10 Threats to Big Data Security and Privacy

Updated on :June 12, 2024
By :Sophie Ross

In today’s online digital world there is a clear danger of getting hacked.

A few decades ago, everything important from individual identification to a company’s financial documents were made and stored in physical form, generally safe from getting stolen. In many ways, information is the most precious thing in our lives.

Take your financial information for instance; if it gets stolen, you will have a really serious issue on your hands. Someone could potentially wipe out all your savings and get away with it in a matter of seconds.

When all the information was kept in physical form and in safes, the potential threat people faced was of totally different kind. But today in this digital age, data is now stored digitally rather than in physical form.

While this change created a lot of convenience and ease, it came with its own sets of issues. Without robust and powerful security tools, bad actors can easily get into database and steal information. From hacking into private individuals’ financial accounts to almost bringing down entire companies by breaching their big databases, there have been many such cases in the last decade alone.

As time passes, the threat is only getting stronger.

What Is Big Data?

Companies started transferring and utilizing big data due to its numerous advantages. Digital data allows them to store, keep track of and access massive amounts of information quickly and easily.

Big data are datasets that cannot be processed in traditional databases due to their size. It is mainly used in customer management services so as to gain more information about customers and thereby provide them better experience.

Big data is extremely helpful to businesses in various ways but at the same time having loads of data can be quite risky if their online security isn’t strong enough.

Why Are We Seeing Issues in Big Data Security?

Huge corporations can afford to have the best online security services in the world. However, the same cannot be said about smaller or medium sized businesses. In order to survive in the highly competitive world, they need to utilize big data for its many advantages. Storing massive chunks of data in the cloud has helped millions of businesses to grow and thrive, but at the same time it has led to several dangerous scenarios.

In most cases, the technology being used to safeguard the data isn’t efficient enough to thwart any threats. A lot of security technology out there just isn’t fit to handle dynamic data. They might be good for static data, but streaming data is a completely different ball game.

A normal security check performed once in a while is not sufficient as it will fail to identify security patches in continuous stream of data.

Here are some of the biggest threats facing big data security and privacy which all the major companies are working on to fix:

  1. Being Vulnerable to Fake Data Generation

Before getting into the core of big data threats, it is important to first focus on fake data generation.

Cyber criminals often go for undermining stored data by generating fake data and inserting it into databases. For instance, if a company uses a certain type of data to either discover or solve a problem, a cybercriminal can get in and change the results slowly over time to make the company more vulnerable.

This hacking technique can cause the company to completely miss out on what is actually taking place. Hence, the first thing to do is to ensure that you are not vulnerable at all to fake data generation and this can be successfullyimplemented by rigorously checking for potential hacks.

  1. Untrusted Mappers

After the collection of big data is done, it is processed through parallel means. One such way is using a MapReduce paradigm, a system initially developed by Google in which big data is split into several pieces and processed by a mapper to allocate them to specific storage options.

The point of time and the mapper’s code is very crucial. If cybercriminals somehow get their hands on that code, they can change the settings and even add new ones. They can then create inadequate lists of key/value pairs and ruin your data processing. They can also gain access to sensitive information and sell it to third parties on the dark web.

In most cases, companies don’t have additional security layers to prevent such a scenario from happening as they focus more on perimeter security systems. When it comes to securing big data, it is highly important to have several layers of concrete security built into your process so that hackers will not find huge holes in your systems.

  1. Leaving Stored Data Unencrypted

Although it is quite obvious that businesses must use a data encryption key to secure storage of big data; the fact is lot of companies fail to do it.

Typically, important information is stored in the cloud but doing so without using high-level encryption protocols is a big mistake.

It is true that encrypting and decrypting big data leads to a slower work process, but it’s far better to do it slow and have all your data than to do it fast and lose everything in mere moments. When it comes to choosing between speed and security, ensure you lay emphasis on data protection.

  1. Data Mining Threats

Most companies focus their time and energy into setting a perimeter-based security system where any and all points of entry and exit are made secure from unwanted access. However, they completely ignore what someone can do from within the system.

There are chances of some internal loop holes in the system. Also, someone from the company might mine data and sell it to rival companies for financial gain. The IT department in particular is usually well-trusted to not engage in such nefarious activity; however ideally it takes only one bad apple to ruin the complete system.  

The ramifications of an inside job can be extremely severe especially if you are storing customer data. This issue can be solved by simply adding more perimeters to your security system or by shrouding your data in anonymity so that even if someone manages to get their hands on it they will only end up with blank data.

  1. Granular Access Control and Big Data

Granular access control allows a user to get access to a certain portion of the data without revealing the full information.

For instance, in the case of medical records, there is a lot of personal information that you don’t want a random medical researcher to see. At the same time, there will be many scenarios in which the researcher requires access to certain parts of the data to complete their job. In such cases, the solution is to grant special access to specific people so that they can perform their tasks.

However, in the case of big data, granular access control is extremely difficult due to the sheer size of the information. The technology currently doesn’t allow one to do so in an efficient manner.

Another effective way to solve this issue is to copy the data into a separate storage entity like a data warehouse and then to grant access to specific people.

However, doing so gives rise to potential problems in the system’s performance and for its maintenance, as there is now more places where the data is stored.

  1. Issues with Historical Data Records

Historical data records or data provenance, document the source of data and all actions performed on it. In the case of big data, the amount of metadata collected is massive due to its size as a whole.

From a security angle, historical data records pose an important concern. For instance, if the metadata is manipulated by a cybercriminal, it might result in improper data sets making it extremely difficult access specific information. Moreover, it will also be difficult to find the root causes of a security breach or fake data generation.

  1. Failing to Utilize Valid End Point Inputs

End point inputs or devices are used to maintain big data. Input data provided by end points is very important when it comes to the storage and processing of data.

Using invalid or illegitimate end point devices can create a lot of issues in data processing and so it is imperative that companies use only authentic and valid end point devices in their processes.

  1. Non-Regular Security Audits

If you are a company that deals with big data, then it is crucial to get regular big data security audits. Big data audits are time-consuming, however, it needs to be done on a regular basis. At times companies skip the process altogether due to such reasons.

A security audit can help you be aware of your standing when it comes to protecting your data. Ignoring one now and then may not bring down your business, but you certainly don’t want negligence to be the cause of a data breach.

By analyzing security logs regularly, you will be able to find out any early signs of malicious activity or an incoming cyberattack before it turns into a huge crisis.

  1. Issues That Come with NoSQL Databases

NoSQL databases are very popular in the big data industry for the application capabilities it offers. Said that, its popularity brings along with it several key issues. NoSQL  has several flaws, but a prominent one is that it’s unable to encrypt data when it is tagged, logged or distributed into various groups.

This poses a serious security concern but, sadly, it is ignored by most.

  1. Complexity of Big Data Diversity

Big data is relatively new, but it is also extremely complex due to its very nature. It is easy to store and manage static conventional data, but the complexities of data sets in big data make it quite challenging to protect it efficiently and easily.

Big data diversity can originate from several sources. Some data forms would be structured, whereas others would be unstructured. Some data sources will be from servers or mobile device data, and others could come from email files or cloud applications.

Due to the sheer diversity of data, it requires far more work and time to provide active and real-time protection.

Final Words

Often when a new technology starts advancing fast, it gets difficult for everything else including security to catch up with it. Though big data offers tons of advantages and opportunities for companies, it is extremely important to not underestimate the potential risks that will come with it. It may give you access to information that could grow your business exponentially, but a lot of platforms that use it now were never designed keeping its security in mind. Most of these platforms don’t have encryption protocols, policy enablement, risk management or even basic security features.

It is true that big data comes with a lot of potential problems and, as it is relatively new, even there are no efficient solutions for some problems. But that doesn’t mean that you should stay away from using it because the pros of big data weigh down its cons.

If you are not confident about the potential security implications, then hiring a big data consulting firm is the way to go. They can provide you with extremely useful knowledge to control and secure big data wisely. With a little bit of research, you can find some of the top big data consulting firms and services with a proven track record of finding real and effective solutions to the above mentioned big data threats. It may cost you some money and a considerable amount of time, but it’s a mere fraction of what you would end up paying if your databases become compromised.

Sophie Ross
Sophie Ross

Sophie is a marketing specialist at Security Gladiators. A writer by day and a reader by night, she is specialized in tech and cybersecurity. When she is not behind the screen, Sophie can be found playing with her dog.

Read Similar Blogs

Small Business Looking for Big Data? Here is What You Need to Know

Small Business Looking for Big Data? Here is What You Need to Know

‘Big Data’ has become yet another techy, trending, industry-wide buzzword that most of us know is important but find difficult to grasp. Data Scienc ... Read more

5 Ways Data Extraction Can Give You an Advantage In ECommerce

5 Ways Data Extraction Can Give You an Advantage In ECommerce

The fuel that powers ecommerce is data. A business needs fresh, relevant data for marketing, product development, and customer service. The internet produces a ... Read more

5 Data Visualization Best Practices to Power Your Data

5 Data Visualization Best Practices to Power Your Data

Our ability to capture data has also evolved significantly. Thanks to advances in cloud computing, we can now store massive datasets using sophisticated online ... Read more