Choosing the right data security solution for big data environments
Data is money. For some organisations, data has become the highest commodity, and this means consumers now hold the power. By gathering these large data sets, businesses can analyse human behaviour and interactions through trends, market patterns and associations which will fundamentally lead them to make business decisions to tailor experiences for consumers. Big data is big business and enterprises of all sizes are investing in data science and analytical platforms. Whenever data is mentioned, security should automatically follow; especially when you consider big data is everywhere – on-premise, in the cloud, streaming from sensors and devices, and moving further across the internet.
Yet, the security aspect of protecting these data sets is often overlooked. In the last five years, the surge of data breaches has seemingly run parallel with the amount of data organisations are now demanding. Yahoo, Facebook, Dropbox, Equifax, Twitter and Google are just some of the high-profile companies that are well known to collect big data, but also share the unwanted tag of experiencing a data breach. With much of the collected data by companies classified as sensitive personal information, cybercriminals are determined now more than ever to get their hands on it for malicious use.
Carrying the baton for protection of personal information in this area is data-centric security. Data-centric security attempts to focus on the data itself – altering or disguising it as a means of protection from misuse and prying eyes – rather than traditional routes which may focus on the IT infrastructure or security of systems. Yet, choosing the right solution from an increasing array of data-centric options can prove tricky. Vendors are quick to state they provide a solution that is data-centric, but often such solutions fail to also meet the stringent demands of being able to use it in a big data analytics environment.
The ideal data-centric solution requires a number of crucial aspects to meet the needs of tomorrow’s analytical workloads. To protect big data effectively and adequately, solutions need to incorporate the following
Naturally, big data environments will be in constant use and so housing security that can keep pace is a must.
Therefore, the data-centric solution needs to have the ability to scale any workload regardless of whether it’s in real-time or for historical use cases and without any visible impact or hinderance to performance.
With the involvement of artificial intelligence and machine learning in many of today’s software programs, businesses are looking to take advantage and incorporate this technology within their systems.
