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The Importance of Predictive Analytics and Machine Learning in Cybersecurity

Experts in the realm of cybersecurity are continually trying to keep up with the changes in the threat landscape. Even with advanced tools on hand, any IT security professional knows that a data breach can happen at any time.

Solutions that rely on machine learning (ML) have somewhat become a remedy for this situation. For one, they allow organizations to reduce the amount of time spent on detecting attacks. However, we continue to see attacks occur.

Is there a way for organizations to predict threat actors' next likely move? Using predictive analytics may be the answer.

What Is Predictive Analytics?

Predictive analytics is an approach that is starting to gain popularity in almost every industry. It enables companies to foresee what could happen and acquire foresight that they previously lacked.

Predictive analytics is also worming its way in cybersecurity. With it, organizations are gaining the capability to determine attack probability, allowing them to reinforce their defenses against incoming attempts even before they surface.

Many cybersecurity vendors are now integrating the process aided by ML into the core of their security offerings. The question is, how exactly can predictive analytics help in combating threats?

How Predictive Analytics Can Enhance Cybersecurity

Let's take a look at three essential ways by which predictive analytics can bolster an organization's network defense.

Near-Real-Time Data Provision

A proactive approach to cybersecurity is becoming the norm, given the current threat landscape. Companies need to be able to crunch data, determine patterns, and identify anomalies as quickly as possible.

Using predictive analytics successfully translates to correlating related incidents and finding patterns and trends to pinpoint what worked and what didn't work. The moment something strange occurs, analysts can immediately jump in and start investigating. Real-time data from various sources can be processed using predictive analytics to identify common attack vectors, for instance, to strengthen defenses against these in advance.

Compatibility with Big Data

Cybersecurity teams face the significant challenge of dealing with vast amounts of data. Filtering and making sense of big data, primarily unstructured information, in the same manner, can be difficult. Massive streams can come from a wide range of databases, programs, and devices. Before these can be analyzed, they first need to be parsed. Organizations need a system to make them all work together.

Predictive analytics solutions, fortunately, thrive on using big data. In fact, the more inputs available, the more insights users can get out of them to make relatively accurate predictions.

Workload Reduction through Automation

Combining predictive analytics with ML can help analysts obtain crucial insights on threats faster. ML can ease the burden on analysts' shoulders by automatically filtering out duplicates, categorizing information, and maybe even determining which incidents to prioritize. ML also reduces human error that is prone to happen due to the sheer volume of information that needs to be processed. Predictive analytics systems backed by ML can thus produce more actionable intelligence.

Predictive analytics backed by ML would only work, however, with well-structured big data. That said, it may be a good idea for users to consider additional threat intelligence sources to get the most out of their systems. One that provides deep insights into a domain — a unique threat source identifier — could be particularly useful.

Consider Using a Domain Reputation Tool

A proactive defense relies on blocking threats from the source. As such, companies need to integrate a comprehensive data source into their predictive analytics solution. Because we know that malicious individuals typically distribute malware via websites, employing a domain reputation API may be beneficial. With this app's help, predictive solutions can be configured to consider the reputation of each domain and the site tied to it when assessing risks.

An API that is backed by a regularly updated database is ideal to assure users they are obtaining accurate domain information. Domain reevaluation is necessary because any site is prone to compromise at any time. This capability provides for more accurate threat detection and risk mitigation.

Other APIs and data feeds pooled from various sources can also be integrated into solutions as well. After all, didn't we say that the more data a predictive analytics system to analyze, the better?

* * *

Predictive analytics is the next big thing in the field of cybersecurity. Companies that want to prevent even unknown threats from affecting their networks would do well to jump on the bandwagon.

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