What is big data analytics and how does it help prevent cybersecurity threats?

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Understanding the needs of your customers is integral to customer satisfaction. And, you need all the information you can get about them to deliver the high quality products or services they want.

The generation and analysis of customer data is essential. If you are running a large-scale business, big data analysis is useful for making informed decisions to move your business forward.

Did you know that you can also use big data analytics to prevent cybersecurity threats? In this article, you’ll learn about big data analytics, how it works, and how you can use it to prevent cybersecurity threats.

What is big data analysis?

Network data

Big Data is a diverse category of data in high volume, variety and speed. The people who interact with your business produce tons of data that is originally raw or unstructured.

The use of advanced technologies driven by Artificial Intelligence (AI) and the Internet of Things (IoT) in modern businesses is increasing the volume of data generated by consumers. Big companies end up with more data than it would be difficult to analyze without big data analytics.

Big data analysis is the process of studying and analyzing patterns of behavior to make informed decisions and predictions.

Businesses interpret large volumes of consumer data using analytical techniques and software to better understand their consumers, meet their needs, and position themselves strategically.

How does big data analytics work?

Cyber ​​security

Data in raw form is of little or no importance to your business. Rather than operating blindly, you can leverage big data to get a clearer picture of where your business is going.

Here’s how big data analytics works:

1. Data collection

The first step in big data analysis is data collection. It’s the process of getting raw, unstructured, or structured information about your customers.

The different data collection media include cloud storage, artificial intelligence, mobile apps, in-store IoT sensors, etc.

You can store the data you collect in warehouses for easy access by the intelligence software you have adopted in your business.

If you are dealing with raw data that is too complex, you can store it in a data lake instead of a warehouse for better access and analysis.

2. Data processing

Most of the data you collect is raw or unstructured. To improve the accuracy of your scan, you should organize it properly before continuing.

The high volume of data being generated nowadays makes it more difficult to process data as you might have an overwhelming amount of data in your hands. However, there are two main methods that you can implement to simplify the processing of your data: batch processing and flow processing.

Batch processing examines large chunks of data over a long period of time. This is very effective when you have enough time to collect and process your data. Stream processing, on the other hand, examines data in small batches. This is very effective when you need to make decisions faster, thereby shortening turnaround times.

3. Data cleaning

No matter how big your data is, you need to clean it to get rid of dirt that could affect the accuracy of your results.

The cleanup process also involves proper formatting and removing irrelevant information.

4. Data analysis

After getting rid of the dirt from your data and formatting it, it’s time to analyze it.

Turning your data into valuable information takes time, but there are processes you can use to speed up the process. The most common big data analysis methods are:

Data mining

Data mining is the process of sorting large databases to identify existing patterns by looking for anomalies and creating clusters.

Deep learning

Deep learning is the process of mimicking human learning with the use of machine learning and artificial intelligence to find similar traits in complex and ambiguous data sets.

Predictive analytics

Predictive analytics is the process of relying on your organization’s historical data to make future predictions about risks and opportunities.

5 Ways Big Data Analytics Prevents Cyber ​​Security Threats

Cyber ​​threat

The increase in data generation creates more opportunities for cyber threats. Cybercriminals are eager to compromise the large volumes of information that large companies produce.

While there is still a lot of work to be done to create safer cybersecurity, data analytics sets up good defenses against cyber attacks. Let’s take a look at some of the ways big data analytics prevent cybersecurity threats.

1. Predictive models

After observing cyber threat patterns, you can create predictive models that trigger alerts as soon as a pattern is observed at your network entry point.

Software services such as AI and machine learning help ensure the accuracy of cyber threat models used in the development of predictive models. If a cyber threat goes beyond your network touchpoint, your system can counter it with a real-time defense response.

2. Large-scale monitoring and automation

Employee ignorance of sound cybersecurity practices causes a large number of cyber attacks in many organizations. If your employees don’t know what to do to avoid an attack, there are things they can do to help attackers get into your network.

While it is highly recommended to create a healthy cybersecurity culture in your organization, you can also use big data analytics to monitor the activities of your employees to detect vulnerable activities. If someone takes an action that puts your network at risk, the system reacts to limit the effect.

3. Real-time intrusion detection

A chance is all an attacker needs to bring down your network. It is your responsibility to make sure they never get that chance.

Implementing intrusion detection systems is a great way to stop an attack before it approaches your network entry point. With big data analytics, you can automate this kind of large-scale process.

Real-time analysis of data generated by intrusion detection systems exposes the details of an attack, showing you all the information you need to stop it. You can integrate this data into your ground defenses to instantly negate an attack.

4. Intelligent risk management

The more you know about the possible dangers around your system, the stronger your defenses will be.

Big data analysis gives you concrete insight into the activities in and around your network. A deep analysis of your cybersecurity data exposes the root causes of security breaches, giving you insight into the origin of those activities.

5. Threat visualization

Big data analytics allows you to be proactive in your cybersecurity strategies by helping you make more accurate predictions about your network activities.

If your organization has experienced a cyberattack in the past, you can use your big data analytics report to gain insight into the attack.

Related: Reactive or Proactive Security: Which Is More Effective?

How did the attacker gain unauthorized access to your network? Which points did they enter? You can find answers to these questions and more in your data analysis reports to prevent future recurrence.

Leave no room for cybercriminals to penetrate your network

Most cyber attacks are caused by human error and blind spots in a network. Either you are unaware of an existing flaw or you have fallen prey to malicious antics from cybercriminals.

Big data analysis highlights vital information about your system’s operations. And, if you ever make a mistake in your operations, you can rest assured that there will be a real-time response to save the day.


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