Big data environments are now more common in companies; nearly every industry has its hand in the cookie jar. Because of this, companies are generating more data today than any other point in history. Vast silos of information are being structured and filled every day giving companies a competitive advantage to better tailor their products to their customers needs. But with this growth of big data and big data environments, we are also seeing growth in cybercrime as well. Security for big data has become a great concern in the last few years. Much of the data that is gleaned is sensitive; we know it, you know it, and cybercriminals absolutely know it and love it.
For companies that run big data environments, securing their data warehouses and modes of deployment is vital to not only their success but also to their customers, clients and business partners privacy. Knowing how to secure your big data isnt as difficult as it might sound.
Security strategies can be found through asking specific questions such as Who is running specific big data requests, What analytics requests are users running or Are users trying to download sensitive data or is the request part of a job requirement, i.e. a marketing query? Answering these questions can help companies pinpoint their weak points.
Simple security for big data can come in many forms, mainly with data management and controls. To whom you specifically give user access and controls to can help weed out potential threats within an organization. Be sure to stay on top of managing these accounts, too–deactivate inactive accounts (dont let past employees still have access to your data), impose a limit on the permitted number of failed log-in attempts, require strong passwords and change them routinely, and above all train your employees in basic security measures. These should be the first steps you take in securing your big data environments.
Other security strategies are also fairly simple. They can include sensitive data discovery and classification, real-time data activity monitoring and auditing, data protection such as encryption or masking, data loss prevention, and vulnerability and compliance management.
Real-time data activity monitoring and auditing is another important step that you can take to protect your data from internal breaches. Implement audit logging technologies within your big data clusters and assign security engineers to examine and monitor these files so that you are immediately aware of any analysis requests that are out of the ordinary. Be sure that maintaining, auditing and analyzing your logs are done consistently throughout your whole organization.
Data protection such as encryption and masking is another quick measure to take to protect data. And it can be as easy as requiring employees to use the encrypt and send function on email applications such as Microsoft Outlook.
Another key to remember for your commercial security system is specifically what data is collected, how it is used and stored and what technologies are being used to analyze. Knowing what datasets need higher security and what servers need a sharper eye can better help companies to balance and organize their data security. Along with the aforementioned security tactics, other security strategies can include application software security–always use secure versions of open-source software. Proprietary technologies like DataStax Enterprise or Cloudera Sentry offer enhanced security within their applications.
Another way to secure your big data environment is to secure configurations for hardware and software. Build your network servers on secure images for all systems in your big data architecture. Keep patching up to date on the machines and once again, limit controller and administrative access and privileges to a select few. Also make sure that the servers in your environment are uniform, you can do so with automation frameworks like Puppet.
As big datas uses and applications continue to grow, the options for security do with it. Getting ahead of game is key in keeping your data safe and unbreachable and organizations that are going to continue to use big data should begin with these first steps.
