Top 10 Threats to Big Data Security and Privacy

Top 10 Threats to Big Data Security and Privacy

In the past, all information, from personal identification to financial documents, was made in physical form. This kept them safe from theft. Information is, in many ways, the most valuable thing in our lives.

Consider your financial information as an example. If it is stolen, you could have serious problems. In a matter of seconds, someone could wipe out all your savings.

The potential threats faced by people were completely different when all information was stored in hard copies and safes. Today, however, data can be stored digitally instead of in the physical form.

This change brought many benefits, but it also created some problems. Bad actors can easily hack into databases and steal information if they don’t have strong security tools. There have been numerous cases of hackers stealing information from companies and stealing financial information.

The threat to our security is growing stronger with each passing day.

What is Big Data?

Because of its many advantages, companies started to transfer and use big data. They can store digital data keep track of large amounts of information and quickly access it.

Due to their large size, big data is a collection of data that can’t be processed in traditional databases. It is used primarily in customer management services to gather more information about customers and provide a better experience.

While big data is a great resource for businesses in many ways, it can also pose a risk to their online security if they have too much data.

What are the Issues with Big Data Security?

Large corporations have the ability to afford the best online security services. Smaller and medium-sized businesses, however, cannot have the best online security services. They must use big data to reap the many benefits of this technology in order to survive in a highly competitive market. Although it has allowed millions of businesses to thrive and grow, the cloud can also lead to many dangerous situations.

Most of the time, the technology used to protect the data is not effective enough to stop any threats. Many security technologies are not suited to handling dynamic data. While they might be able to handle static data, streaming data is completely different.

A security check that is performed every once in a while may not be sufficient. It will not identify security patches in continuous streams of data.

These are the top threats to big data security and privacy that all major companies are trying to address:

1. Being Vulnerable to Fake Data Generation

Fake data generation is the first step before we get into the heart of big data threats.

Cybercriminals often try to hack into stored data, creating fake data and then inserting it into databases. Cybercriminals can change data slowly over time, making it more difficult for companies to solve or discover problems.

The company could be completely unaware of what is happening because this hacking technique can lead to a complete missive. So, the first thing to do is to make sure that you are not at all vulnerable to fake data generation and this can be successfully implemented by rigorously checking for potential hacks. So, the first thing to do is to make sure that you are not at all vulnerable to fake data generation and this can be successfully implemented by rigorously checking for potential hacks.

Also read: Top 10 Big Data Challenges and Solutions

2. Untrusted Mappers

Once big data has been collected, parallel processing is used to process it. One way to do this is to use a MapReduce paradigm. This is a method that Google developed initially to split big data into multiple pieces that are then processed by a mapper to assign them to storage options.

It is crucial to know the point of time as well as the code used by the mapper. Cybercriminals can alter the settings or even create new ones if they have access to that code. Cybercriminals can create insufficient key/value pairs lists and destroy your data processing. They may also be able to gain access to sensitive information to sell it to others via the dark web.

Most companies do not have any additional security layers in place to stop such a scenario. Instead, they concentrate more on their perimeter security systems. It is important to have multiple layers of concrete security in place when it comes to protecting big data. This will ensure that hackers don’t find large holes in your system.

3. Leaving Stored Data Unencrypted

It is obvious that big data must be stored securely in a business’s data encryption key. However, many companies fail to use it.

Although important information is often stored in the cloud, it is not recommended to do so without high-level encryption protocols.

Although it is true that encryption and decryption of large data take longer, it is better to take your time and preserve all your data than rush to complete the task and lose everything. Data protection is the most important aspect of balancing speed and security.

4. Data Mining Risks

Companies tend to focus their efforts on creating a perimeter-based security system that protects all entry points and exits from unwelcome access. They completely overlook what an individual can do within the system.

There is a possibility of loopholes in the system. There is also the possibility that a company employee might sell data to other companies in order to make a profit. This is especially true for the IT department. However, it only takes one bad apple to destroy the entire system.

Insider jobs can have serious consequences, especially if customer data is involved. You can solve this problem by adding more security perimeters or hiding your data in anonymity so that even if they do manage to obtain it, they will end up with nothing.

Also read: 7 Best Data Mining Techniques

5. Granular Access Control, and Big Data

Granular access control allows users to access a portion of data without having to reveal all the information.

In the case of medical records, there are many personal details that you don’t want any random medical researcher to view. There will be situations where the researcher needs access to specific parts of the data in order to do their job. The solution in such situations is to give special access to certain people to enable them to complete their tasks.

Due to a large amount of data, access control can be very difficult in big data. The technology is currently not efficient enough to allow this.

A more effective solution is to copy the data to a separate storage unit like a data warehouse, and then grant access to certain people.

But, this can lead to problems in the system’s performance and maintenance as there is are now more places to store the data.

6. Issues With Historical Data Records

Data provenance or historical data records, which document the data source and any actions taken on it, are called data records. Because of its large size, big data has a lot of metadata.

Security is a concern for historical data records. If metadata is altered by a cyber criminal, it could lead to incorrect data sets that make it difficult to access particular information. It will be very difficult to determine the cause of a security breach or fake data generation.

7. Failure to Use Valid End Point Inputs

Big data is maintained using end-point inputs and devices. Endpoint input data is crucial for data storage and processing.

Invalid or illegitimate devices can cause problems in the data processing. It is important that companies only use valid and authentic endpoint devices in their data processing processes.

8. Non-Regular Security Audits

It is essential to have regular security audits of big data if your company deals with large amounts of data. Although big data audits can be time-consuming, they should be performed on a regular basis. Sometimes companies will skip this step altogether because of these reasons.

An audit of your security can help you assess your current status in protecting your data. While ignoring one may not cause your business to fail, you don’t want negligence as the reason for a data breach.

Regularly analyzing security logs will help you spot any signs of malicious activity and an incoming cyberattack, before they become a major crisis.

Also read: 6 Ways Cloud-Based Security Tools Can Help Your Business

9. Problems with NoSQL Databases

NoSQL databases are extremely popular in big data because of the many applications it provides. Despite its popularity, there are several key problems. NoSQL is not able to encrypt data after it has been tagged, logged, or distributed to different groups.

Although this is a serious security issue, most people ignore it.

10. Complexity and Big Data Diversity

Although big data is relatively new, it is extremely complex by its very nature. Although it is simple to store and manage conventional data, it can be difficult to efficiently and effectively protect big data due to the complexity of its data sets.

Many sources can provide big data diversity. Some data forms will be structured while others are unstructured. Some data sources could be mobile devices data or servers, while others could be email files or cloud-based applications.

It is difficult to provide real-time and active protection due to the variety of data available.


It is often difficult for security and other departments to keep up with the pace of new technologies. Big data has many benefits and potential opportunities for businesses, but it is important to recognize the potential dangers that it can bring. Although it may provide you with valuable information that can help your business grow exponentially, many platforms that use it today were not designed with security in mind. These platforms lack encryption protocols, policy management, or basic security features.

Big data has many potential problems. And, because it’s still relatively new, there may not be an efficient solution for certain problems. Big data has its pros and cons, but that doesn’t mean you shouldn’t use it.

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