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The top technologies shaping future ‘proactive’ cybersecurity

Tue 12 Feb 2019
FYI, this story is more than a year old

There are several emerging technology trends that are set to change the way enterprises protect themselves against cybercrime, moving from passive to proactive.

With cybercrime becoming increasingly sophisticated to the point of becoming a method of warfare, Frost & Sullivan says technologies like machine learning, big data, and blockchain are becoming 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.

"Deploying big data solutions is essential for companies to expand the scope of cybersecurity solutions beyond detection and mitigation of threats," says TechVision from Frost & Sullivan research analyst Hiten Shah.

"This technology can proactively predict breaches before they happen, as well as uncover patterns from past incidents to support policy decisions."

Shah’s analysis comes from Frost & Sullivan’s recent report - Envisioning the Next-Generation Cybersecurity Practices - that looks into enterprise cybersecurity and analyses the drivers and challenges to the adoption of best practices in cybersecurity, in addition to the technologies impacting the future of cybersecurity and the main purchase factors.

"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," says Shah.

"Such collaborations will lead to mergers and acquisitions, ultimately enabling companies to provide more advanced solutions."

Shah has listed the technologies that are likely to find the most application opportunities, which include:

  • Big data: It enables automated risk management and predictive analytics. Its adoption will be mostly driven by the need to identify usage and behavioural 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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