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Importance of Big Data Analytics in Cybersecurity Strategy Confirmed

The weight big data analytics carries in developing a strong cybersecurity strategy can no longer be ignored, confirms research data from the Ponemon Institute. The institute has released its first analysis of how organizations can incorporate big data analytics into their strategy and best practices.

After interviewing 592 IT and IT security specialists from various industries in the United States, researchers found 72 percent have already used big data to detect cyber threats, while 65 percent feel big data is vital to establish and consolidate strong cybersecurity strategies inside their organizations. However, 72 percent are not confident that big data analytics can be used with traditional technologies to prevent advanced cyber threats.

“The data pouring into enterprise environments holds valuable information that can be used to identify and mitigate threats, but in many cases it is simply too overwhelming to extrapolate anything useful from,” said Dr. Larry Ponemon, Chairman and Founder of the Ponemon Institute. “Not only does this first-of-its-kind report show that organizations know they have the information needed to stand up against advanced threats, it also reveals that when data is used in conjunction with the right analytic tools that it can be used to reduce risk across their organizations.”

Respondents have also ranked user behavior analytics and machine learning as up-and-coming technologies, while early adopters have noticed greater accuracy in the detection of malware, compromised devices and zero-day attacks.

About the author


From a young age, Luana knew she wanted to become a writer. After having addressed topics such as NFC, startups, and tech innovation, she has now shifted focus to internet security, with a keen interest in smart homes and IoT threats. Luana is a supporter of women in tech and has a passion for entrepreneurship, technology, and startup culture.

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  • Big Data analytics can be leveraged to improve information security and situational awareness. For example, Big Data analytics can be employed to analyze financial transactions, log files, and network traffic to identify anomalies and suspicious activities, and to correlate multiple sources of information into a coherent view.

    Ultimately Big Data analytics for security is to obtain actionable intelligence in real time. I think in coming time Big Data analytics have some significant promise.