet.
Monitoring, understanding and analyzing audit logs
For any enterprise, understanding and monitoring its big data clusters is a crucial existential necessity. This is achieved by implementing audit logging technologies within the organization. The analysis of big data clusters and a careful examination and understanding of logs is something that must be done consistently.
In order to safeguard its data, an organization requires an all-around safety strategy. Identify all sensitive data that may need encryption and/or integrity controls and use the appropriate encryption tools to secure it. Constantly review the security policies and practices used by your cloud provider. A deployment of automated tools to all your organization’s network perimeters is also important to make it easy for monitoring confidential information like keywords. Such tools will enable you to detect any unauthorized attempts at data access. Finally, periodically run automated scans on all your data servers to ensure that all information remains in clear text.
Quick and appropriate response to incidents
No human system can remain breach-proof forever. Therefore, you need to implement an incident response policy that is appropriately documented and easily accessible to the relevant parties. This policy should have a clear definition of all the possible incidents and their level of seriousness, as well as specify the personnel to deal with each case. A review of the policy should be done periodically and any necessary updates implemented.
The security of big data should be a serious consideration for any business or corporation, with proper measures being put in place to ensure that any potentially calamitous data breach does not happen. While big data could be a big lucrative opportunity for businesses, the security threats involved could also pose a major challenge for corporations, something that should be handled with care using the most appropriate policies and tools. With all these measures in place to help safeguard both your big data and the applications involved, you can afford to have some peace of mind and enjoy the fruits of your labor.
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