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AI Engine

Battle Tested, Purpose-built, AI

Go beyond alerts – detect and respond to Incidents. Industry leading Machine Learning (ML) algorithms detect threats in the enterprise. Stellar Cyber’s AI engine is like a team of world class security experts working around the clock at massive scale to make your team faster and more effective.
AI That Delivers Results
The output of Stellar Cyber’s AI Engine can be simplified down to generating two types of data for security teams: alerts and incidence incidents . Together,alerts and incidents provide the depth and holistic view teams need to make rapid decisions

Novel Alerts

Alerts are instances of specific suspicious or high risk behavior and are the building blocks of Incidents. Stellar Cyber ships with 200+ Alert Types out of the box; no configuration required. Alert Types are mapped to the XDR Kill Chain , to enable prioritization and correlation. Individual Alerts have a generated, human readable description of what happened, and recommended remediation for fast response.

Example alert types include:

NDR platform
NDR platform

Automatically Correlated Incidents

Incidents are correlated sets of Alerts and other supporting data including signals, assets, users and processes. Incidents represent an entire attack or sequence of high risk actions. In real time, as new Alerts are generated, Alerts are assigned to relevant Incidents so that attacks can be detected and responded to before completion. Incidents in Stellar Cyber are mutable, meaning they can get updated, and are not limited to any certain time window so they can pick up complex attacks.

Real-world incidents detected in Stellar Cyber:

How Stellar Cyber’s AI Engine Works

SIEM replacement

Key Features

AlienVault alternatives


Alert Fatigue is a serious problem. Not every anomaly is a security incident. security analyst should stop sifting through countless anomalies and focus on the real threats. Core to Open XDR , Stellar Cyber’s AI Engine leverages state-of-the-art machine learning algorithms to implement the best accuracy for detection.

It analyzes time series and peer groups with unsupervised learning, performs complex behavior analysis through modeling relationships with Graph ML, and generalizes known attack patterns with supervised learning. It also correlates and builds context with advanced Graph ML, so that we can present the high priority attacks with rich context.

Real Time

It may take minutes for hackers to infiltrate your system and steal valuable information. You need virtual security experts to continuously work around the clock and detect threats in real time. Stellar Cyber’s AI Engine performs ML inference in real time and provides detailed reasons for its output.

cloud detection and response


Our single advanced AI Engine powers Stellar Cyber’s Open XDR technology and works on various data sources after normalization regardless of data types such as logs or network traffic.

When a new data source is ingested, all existing detections will be directly applied. For example, our machine learning can perform user behavior analysis based on behavior data from different data sources. Machine learning inference is natively embedded in our data processing pipeline without the need to send data outside.
Endpoint detection and response tools


Stellar Cyber goes wherever you need it to go – on-premise, in the cloud or hybrid. Multi-tenancy is built in from the beginning to ensure flexible, secure operations for any organization. Multi-site allows data to stay resident in its own region to be compliant and scalable in complex operating environments.

Extended detection and response

Explainable And Actionable

The ultimate goal for detection is to take action to stop attacks and to keep your environment safe. Action-taking is a serious decision; security analyst need to fully understand the situation in order to make an informed decision regarding what is the best action to take. With the latest explainable AI, instead of being a black box, the AI Engine provides human-friendly evidence and easy-to-digest-details from ML models to ease decision-making. With that, a serious decision; security analyst can easily understand the reasons and evidence for any detection in order to block an attack with high confidence without mistakenly interrupting protected users or applications.

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