Forescout: AI Behavioral Anomaly Detection w/ Automated Enforcement
Description
Leverage AI/ML models to learn normal behavior patterns across endpoints, IoT, and OT devices. Identify zero-day threats, insider attacks, or lateral movement by compromised devices that are missed by point-in-time tools within the network. Use these patterns to detect anomalies in real time. When an anomaly (unusual data transfer, unauthorized protocol, lateral movement) is detected Forescout automatically enforces a response policy—such as quarantining the device, notifying SOC security teams, or triggering an orchestration workflow.
Detailed example
Response policy; quarantining the device, notifying SOC security teams, or triggering an orchestration workflow
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Benefits include real-time response to unknown threats, reduced time to detect and respond, eliminates manual correlation with siloed tools.
Controls / human review
ATO: Not reported; PIA: Not published