Use of Big Data Technology in Cybersecurity Solutions to Help Enterprises Proactively Prevent Breaches

Machine learning aids early detection of anomalies, while blockchain creates a trustworthy network between endpoints, finds Frost & Sullivan

As cybercrime becomes more sophisticated and even a method of warfare, technologies such as machine learning, Big Data, and blockchain will become prominent. The rise of the Internet of Things (IoT) has opened up numerous points of vulnerabilities, compelling cybersecurity companies, especially startups, to develop innovative solutions to protect enterprises from emerging threats.

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“Deploying Big Data solutions is essential for companies to expand the scope of cybersecurity solutions beyond detection and mitigation of threats,” said Hiten Shah, Research Analyst, TechVision. “This technology can proactively predict breaches before they happen, as well as uncover patterns from past incidents to support policy decisions.”

Frost & Sullivan‘s recent analysis, Envisioning the Next-Generation Cybersecurity Practices, presents an overview of cybersecurity in enterprises and analyzes the drivers and challenges to the adoption of best practices in cybersecurity. It also covers the technologies impacting the future of cybersecurity and the main purchase factors.

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“Startups need to make their products integrable with existing products and solutions as well as bundle their solutions with market-leading solutions from well-established companies,” noted Shah. “Such collaborations will lead to mergers and acquisitions, ultimately enabling companies to provide more advanced solutions.”

Technologies that are likely to find the most application opportunities include:

• Big Data: It enables automated risk management and predictive analytics. Its adoption will be mostly driven by the need to identify usage and behavioral patterns to help security operations spot anomalies.
• Machine Learning: It allows security teams to prioritize corrective actions and automate real-time analysis of multiple variables. Using the vast pools of data collected by companies, machine learning algorithms can zero in on the root cause of the attack and fix detected anomalies in the network.
• Blockchain: The data stored on blockchain cannot be manipulated or erased by design. The tractability of activities performed on blockchain is integral to establishing a trustworthy network between endpoints. Furthermore, the decentralized nature of blockchain greatly increases the cost of breaching blockchain-based networks, which discourages hackers.

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