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thatDot Novelty streaming anomaly detection has a built in pattern learning AI that can find the anomalous events in your stream, without training.

Find Stolen Credentials Use in AWS CloudTrail Logs using Quine Graph

The move to the cloud represents new challenges for enterprise security teams. Use thatDot Novelty Detector to detect the attack quickly.

Dynamic Duo: Quine & Novelty Detector for Insider Threats

In a big update to the VAST Advanced Persistent Threat blog, we demonstrate an end-to-end Quine plus Novelty Detector workflow.

Using Indicators of Behavior (IoB) Analysis for IoT data

Indicator of Behavior (IoB) analysis is extending beyond the cybersecurity domain to offer new value for finance, ecommerce, and especially IoT use cases.

Categorical Data: An Untapped Source of Real-Time Insights

Categorical data is an oft-ignored source of valuable business intelligence. Quine makes it easy to process categorical data with your existing ETL pipeline.

The Future of Modern Threat Hunting is Streaming Graph

Standards-based threat detection & automated response using Quine streaming graph.

Categorical data-driven apps made easy with Meroxa + thatDot

Learn how Meroxa’s Turbine makes it easy to connect data sources to Novelty Detector to create real-time anomaly detection with categorical data.

What’s the difference between Categorical and Numerical Data?

Categorical data is enormously useful but often discarded because, unlike numerical data, there were few tools available to work with it until graph DBs and streaming graph came along.

Real-time Blockchain Monitoring is Hard without A Streaming Graph

As crypto currencies go mainstream, better techniques for protecting users from fraud are needed. Enter streaming graph.

AWS Names thatDot’s Novelty Detector As A Containers Anywhere Partner

Bringing cloud-based data management into the enterprise data center, where much enterprise data still lives, is now simpler than ever.