Why Big Data Security Is Important – and How to Set It Up

Did you know that Netflix saves $1 billion annually thanks to big data? From that perspective, it’s not surprising that 97.2% of organizations are already investing in that field. By 2023, the big data analytics market is set to reach $103 billion. However, with this rapid growth also comes a rising threat – cybercrime. To be safe, it’s crucial to understand why big data security is important.

From the perspective of cybersecurity, big data is quite specific. Huge volumes of data usually have insufficient protection and are a great opportunity for criminals to ply their trade. To fight off these malicious activities, you need protection in form of adjusted big data security measures.

To help you set them up, in this article we’ll explain how to get them going, and whose responsibility they should be within an organization.

What is Big Data Cyber Security?

As TechVidvan puts it, you can define big data security as the tools and measures set up to guard both data and analytics processes. Its goal is to protect against malicious activities that could harm your data – like theft, ransomware or DDoS attacks that can crash servers. For organizations operating in the Cloud, the list of possible threats is longer. Luckily,  most public Clouds providers can easily deliver sufficient security tools protecting against these issues.

However, without proper security, the long-term effects of becoming a victim of malicious activities can be dire. Among others, they include serious financial repercussions (like losses or litigation costs). Additionally, they can also seriously hurt your operations and reputation.

Luckily, there are ways to avoid these grim scenarios.

How Can We Protect Big Data?

Citing Datamation, the goal of big data security is keeping unauthorized users and intruders away. You can try to achieve it through strong user authentication, firewalls, end-user training, intrusion protection systems, or intrusion detection systems.

However, big data security is quite specific. Since big data projects usually have 3 stages, security best practices must stand guard at each of these steps. And, importantly, in the phases in-between.

The first stage is the acquisition of data.  When in transit, data is vulnerable to corruption or interception. The second stage is data storage. At this point, data can become a victim of theft or be held hostage (both in the Cloud or on-premise servers). Finally, the last stage is the consumption of data. Here, your information can serve as an access point for malicious intruders.

Luckily, there are several big data security techniques that you can implement to protect your information and operations. None of these security tools is per se new to big data. But thanks to their scalability and ability to protect different types of data at different points of the mentioned stages, they are very useful in providing safety.

Let’s look at a few of them.

Encryption

Encryption is one of the most common security techniques. To some extent, it’s relatively simple – but extremely powerful. Properly encrypted data is useless to hackers if they don’t have the key to unlock it.

This technique can secure both data in-transit and at-rest. If you’re sharing your data or exchange it with other consumers, HTTPS protocols encrypt your data automatically during transport. On the other hand, data stored or archived on specialized file systems like Hadoop Distributed File Systems (HDFS) can be encrypted through files or disc encryption tools.

Access Control

A basic (but most important) network security technique. Without proper access control, your data is open to unauthorized users. And, ironically, might be unavailable to those, who should have access to it.

Ideally, user control should be automated, auditable, and as narrow as possible. In this complex security system, roles and policies are the main weapons. Since those roles and policies tend to be numerous (in an ordinarily sized system, there can be from tens to hundreds of different ones), automation will make it possible to manage them easily. Finally, every change to your access control configuration should be kept in an auditable and easily reversible form.

Intrusion Detection and Prevention

The distributed architecture of big data provides a good opportunity for outside intruders. But with an IPS – Intrusion Prevention System (which can be described as a more sophisticated firewall), network traffic can be monitored, which helps to protect the platform from vulnerability exploits. Moreover, intrusion detection and prevention systems often work directly behind the firewall and allow to detect the intrusion quickly (and isolate, if needed, before it manages to do any actual damage).

Key Management

Key management aims to protect cryptographic keys from loss or misuse. For years now, key management is a security best practice. It is also immensely useful in big data environments, especially for those distributed all over the globe.

For key management, the best practices include policy-driven automation, on-demand key delivery, logging, or abstracting key management from key usage.

Physical Security

Finally, it would be nice to have some physical security. Don’t forget it if you have your own big data platform in your data centre. Additionally, if you’re relying on an outside provider, carefully examine your potential Cloud provider’s data centre security.

Obviously, physical security systems deny strangers access to your data centre. But importantly, they also prevent your staff members from accessing it. After all, not every employee should have access. Video surveillance and security logs are also a good idea.

Big Data Security Challenges

Statistics show that the biggest cybersecurity risk faced by US companies is employee negligence. CNBC reports that half of all data breaches are caused by basic human error (like losing a document or device). However, some of the employee-caused data leaks are not simple mistakes. In fact, over 70% of departing employees admit to stealing some company data.

To avoid these issues, experts recommend setting up clear workplace policies. Moreover, it would also be good to implement precise data access regulations, so that only essential employees have access to key information.

Apart from human-caused issues, another key challenge is presented by fake information. Fake data is a huge big data security problem mainly because it limits your possibility to identify other issues. What’s more, it can also add unnecessary workloads, taking up the time you could spend on more pressing activities.

Big Data Security Management

Finally, we get to the question of who within an organization should be responsible for handling big data security. The answer is peculiar: almost everyone.

IT and InfoSec handle policies, procedures, and security software for protection. Compliance officers must cooperate closely with the IT team to protect compliance – like, for example, automatically stripping credit card numbers from results sent to a quality control team. DBA’s need to operate closely with IT to safeguard their databases.

And importantly, any other employee will generally also be responsible for company data. The reason is simple – big data platforms are also vulnerable to malware. Some modern phishing attacks are extremely creative, that’s why users should be aware of the threat.

Big Data Security – Conclusion

The use cases of big data are bound to rise in the following years. With their growth, cybersecurity threats will also expand. That’s why it’s important to treat big data security seriously from day one. Developing an effective big data protection system now will also bear fruits if you decide to expand later on.

Good luck! And if you’d like to hear more about big data security, visit our dedicated webpage for more information.