What is Big Data Security?
Big data security is the collective term for all the measures and tools used to guard both the data and analytics processes from attacks, theft, or other malicious activities that could harm or negatively affect them. Much like other forms of cyber-security, the big data variant is concerned with attacks that originate either from the online or offline spheres.
For companies that operate on the cloud, big data security challenges are multi-faceted.
These threats include the theft of information stored online, ransomware, or DDoS attacks that could crash a server. The issue can be even worse when companies store information that is sensitive or confidential, such as customer information, credit card numbers, or even simply contact details. Additionally, attacks on an organization’s big data storage could cause serious financial repercussions such as losses, litigation costs, and fines or sanctions.
See it in action:
The first challenge is incoming data, which could be intercepted or corrupted in transit. The second is data in storage, which can be stolen or held hostage while resting on cloud or on-premise servers. The last is data that is being outputted, which seems unimportant but could provide an access point for hackers or other malicious parties.
These three concerns should play a central role in creating a flexible end-to-end big data security philosophy for any organization.
How Can You Implement Big Data Security?
There are several ways organizations can implement security measures to protect their big data analytics tools. One of the most common security tools is encryption, a relatively simple tool that can go a long way. Encrypted data is useless to external actors such as hackers if they don’t have the key to unlock it. Moreover, encrypting data means that both at input and output, information is completely protected.
Building a strong firewall is another useful big data security tool. Firewalls are effective at filtering traffic that both enters and leaves servers. Organizations can prevent attacks before they happen by creating strong filters that avoid any third parties or unknown data sources.
Finally, controlling who has root access to BI tools and analytics platforms is another key to protecting your data. By developing a tiered access system, you can reduce the opportunities for an attack.