How Data Analytics for Cyber Security Prevents Threats

Why Is Data Analytics For Cyber Security Important?

Today’s time can be called the digital era because everything is happening online. There is not a single industry or business that does not use the internet to carry out some activities. For this reason, a large amount of information is available online, and hackers are constantly lurking about their prey. Data analytics for cyber security increase the level of data security and help prevent malicious activities online.

It constantly scans requests on the network, analyzes them, and tries to find suspicious activities. When the system detects something suspicious, it promptly activates the following steps and implements measures to prevent malicious activities.

How Is Data Analytics Used In CyberSecurity?

Data analytics for cyber security helps analyze a huge amount of data to detect any malicious activity. Hacking attacks are frequent activities carried out by individuals to steal important and confidential company or individual information. Information is precious today, and I think the hunt for information has become more profitable than the hunt for elephants. I would like to point out that I do not approve of hunting in any way.

Data analytics in cyber security closely monitors network behavior and tries to identify anything unusual. For example, tonight at 3 AM, someone is constantly trying to access an account but continuously entering the wrong password. This could attract attention because it's likely a hacking attempt.

Yes, you would agree that this indicates a brute-force attack. For those who haven't heard, a brute force attack is when someone keeps trying to guess the password by trying various combinations until they guess the right one. No, they don't do this manually by typing different passwords on the keyboard, but they use serious tools and scripts.

In the seventh mega-event that struck Target (a large retail chain) in 2013, Target fell victim to a hacker attack in which hackers stole data on tens of millions of customers' credit and debit cards. In this attack, they used one of the phishing methods. So, if they had better data analytics, cyber security would have identified the attack in time and prevented this tragedy, and the consequences would have been much smaller.

Effective Methods Of Data Analytics For Cyber Security

Detecting and preventing threats is crucial in cyber security. If criminals sniff out and preempt company data and information in time, they will remain safe. Therefore, it is necessary to take and implement certain methods to preserve data protection in the company.

I want to mention just a few of the most common practices used in data analytics for cyber security:

1. Real-time monitoring: This excellent system continuously scans network activity. It listens to communication and requests sent in real-time. As soon as it detects something suspicious, the system triggers an alert and automatically sends it to prevent unauthorized access to data.
2. Automating threat detection: The most popular technology used for automating threat detection today is artificial intelligence (AI). AI utilizes its smart machine learning algorithms to recognize what is a threat and what is not.
3. User behavior tracking: This involves monitoring user activity. If there is a change in user behavior, such as the user being active outside of working hours, this suggests a hacking attempt.
4. Using historical data: This involves analyzing previous attacks and learning from them. Here, the system learns to recognize repeated patterns. If the pattern repeats, it can be easier to react.

Example: In May 2017, Equifax experienced a data breach. If they had used advanced data analytics in cyber security, they could have detected the vulnerability in the system before hackers stole data on over 147 million people.

Effective methods help you avoid becoming the next Target or Equifax.