With some of history’s most significant data breaches arising in the past years, big data security analytics has gained tremendous value. Moreover, with organizations gathering and handling a lot more sensitive information concerning workers, partners, and customers, it’s evident that big data and cybersecurity have become vital. 

Procedures to protect data have become more complex than ever before, and new means of processing such vast amounts of information have had to be created. So, when big data meets security analytics, it can be highly beneficial to prevent future cyber-attacks and analyze those assets more efficiently than previously feasible. Furthermore, since big data is formed by large volumes of both organized and unstructured information, businesses increasingly use it to find patterns and trends in behavior while detecting threats. 

If you have already embraced such vital procedures or are about to incorporate them into your organization, let’s dive into some of the challenges you may face and how to ensure you get it right. 

Big data security areas possible threats

Challenges of big data and cybersecurity 

Due to the ongoing rise of cybersecurity threats, guarding sensitive information while maintaining corporate development and performance has become increasingly challenging. In reality, businesses all around the world process numerous sensitive records repeatedly. Can you imagine what would happen if this information got into the wrong hands? The repercussions would be devastating. 

Now, traditional preventative security technologies used for data mining and cyberattack prevention are deficient for many organizations. As a result, cybersecurity professionals are increasingly relying on big data integration and analytics for cybersecurity in order to fight some of the most common hurdles. Below we’ll explain the four most common security risks companies face. 

1. Sensitive information 

User-privacy violations have been issues that even the most renowned companies have dealt with in the past years. Let’s take Apple location services as an example. Despite a statement being explicitly provided on Apple’s website about handling privacy on iPhones and being easily turned off in the settings, location services don’t actually stop monitoring user activities. Although Apple never shares the information about consumers’ movements to any third party, a new class action was filed against Apple for keeping it for future reference. Since big data and cybersecurity is stored not only in local storage but also in cloud storage platforms, incidents of user-privacy violation occur all the time in the age of big data. 

The same happens when an organization fails to protect its employees’ sensitive information. In this case, governments or industry regulating parties may impose harsh penalties on the employer for a lack of big data security analytics tools and best practices in place. 

2. Data storage 

As big data has gone from centralized systems to distributed or self-governed ones, it does not remain in one location. Furthermore, it flows from one system to another, increasing the quantity of assets being stored. In this case, untrusted distributed programming frameworks can cause leakages and provide inaccurate findings, ending in wrong aggregate results. 

3. Data processing 

Real-­time monitoring does not always work flawlessly, and when the volume and pace of data streams rise, the problem becomes more acute. Moreover, it requires large investments and storage to make faster and more reliable judgments. In addition, processing information sometimes includes the participation of outside contractors or business partners elevating risks. An untrustworthy partner misusing these databases and exposing consumers’ sensitive information can be irreparable. 

4. Data accessing 

Enterprises obtain data mining and analyze assets from various sources, including end­-point devices such as hardware and software applications within business networks. However, with extensive data chunks at business dimensions, validation and filtering become challenging. In the era of remote working, this issue has become relevant as employees can use their own computers or smartphones to work from home and connect them to the company’s network, sometimes without the company’s knowledge or consent. 

Although these common issues can come up as companies embrace big data security analytics, organizations confront another matter regarding personnel. When data analysts lack the necessary skills to respond effectively to any possible dangers that may emerge, it can be exceedingly hurtful to the company. However, as artificial intelligence (AI) and machine learning continue to be deployed and understanding of big data cyber security analytics improves, this should become less of a problem for organizations.

Big data security use cases and areas

Today’s threat environment 

Because of the expanding threat landscape, the number of sophisticated tools, and computing power at cybercriminals’ disposal software security firms are confronted with unprecedented problems as big data rapidly increases. 

The right blend of methods, human insight, professional awareness of the threat landscape, and rapid processing of extensive big data security analytics is required for successful protection. Every day, an ordinary end user visits hundreds of websites and uses an increasing number of operating systems and apps via various mobile and desktop devices. The result is an overwhelming and ever-increasing volume, velocity, and diversity of information created, exchanged, and spread. We’ll discuss how each point increases at an astounding pace and initiates a shift in how security vendors handle threats

  • Volume 

As access to technologies keeps on evolving, the danger landscape is also changing in several ways, including an increase in the sheer number of threats. Just imagine how in the 90s, the average user got one or two spam messages per day, then about 20 years later, the number of spam dispatched was over 200 billion every day. File transfers and web page access show similar increments meaning that cybercriminals have an extensive playground to exploit. 

  • Variety 

With each passing year, hackers use new tactics that are also more comprehensive. In addition, cybercriminals are becoming more adept at developing tools in real time. Today’s malware, for example, frequently passes through quality control procedures. However, cybercriminals test it on a variety of devices and operating systems to guarantee that it is undetectable. In reality, malware parts can be replicated in thousands of ways and are no longer limited to home computers. Mobile devices are also in danger due to multi-platform malware. 

  • Velocity 

The requirement to handle, store, and process this massive volume and variety of data daily poses an extreme velocity challenge to security providers. The internet’s volatility across time adds to the problem’s severity. Unlike an actual street address, which cannot be changed without leaving considerable evidence, switching IP addresses on the Internet is simple, quick, and hard to detect. Consequently, an individual or a corporation can travel from one area to another swiftly and easily without altering the environment. 

Big data security and cyberattacks

Going all in and getting it right 

As big data security analytics are incorporated into every organization, it’s important to mention how internet of things and big data analytics can help security. In fact, most of the information collected daily flows in from an increasing number of smart gadgets collecting, analyzing, sharing, and sending it in real-time. Furthermore, thanks to the internet of things (IoT), all interconnected networks provide essential information to track malfunctions. However, despite the efforts of businesses to abide by government rules to provide customers with the required protection, data breaches continue to grow at an alarming rate. Our deep experience in data analytics services allows us to offer some working practices for effective big data security analytics: 

Execute endpoint security 

Having trusted certificates at each endpoint helps to keep your information secure. Extra safety precautions such as frequent resource testing and enabling trustworthy devices to connect to the network via a mobile device management platform can also help. However, be sure that all the data is correct. Keep in mind that many input programs and devices are vulnerable to viruses and hackers. In addition, intruders are capable of imitating other login IDs or polluting the system with false information. Your big data security analytics procedures should be able to detect and prevent infiltration and detect inaccurate assets. 

Adopt customized solutions 

Big data security systems are extremely complicated, and protecting them is tough, so there’s no such thing as a one-size-fits-all answer. Instead, businesses must tailor a set of techniques that satisfy the security goals shown at the outset of the entire process. In addition, with big data platforms increasingly being handled as custom apps rather than databases, there’s a chance to employ proper security strategies to meet current safety requirements. 

Use real-time security monitoring and compliance 

Compliance is a source of frustration for many businesses, especially when dealing with constant high volumes of information. At each stack level, you may face the problem using real-time security big data analytics. After that, you may apply security restrictions throughout the stack at the application, cloud, and cluster levels, mine logging events, and even deploy front-end security solutions. Companies should also avoid any attempts that try getting around big data infrastructure. 


Big data cyber security analytics will only become more vital as businesses look to effectively secure themselves against the growing cybercrime. In fact, the value of the global big data analytics market will continue to grow, and it’s projected to reach $420.98 billion by 2027, growing at a CAGR of 10.9% from 2020 to 2027. 

Unfortunately, getting big data and cybersecurity wrong may cost companies a lot of time and money. So, what does it take for companies to choose the right kind of solution? The answer mainly depends on your actual business needs, the part of big data you want to secure, and how you want to safeguard your information. Ensure you get it right and seek providers who can fully understand what’s best specifically for your organization. Contact us to help you answer your questions and customize your experience.