15 Sep · 6 min read
With some of the largest data breaches in history occurring in 2021, it's clear that cyber-attacks aren't going away anytime soon. From the Capital One banking data breach, which affected over 100 million customers, to the major Flipboard breach, which could affect over 1 billion users, the rise in cyber-attacks is extremely concerning for businesses and individuals.
In addition, the amount of personal information processed by businesses around the world has grown significantly over time. As a result, cybersecurity practices have had to advance further than ever before, and new methods of processing such large volumes of data have had to be introduced.
This is where big data has the potential to be extremely useful, as it can not only help to block potential cyber-attacks but also analyze massive amounts of data much more easily than was previously possible. Despite this, big data and cybersecurity have a long way to go before they can coexist peacefully as it is not completely secured.
Simply put, big data is a massive amount of structured and unstructured information that, due to its size, cannot be processed using traditional databases and software techniques. It is increasingly being used by businesses to discover patterns and trends in behavior, as well as for advanced threat detection and machine learning.
According to Forbes, the big data analytics market was estimated to be worth $203 billion in 2017. As businesses seek to adequately protect themselves against the growing threat of cybercrime and handle ever-increasing volumes of data, the market's value will undoubtedly rise significantly in the coming years.
However, the rise in big data usage has not gone unnoticed by online criminals, with many hackers now making big data companies their primary target. The increasing number of data breaches in recent years is a clear indicator of big data's vulnerabilities. But, what are the specific challenges of big data in cybersecurity? In the later subsection, we'll go over everything in greater depth.
Maintaining business growth and performance while protecting sensitive information has become increasingly difficult as cybersecurity threats continue to rise. As a result, one of the most difficult challenges of big data in cybersecurity is preventing data breaches.
Businesses all over the world process countless sensitive records daily. If this information falls into the wrong hands, the consequences could be disastrous, as evidenced by previous data breaches, including the ones mentioned earlier.
We will go over some of the most common big data security concerns.
Data privacy is a major issue in today's digital world. Its goal is to protect personally identifiable or sensitive information from cyberattacks, breaches, and intentional or unintentional data loss. To strengthen Data Protection, businesses must adhere to stricter Data Privacy principles with the assistance of cloud access management services, including stringent privacy compliance. Along with implementing one or more Data Security technologies, it is best to follow a few rules. The general rules are knowing your data, having more control over your data stores and backup, protecting your network from unauthorized access, conducting regular risk assessments, and training users on Data Privacy and Data Security regularly.
Controlling which data users can view or edit allows businesses to ensure not only data integrity but also data privacy. However, managing access control is difficult, especially in large organizations with thousands of employees. However, the transition from on-premise to cloud-based solutions has simplified the process of working with Identity Access Management (IAM). IAM controls data flow through identification, authentication, and authorization. Following relevant ISO standards is a good place to start to ensure that organizations are adhering to the best IAM practices.
Businesses are using Cloud Data Storage to quickly move their data and speed up business operations. However, the risks associated with security issues are exponential. Even the smallest error in data access control can allow anyone to obtain a trove of sensitive information. As a result, large technology companies embrace both on-premise and Cloud Data Storage to achieve security and flexibility.
While mission-critical data can be stored on-premise, less sensitive data is kept in the cloud for convenience. Companies, on the other hand, require cybersecurity experts to implement security policies in on-premise databases. Even though it raises the cost of managing data in on-premise databases, businesses must not take security risks for granted by storing all data in the cloud.
Fake data generation is a serious threat to businesses because it consumes time that could otherwise be spent identifying and resolving other pressing issues. Because assessing individual data points can be difficult for businesses, there is more opportunity for leveraging inaccurate information on a large scale.
False positives for fake Data can also be used to drive unnecessary actions that may reduce production or other critical business processes. One way to avoid this is for businesses to be critical of the data they are working on to improve business processes. An ideal approach would be to validate the data sources regularly and evaluate Machine Learning models with diverse test datasets to detect anomalies.
There are several Machine Learning solutions available today, such as chatbots that have been trained on massive amounts of data. The benefits of such solutions are that they improve as users interact with them. This, however, leads to Data Poisoning, a method of attacking the training data of Machine Learning models. It is classified as an integrity attack because tampered training data can impair the model's ability to predict correctly. The consequences can be disastrous, ranging from logic corruption to data manipulation and injection. Outlier detection, which allows the injected elements in the training pool to be separated from the existing data distribution, is the best way to combat evasion.
Because of advances in data culture, every employee now has access to critical business information. While it promotes data democratization, the risk of an employee intentionally or unintentionally leaking sensitive information is high. Employee theft is common not only in large tech firms but also in startups. To prevent employee theft, businesses must implement legal policies as well as secure the network with a virtual private network. Furthermore, businesses can use Desktop as a Service (DaaS) to remove the functionality of data stored on local drives.
Overall, it is appropriate to suggest that big data analysis can be extremely beneficial for businesses by detecting growth and performance insights that can help drive a business forward. Big data is also arguably the best way forward in cybersecurity, as it makes detecting threats at the earliest possible opportunity easier than ever before.
Big data has undeniable benefits for any company that needs to process large amounts of data regularly. Despite this, cybercriminals' increasingly sophisticated techniques are becoming increasingly difficult to combat. Taking this into consideration, it is safe to say that businesses must implement optimal cybersecurity practices. Otherwise, large-scale data breaches will continue to wreak havoc.