The drivers of success in the digital age of business are steadily shifting from bold and impulsive to analytical, data-driven decision-making.

Companies collect, process, and benefit from large quantities of data to achieve their goals. Yet, they also have a responsibility to keep such data safe.

This article delves into the idiosyncrasies of big data Security and the need for vigilance in protecting it. Continue reading to gain a better understanding of what challenges securing big data entails and how to overcome them.

Why Is Big Data Security Unique?

Companies leverage big data to refine business strategies, predict trends, and operate more efficiently. Big data has also become indispensable in customer profiling and associated attempts to create increasingly individualized marketing campaigns and customer journeys.

Such data comes from a broad array of sources. It varies in terms of format, structure, and source. Users directly contribute part of it, while tools like CRMs generate much data automatically.

Companies that handle big data face the problem of securing it at three stages. There’s the ingress stage at which all this disparate data comes together. The data is then at rest, occupying a company’s on-premise storage or residing in the cloud. The final stage involves analyzing and structuring output data that leads to actionable insights.

The dangers of unsecured big data

Data in all three stages is valuable and susceptible to various cyberattacks. Merely collecting big data means a company possesses terabytes of personally identifiable or financial information. The average cost of a data breach targeting such information exceeds $4 million and will only rise.

A PII breach is already bad enough. Still, hackers who manage to steal the fruits of big data analytics, like competitor intelligence, customer sentiment analyses, or the outlines of current and future business strategies, can cause damage many won’t be able to recover from.

What Are the Most Pressing Big Data Security Challenges?

Addressing big data security challenges comes down to controlling the data’s veracity, integrity, and accessibility.

Here are some of the specific threats companies that use and store such data have to mitigate:

Fake data and data poisoning

Untrustworthy and inaccurate data can have a profound negative impact on big data’s predictive and analytical capabilities. It affects the decision-making process, makes it harder to detect fraud, and can lead to misinformed actions customers won’t appreciate.

Fake data can result from poor data gathering system design or incompetent third parties. However, it might also be the product of malicious intent.

Data poisoning is a practice that targets the datasets used in training machine learning algorithms. It involves intentionally introducing false or misleading information into ML’s training data, altering the outputs to become inaccurate, or even favoring outcomes the attacker prefers.

Storage concerns

Companies increasingly use cloud storage to offset the costs and logistics of on-site server management. However, storing big data responsibly requires the best protection and granular controls that cloud storage may not offer.

The best solution is to relegate general storage to the cloud while keeping the most vulnerable data on tightly controlled company servers. That introduces the need for strong on-premise cybersecurity measures companies might not be aware of or have room for in their budgets.

Privacy

We already mentioned the potential extent of information on individuals companies collect and process. In doing so, they need to adhere to industry standards and consumer protection laws in an increasing number of countries. There’s also the matter of consent and obtaining such information ethically.

Vulnerable endpoints

Hijacking an endpoint could introduce false or alter existing data to suit the attacker’s purposes. Endpoints like IoT devices are particularly susceptible while exponentially enlarging a company’s attack surface.

Access control

Large organizations have trouble keeping track of all users and their access privileges, which means someone could slip through the cracks and gain unauthorized access.

Malicious insiders and human error

Working with big data is impossible without at least basic interaction for low-level employees. However, that means everyone has at least some access, which unscrupulous individuals may exploit. While bad actors are a legitimate threat, human error and a lack of cybersecurity understanding are responsible for far more incidents.

Which Security Tools Can Meet These Challenges?

Best practices for securing conventional networks are at the core of any big data protection strategy.

Whether a company is using only established tools or mixing in new ones isn’t as important as their scalability and ability to handle such data in all its states.

Encryption

Making big data undecipherable to hackers is any company’s first security responsibility. Encryption has to encompass all stored data as well as analytics tools and the output data they generate.

Encrypting data at rest is relatively straightforward but becomes trickier if remote employees or branch offices also need to interact with it securely.

Business VPNs protect and encrypt remote network access, allowing for undisrupted business operations independent of location or connection method. To ensure the integrity of these connections, performing a vpn leak test regularly is essential for identifying potential vulnerabilities.

Intrusion detection

The distributed nature of big data storage makes intrusion attempts easier. Luckily, intrusion detection systems can identify, log, and quarantine such attempts before they can do damage at scale.

Access control policies

Access control minimizes users’ potential impact on big data. Network segmentation, user hierarchies, and zero-trust policies ensure everyone gets adequate access without leaving room for individuals to overstep their authority.

Insider threat detection and behavior analytics

Logging suspicious activities like irregular downloads or access logins during odd hours used to be the primary means of Insider Threat Detection. Behavior analytics introduces a more refined approach by establishing baseline user patterns and alerting admins if these start to deviate.

Conclusion

Analysis of big data offers unprecedented benefits for businesses with the foresight to recognize the potential of the resulting insights.

The unique nature of such data comes with a new set of security challenges companies will need to factor into their budgets and cybersecurity strategies to reap the benefits without compromising the data. Hopefully, our list will help you take the first steps towards better Big Data security.

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