The increasing reliance on big data and the interconnection of devices through the Internet of Things (IoT) has created a broader scope for hackers to exploit. Now both small and large businesses have an even wider surface to work on protecting. Yet, all it takes is one new trick for an attacker to penetrate even the most sophisticated firewalls in a matter of seconds. The good news is that while, on the one hand, increased reliance on big data puts businesses at risk of cyberattacks, if used well, the same data can be used to enhance cybersecurity.
How Big Data Is Helping Cybersecurity
We are so used to the idea of protecting data that using it to bolster cybersecurity might not be top of mind. However, it's not only sensible, but also incredibly effective. According to the results of a study conducted by Bowie University, 84% of businesses using big data successfully managed to block cyber-attacks. What was their secret? Three words: big data analytics.
Big data analytics refers to the process of analyzing or assessing large, varied volumes of data that is often unexploited by regular analytics programs. The data can either be unstructured or semi-structured, and in some cases, it could be a mix of both. Initially, the aim of analyzing such data was to make data-driven decisions and determine customer preferences to improve operational efficiency and enhance client satisfaction. But now, data analytics is also being used to retrieve important information from big data, with the sole aim of strengthening cybersecurity. This is done by analyzing historical data to come up with better security threat controls.
By combining big data analytics and machine learning, businesses are now able to perform a thorough analysis of past and existing data and identify what's “normal.” Based on the results, they then use machine learning to strengthen their cybersecurity parameters so they can receive alerts whenever there's a deviation in the normal sequence of things, and consequently, thwart cybersecurity threats.
For instance, if big data analytics on past and existing data show that all employees log in an entity’s system at 8 in the morning and log off at 5 in the evening, the business will mark this as the standard and expected sequence of things. They will, therefore, come up with a way to prevent and get alerts any time there’s an attempted login before 8 a.m. or 5 p.m. This, in turn, can prevent potential hacks from happening. In a nutshell, carrying out a thorough analysis of historical data helps an organization identify its network’s regular patterns, so it can come up with solutions to detect and prevent deviations in real-time.
The Analysis of Current and Historical Data for Threat Visualization
By analyzing big data, businesses can foresee future attacks and come up with effective measures to prevent them. For instance, if a company is already a victim, carrying out a thorough analysis of the data of the events leading to the attack helps it identify the patterns followed by the hackers before they gained successful entry into the network. They can then use machine learning to formulate a solution that will ensure such a thing doesn't happen again.
Alternatively, if a business has never been attacked, it can use current and historical industry data to identify strategies used by hackers to attack other entities. Based on what it comes up with, it can then visualize what steps similar attackers would take to penetrate its system, and consequently, come up with a solution before they do.
While it’s true cyber-criminals do target big data while formulating their attacks, organizations can use the same data against them through data analytics and machine learning.