Enterprises can use big data & AI cybersecurity solutions to proactively prevent breaches says Frost & Sullivan

11 February 2019

Frost & Sullivan cybersecurity

AI & machine learning can aid early detection of anomalies, while blockchain creates trustworthy networks between endpoints, report Frost & Sullivan.

As cybercrime becomes increasingly sophisticated – even being deployed as a method of warfare – technologies such as artificial intelligence (AI) and machine learning, big data and blockchain have the potential to aid cybersecurity efforts. The rise of the Internet of Things (IoT) continues to open up numerous points of vulnerabilities, driving 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,” 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 analyses the drivers and challenges to best practice adoption in cybersecurity. It also covers the technologies impacting the future of cybersecurity and key purchase factors.

"Startups need to make their products capable of being integrated with existing products and solutions, as well as bundle them 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 finding the most application opportunities include:

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


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