Rapidly analyze streaming data, detect outliers in real-time
Streamline your ability to detect threats and outliers in using built-in analytics and machine learning models.
DNIF Query Language (DQL)
DQL uniquely enables you to query, correlate, validate, respond and mitigate. Easy and expert modes available to enable anyone to operate the system.
Deliver various analytics and searching capabilities using a single, comprehensive and easy-to-use DNIF Query Syntax
Native functions such as data sorting, mathematical, statistical, logical operations, workflows and subroutines, lookups and triggers
Create a wide spectrum of analytics use cases ranging from simple data presentation to visual dashboards, correlations, ML models and workflows
Intermix DQL with SQL and/or Python to leverage publicly available libraries for building analytics content
No Code Machine Learning
Implement custom ML models by choosing one of the many inbuilt ML models. Enable anomaly detection without having to write rules for every scenario.
Predefined models of supervised and unsupervised machine learning algorithms. Learn from your data to derive normal and anomalous behaviour
Intelligent suggestions for feature selection and optimization of the model to achieve 95% accuracy
Easy-to-use interface to build, test, train and validate machine learning models
Super charge threat hunting by using ad-hoc outlier detection and investigating anomalies without writing scenario based rules
Integrated Visualization and Reporting Framework
Fully customizable dashboards and reports for easy analysis, investigation and response by the SOC analysts.
Integrated framework to create custom dashboards for reports powered by the DQL
Pre-defined library of dynamic visuals for effective representation of data running in realtime on the datalake
Out-of-box reporting templates for creating scheduled and ad-hoc reports
Create drill down widgets for easy analysis and drive click based investigation and response for SOC Analysts