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Preventing data breaches with predictive threat models

Varonis Systems : 09 November, 2015  (New Product)
Metadata Framework platform enhanced with new set of predictive threat models to provide greater ability to prevent data breaches
Preventing data breaches with predictive threat models

Varonis Systems has announced the beta availability of an unprecedented set of predictive threat models that can be used to analyse and detect everything from insider threats and outsider attacks to CryptoLocker infections and suspicious behaviour.

Version 6.2.5 of the Metadata Framework platform includes major enhancements to Varonis DatAlert, one of the most widely used solutions for monitoring unstructured data – which is typically the largest, most valuable and most sensitive type of data and is therefore the target of most attacks. The new capabilities represent the most advanced analytics and predictive threat models available to help organisations stem the rising tide of data breaches that are caused or exacerbated by previously undetected insider access and behavior, such as compromise of privileged accounts and privilege abuse.

The new capabilities in Varonis Metadata Framework version 6.2.5 include:

* Dozens of new automated threat models for Varonis DatAlert to provide advanced behavioural alerts on access anomalies and suspicious activity
* Enhanced ability for Varonis DatAdvantage to analyse metadata and automatically discover critical assets, noteworthy people and normal activity, establishing a baseline of behavior
* The ability for Varonis DatAdvantage to more accurately identify unusual behavior with machine learning algorithms and behavioural threat models

Enovos, an energy supplier for multiple countries in Europe, takes its responsibilities very seriously when it comes to protecting its data. Varonis’ new analysis capabilities are helping Enovos enhance data security by analysing past user activity, building additional behaviour baselines and spotting more unusual activity. “This is a welcome advance, and will certainly help us detect future insider threats more quickly,” the company said.

Doug Cahill, Senior Analyst at Enterprise Strategy Group, said, “One of the challenges organisations face when they deploy various kinds of security sensors is that the sheer volume of meaningless alerts and false positives often forces them to cry uncle and turn off the alerts, reserving them for use only in known threat situations and eliminating their proactive effects. By analysing months of activity, Varonis can help companies define the baseline for normal user activity and then deploy the new automated threat models for contextual and thus meaningful and actionable alerts. Such behavioural-based anomaly detection is a real advance in the burgeoning field of user behaviour analytics.”

Yaki Faitelson, Varonis Co-Founder and CEO, said, “By now it is understood that protecting the perimeter of your network can no longer be the primary focus of a comprehensive security strategy. Organisations must gather, analyse and act on real-time knowledge of the relationships between users and data. Your analysis can only be as good as the metadata you’re analysing, and Varonis has more and better metadata than any other company that deals with unstructured data. We have been

collecting and analysing metadata for nearly 4,000 customers over the past decade. Our newest innovations provide a level of automation that has never been available, including pre-built threat models against advanced behavioral baselines. We will continue to create new threat models based on user behavior and add to our inside-out security capabilities.

Faitelson continued, “It is clear that organisations cannot reverse or even slow the trend of cybersecurity disasters – which threaten their ability to conduct business – without having Varonis as a core component of their infrastructure."

With new threat models based on user behaviour, Varonis will bring unprecedented insight and equip organisations with the ability to detect and prevent potentially devastating data breaches. Until now, Varonis DatAlert allowed IT and security administrators to flag suspicious activity based on static conditions. The new Varonis 6.2.5 expands this capability by including pre-built threat models and adaptive baselines that evolve over time.  Varonis UBA threat models can perform much deeper analytics on the metadata that its solutions have already been capturing in a decade of deployment. The accuracy enabled by such threat models will lead to more operationally efficient detection and allow for greater visibility, as more sensors, behaviours, and the events they generate can be used in combatting threats.

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