Security Intelligence: Big Data Analytics for Cybersecurity

The business world is constantly digitalizing, which is putting it more at risk of cyber attacks. According to research conducted by CSOonline, big data security analytics is the answer to protecting businesses against these attacks.

84% of businesses use Big Data to help block these attacks - CSOonline

What is big data security analysis?

Big data security analytics is a collection of security data sets that are larger in quantity and more complex. These data sets are so complex that processing them using traditional security data processing tools is almost impossible.

In other words, big data analytics is the process of assessing big data. Big data analytics is used to discover

  • consumer preferences
  • market trends
  • hidden patterns
  • unknown correlations

This piece of information is to be used for making data-based decisions.

It enables businesses to enhance their

  • operational efficiency
  • improve its customer service
  • adopt more effective marketing strategies
  • find new revenue opportunities
  • gain a competitive advantage over its rivals

The process of big data security analytics involves complex applications mixed with statistical algorithms and predictive models that are driven by high-powered computing systems.

Big data analytics makes use of analytic techniques such as

  • (ML) Machine learning
  • (AI) Artificial Intelligence
  • Data Mining & Statistics
  • (NLP) Natural Language Processing

Data analytics tools are usually utilized by

  • Analysts
  • Researchers
  • Engineers

Characteristics of big data security analytics solutions

Let’s define the characteristics of modern big data analytics solutions.


Big data security analytics solutions must be able to collect, process, and store petabytes of data for various security analytics ventures.

Analytical prowess

Big data security analytics solutions must allow users to interact, enquire, and visualize huge data sets.


Big data security analytics must be built upon an appropriate computing architecture for processing the much-required data analytic algorithms and complex queries. It should be able to deliver results in the anticipated timeline.


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How did big data security analytics come to be?

The industry has reached a point where business intelligence algorithms have been commoditized for businesses of every shape and size. 

Developers are using modern and inexpensive frameworks like Apache Hadoop and hardware to build big data solutions. These solutions are used by enterprises to collect, store, and analyze unstructured data in real time.

Why is it necessary for businesses to pay attention to big data security analytics?

As we said, modern business technologies are undergoing swift changes. With these changes, even the threats are getting more advanced.

Rising cyber-security threats

A corporate security perimeter has started disappearing in recent years thanks to the growing adoption of cloud and mobile services. Information security experienced a shift from traditional protection tools toward detecting malicious activities within corporate networks.

Cybercriminals have started using sophisticated methods of attacking and breaching the security of modern enterprises.

Companies have started rethinking their security strategies

Analytics holds the key to leveraging cyber resilience. There is no stopping the attacks that are taking place on business enterprises.

How does big data analytics fit in this mix?

Improving attack detection is at the core of such an approach, this is where big data analytics plays its pivotal role. Modern data security infrastructure must be capable of identifying constantly changing patterns. This is necessary for

  • Rapidly executing complex analysis in real-time
  • Performing complex correlations across a variety of data sources
  • Logging all the network events, including every user activity

Businesses need to apply advanced analytics with the ability to run the analysis of current and historical data. Businesses must combine their current state of analytics with strong data security to improve cyber resilience.

Combining data for predicting suspicious activities

  • Doing so enables businesses to combine real-time and historical analysis for identifying new incidents.
  • This must be coupled with external security intelligence to provide real-time information about the latest vulnerabilities.
  • It facilitates the identification of advanced cyber attacks on the network.
  • A large amount of historical data can be used for identifying anomalies.

Identifying historic incidents

  • With big data analytics algorithms, modern solutions can identify data security anomalies, indicating malicious activities.
  • Filter out each and every statistical noise.
  • Using big data security analytics, businesses can restrict raw security events into manageable, concise, and categorized alerts that enable precise decisions.

Creating automated workflows

Modern big data security solutions help businesses create multiple automated workflows.

These workflows help in detecting threats, such as

  • Disrupting identified malware attacks
  • Submitting suspicious events for managing security services

Automated controls for cybersecurity and fraud detection are key drivers of implementing enhanced cyber-security.

Big data security analytics: Why is it needed for enhanced cybersecurity?

There is no hidden fact that business data is a valuable asset for enterprises. Entrepreneurs are more aware of its importance as the key success factor of their business.

Mobile devices are the most preferred tool for business owners today. These devices are collecting a massive amount of data from the users; cybersecurity of these devices is the top concern.

By using big data analytics and machine learning, every business can thoroughly analyze its collective information. This is also helpful in detecting any cyber attack on the business network.

Deploy a capable detection system

Big data security analytics is helping businesses discover irregularities in the security stream by analyzing the

  • Network flow
  • Logs
  • System events

As a digital product development company that has been helping other businesses create powerful cyber-security measures, we recommend you get in touch with our team. Let’s chat about the nature of your service requirement for staying ahead of cyber-security threats.
At Rapidops, we are dedicated to developing real-life problem-solving digital products for the benefit of your business.


Saptarshi Das

9+ years of expertise in content marketing, SEO, and SERP research. Creates informative, engaging content to achieve marketing goals. Empathetic approach and deep understanding of target audience needs. Expert in SEO optimization for maximum visibility. Your ideal content marketing strategist.

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