Unified Visibility: Spyderbat uses lightweight eBPF nano-agents that deploy consistently across different environments—AWS, Azure, GCP, on-premisedata centers, VMs, and Kubernetes/OpenShift clusters. This creates a single source of truth for all workload activity, regardless of where it runs.

Automated Causal Analysis (Spydertraces): The platform automatically maps raw data streams into a single, visual "Behavioral Context Web" (Spydertraces). This instantly connects multi-stage attack activities that might span from a cloud control plane misconfiguration to an on-premiseserver, providing immediate root cause analysis.

Behavioral Detection: Spyderbat's AI engine learns normal behavior and detects "application drift" or anomalies in real-time, providing high-fidelity alerts that work consistently across all environments, reducing alert fatigue.

Surgical, Automated Response: The platform enables automated responses (killing processes, terminating pods, or triggering cloud-native functions via APIs/webhooks) that are precise to the detected threat's causal chain, ensuring minimal disruption across complex hybrid environments.

Spyderbat provides a single pane of glass and automated context that allows SecOps teams to detect and respond to threats across any combination of cloud or physical infrastructure as if they were a single environment.