Big data security analysis: how it works

Big data security

Currently, there are more than 26 billion IoT devices connected to the Internet. These have created larger cracks that increase the risk of cyber attack, a threat that can be controlled by big data security analysis.

Business data is very important for any organization, but this overwhelming reliance on information to make important decisions also requires better protection. Organizations must implement strategies that allow even the most sophisticated attacks to be thwarted.

To combat the growing number of cyber attacks, big data security analysis is showing its great potential and organizations are betting more and more on this protection.

What is big data security analysis?

Security big data analysis is the process of evaluating large data sets with the objective of discovering hidden patterns, complex correlations, market trends, consumer preferences and other useful information to detect any evidence that may lead to prevention of an attack.

Organizations have finally reached a point where they take advantage of intelligent algorithms and large-scale data processing to protect business data.

The big data security analysis:

  • It uses statistical and advanced data science models for fast and efficient detection. At the same time, a real-time flow processing allows a quick analysis of threats.
  • It is based on tools that operate in real time and generate a small amount of security alerts according to their level of severity.
  • These alerts are complemented by additional forensic details for rapid detection and mitigation of cyber-violations.

Big data security

How can big data security analysis be used to protect the business?

To address cybersecurity threats, big data security analysis works in four directions:

Analysis of historical data.

Historical data can be used to predict attacks since the impact on costs due to worms, viruses or other malicious programs is significant. With the help of advanced analysis, companies can create statistical baselines to discover what is considered “normal”, which in turn will help them determine the deviation of the data. This risk assessment, together with a quantitative prediction of vulnerability to attack, can help organizations develop counterattack measures. Historical data can also be useful for developing predictive models, statistical models and AI-based algorithms.

Monitoring and automation of workflows.

The big data security analysis can play a vital role in the supervision and automation of workflows. That means that it can also serve as a valuable tool for the employee system monitoring program, as there are many cases of security threats due to employee-related violations. Automation helps to quickly respond to detected threats, for example, avoiding clearly recognized malware attacks.

Deployment of the intrusion detection system.

With the help of network flows, registers and system events, big data security analysis can identify anomalies and suspicious activities. Since firewalls, data encryption and multifactor authentication are common security techniques, the intrusion detection system is very powerful due to the increasing sophistication of cyber-infractions.

Analytical functionalities optimized by automatic learning.

Advanced algorithms allow a computer to display the behavior learned from past experiences, rather than human interaction. By collecting, analyzing and processing data based on previous cyber attacks, machine learning can allow companies to develop specific defense responses. Thus, organizations can use these capabilities to detect vulnerabilities, identify a violation as it occurs, and correlate information from multiple sources.

Real-time data processing for threat analysis in real time.

As soon as they are available, the data can be analyzed, helping to assess possible risks before they occur. Real-time data processing is connected to external data sources, which allows companies to integrate valuable data into the application flow and can be beneficial in scenarios where new and dynamic data are generated continuously.

In this technology-driven world, the risk of cybersecurity is growing with a huge proportion due to the greater sophistication of cyber attack techniques. Companies need equally powerful cybersecurity measures to protect their data. And all of them increase their effectiveness when the time factor is in favor. We should give thank to big data security analysis.

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