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THREATNEXUS GRAPH TECHNOLOGY AND CYBER ONTOLOGY

TECHNOLOGIES

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Graph Analysis
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Graph Analysis

Siemplify analyzes and maps every event reported by the different security systems across an organization into a graph. Security experts are able to analyze threats and suspicious activities in real-time while enabling rapid and accurate decision-making. Advanced machine learning algorithms run on the graph and provide insights to the security team.

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Data Fusion
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Data Fusion

Siemplify combines security data derived from disparate security systems and organization silos into one domain-based language, meaningfully defined entities and relationships.

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Big Data & Scale
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Big Data & Scale

Siemplify uses cutting edge big data technologies to provide a robust and scalable answer to data intensive challenges. Our architecture is flexible and scalable, allowing us to support any number of data sources.

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Noise Reduction
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Noise Reduction

Siemplify is able to determine which events are unimportant and repetitive, and which are unusual enough to warrant attention. During a security investigation, Siemplify will prioritize irregular actions, place them in context, and provide analysts with an intuitive storyline that highlights relevant and important data.

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Automatic Correlation Engine
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Automatic Correlation Engine

Siemplify uses a proprietary contextual clustering algorithm to correlate different incidents and place them all in one storyline. The algorithm groups events that have been logged in different systems – under different names – and draws correlations between individual alerts.

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Easy Integration And Flexible API
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Easy Integration And Flexible API

Siemplify is data-source agnostic and works with all SIEM and log management tools. With our API, businesses can quickly and easily integrate the Siemplify platform into their existing environments.

icon1.png

Graph Analysis

Siemplify analyzes and maps every event reported by the different security systems across an organization into a graph. Security experts are able to analyze threats and suspicious activities in real-time while enabling rapid and accurate decision-making. Advanced machine learning algorithms run on the graph and provide insights to the security team.
icon1.png

Data Fusion

Siemplify combines security data derived from disparate security systems and organization silos into one domain-based language, meaningfully defined entities and relationships.

icon1.png

Big Data & Scale

Siemplify uses cutting edge big data technologies to provide a robust and scalable answer to data intensive challenges. Our architecture is flexible and scalable, allowing us to support any number of data sources.

icon1.png

Noise Reduction

Siemplify is able to determine which events are unimportant and repetitive, and which are unusual enough to warrant attention. During a security investigation, Siemplify will prioritize irregular actions, place them in context, and provide analysts with an intuitive storyline that highlights relevant and important data.

icon1.png

Automatic Correlation Engine

Siemplify uses a proprietary contextual clustering algorithm to correlate different incidents and place them all in one storyline. The algorithm groups events that have been logged in different systems – under different names – and draws correlations between individual alerts.

icon1.png

Easy Integration And Flexible API

Siemplify is data-source agnostic and works with all SIEM and log management tools. With our API, businesses can quickly and easily integrate the Siemplify platform into their existing environments.

HOW IT WORKS

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START WITH DATA FROM MULTIPLE SOURCES

Siemplify seamlessly ingests all your existing data sources across the enterprise, ranging from SIEM and log repositories , through the organizational data silos and all the way to Threat Intelligence and Vulnerability information.

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FUSE DATA INTO SINGLE STORYLINE

The ingested sources then go through our modelling process. An intensive process that fuses the data together, leveraging our inherent expertise to model the different log information and related data into an unified, expressive domain-based graph representation.

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CONTEXTUALIZE

Once modelled, the constructed graph is then processed using our proprietary algorithms, resulting in comprehensive storylines and activities that effectively utilize information drawn from all ingested sources.

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INTUITIVE THREAT INVESTIGATION

The analysts are then provided with a contexualized view of the alerts, helping them evaluate otherwise complex situations and allowing them to instantly pivot between alerts, entities, and other data sources in order to rapidly locate and neutralize the different threats.