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Cybels Analytics Cybersecurity Platform to be Introduced by Thales Group

January 26, 2020

During the 2020 edition of the International Cybersecurity Forum (FIC), Thales is unveiling Cybels Analytics, a cybersecurity platform that relies on advanced AI and Big Data analytics technologies.

The platform provides faster, sharper, more exhaustive detection of the most complex attacks in real time or proactively in differed time (hunting). It meets the needs of the most demanding customers, operating as a single, simple platform that even allows individual users to adapt the AI algorithms to the specific operational context of each sector.

The cyberattack techniques that have emerged in recent years are increasingly complex and hard to detect. Despite growing awareness on the part of organisations, and frequent deployments of rules-based detection systems designed around well-known attack patterns, cybersecurity analysts also need to detect previously unknown threats, detect attacks more quickly and save time in conducting investigation analysis once a system has been compromised.

Thales has developed Cybels Analytics, a comprehensive and advanced attack detection solution, to meet these needs. The innovative platform combines real-time threat detection based on analysis of existing threats (Cyber Threat Intelligence) and proactive search for advanced and unprecedented cyberattacks ("cold" investigation or Hunting), thanks to artificial intelligence and graphic visualization modules. These capabilities significantly reduce the time taken to detect advanced persistent threats from three months in average to just a few days, according to test results.

Cybels Analytics uses machine learning algorithms developed by Thales to detect abnormal situations based on huge volumes of heterogeneous data from multiple sources (network data, end point analysis, OT logging, etc.), helping to identify attack patterns and discover previously unknown threats. These algorithms, based on the principles of Thales TrUE AI, can be tailored to the specific needs of each business sector of activity by customers themselves via an easy-to-use graphical interface.

Cybels Analytics can be integrated with an on-premise Security Operations Centre (SOC) or provided as a service in the cloud, enabling all the user's detection systems (SIEM, EDR, NIDS, etc.) to work together and complement one another. The platform is an important addition to Thales's cybersecurity offering, rounding out the range of managed services provided through its SOC network and supporting the Cybels Sensor trusted probe, which is accredited by France's National Agency for Information System Security (ANSSI). Cybels Analytics is also connected to the Thales Cyber Threat Intelligence service. By cross-referencing information about existing cyberthreats with an organization's system logs, Cybels Analytics ensures more acute, more exhaustive detection of untargeted attacks, revealing three times more indicators of compromise detection than conventional attack detection products.

With the entire threat detection ecosystem integrated on the same platform, Cybels Analytics improves the customer's detection capabilities while simplifying the process for users. In addition, to tailor the platform to the specific environment of each sector of activity, powerful data visualization modules enable users to run their own searches easily, identify any anomalies at a glance and save precious time at the investigation analysis stage. While it often takes weeks to build a complete picture of an organization's information system using standard investigation products, Cybels Analytics shortens this process to just a few hours.

Thales's Cybels Analytics platform is a powerful log analytics solution that automatically learns from experience, allowing enterprise and government customers to detect more anomalies faster and better, and continuously improving the operational efficiency of their efforts to detect new threats and tailor their cyber defenses to their specific operating environments.

Filed Under: Data Processing & AI/ML, Space Systems Software Engineering

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