Behavioral Analytics for Cyber Security
Enterprises, today, are in a constant tug of war between ensuring security without compromising on productivity. Their goal is to somehow tackle the ever-increasing security threats without locking down the enterprises’ environment. The quest to achieve that perfect balance between security and productivity puts enterprises in a challenging position where they are constantly on the lookout for increasingly sophisticated threat prevention and detection solutions. Behavioral Analytics is one of the many modern methods that focus on detecting suspicious behaviors at the user or network end way before they become an actual threat.
What is Behavioral Analytics?
It is a part of Data Analytics focusing on driving insights based on human actions. Businesses use behavioral analytics to predict consumer demands and market trends. However, implementing behavioral analytics as a cybersecurity strategy is also becoming a common practice. Human beings have certain habits that are visible in their use of the internet as well. By deploying behavioral analytics that outlines an individual’s usual behavior patterns, enterprises can detect anomalous behavior and suspicious activities to ward off malicious attackers. But what sort of threats can behavioral analytics actually predict? And how can companies leverage behavioral analytics to achieve maximum results? The answer to that may not be a straightforward one. However, gaining deep insights into the users’ behavior patterns and focusing mainly on security-critical operations to reduce false alarms and highlight actual threats is the key.
What’s Behind Behavioral Analytics?
Behavioral Analytics leverage machine learning algorithms and incorporate SIEM systems to allow enterprises to gain a holistic view of their security infrastructure. Unlike traditional security approaches that focus on generating alerts and locking down the entire system, behavioral analytics focuses on detecting suspicious activities that are more likely to become an actual threat and not just a false alarm.
How Behavioral Analytics work?
Behavioral analytics is based on a number of user behavior patterns such as specific schedules that employees follow. Any activity carried out by an employee outside of his/her regular work hours could be a potential warning sign and can trigger some other authentication mechanism. Another potential warning could come from a user trying to access application or resources that they are not authorized to access. The physical location of users is another parameter utilized by behavioral analytics.
A user that is supposed to be in New York should not have a Washington based IP address. Similarly, a user trying to access the internal network through an unknown public WiFi should also trigger a warning.Taking this a step further, behavioral analytics can even identify a change in typing pattern, keyboard strokes or mouse moves to determine if something suspicious is going on at the user’s end.
There’s no one-size-fits-all solution
With behavioral analytics, security teams must first develop an understanding of how and where can it actually help the organization. Security teams must familiarize themselves with the regular flow of data within their network to be able to detect anomalies in the data flow. Following good network security practices can also ensure that malicious users stand out as soon as they fail to conform to the normal network behaviors, which they most likely will. Organizations must keep track of several or all of the above-mentioned factors to utilize behavioral analytics as a powerful security tool while reducing the number of false positives. Similarly, to avoid an overwhelming amount of threat alerts, security teams can identify the most critical operations in their IT environments and apply behavioral analytics to specific infrastructures only.
Another important aspect is to integrate information about employees that are traveling or those who are on a break by integrating the SIEM systems. Instead of a one-size-fits-all solution distinctly blocking a few pre-defined events from happening, implementing behavioral analytics in such a way can yield better results for ensuring cybersecurity. Essentially, the goal is to block only the things that look malicious and allow everything that seems alright to proceed smoothly in order to avoid causing disruptions and compromising productivity.
