Organisations are collecting more information than ever — much of it now coming from an exploding range and number of devices connected via the Internet of Things
There’s no time to waste when it comes to rethinking security for big data environments. The number and breadth of data breaches continues to grow unabated, with a 40% increase in data breaches in 2016 reported by the Identity Theft Resource Centre
The IDC predicts continued double-digit growth for big data and business analytics through 2020 which will be used to handle this ever-growing amount of data.
However, some of the big data that is stored and processed will undoubtedly be sensitive and will therefore need to be protected. So there are things that need to be considered such as; are the environment and the data vulnerable to cyber threats and who has access to the data? And there’s also the issue of compliance.
Big data deployments are subject to the same compliance mandates (e.g., GDPR, HIPAA, PCI, and SOX) and require the same protection against breaches as traditional databases and their associated applications and infrastructure.
All the best practices for data security are still applicable for big data environments. The problem is how to achieve security and compliance for big data environments given the unique challenges they present.
Much of the challenge of security big data is the nature of the data itself. Consider the impact on security of the well-known three V’s of big data:
Volume
Enormous volumes of data require security solutions built to handle them. This means incredibly scalable solutions that are, at a minimum, an order of magnitude beyond that for traditional data environments.
Velocity
Your security solutions must be able to keep up with big data speeds. You’ll need to focus on data parsing and collection throughput, the degree of automation that is available, and the ability to deliver real-time visibility of policy violations and other events.
Variety
Mixing multiple sources and types of data with different access permissions compounds classification and policy-setting challenges, elevating the need for robust audit capabilities.
It’s not necessarily the associated infrastructure and technology within big data environments that make it more challenging to secure, it’s the multiplicity that dramatically increases complexity.
