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Upwind Security
Vendor IDupwind-security
Category
CNAPP
Websiteupwind.io
Clouds
AWSAZUREGCPOCION_PREMISES_HYBRID
Agentless
Yes
Agent-Based
Yes
BYOC
No
Verified
2026-08-[DATE]T00:00:00Z
Beta Vendor Disclaimer
Vendor information is currently in beta. Information is compiled from public technical specifications; consult with the vendor for official compliance validation.
Description
Upwind Security is a runtime-powered Cloud-Native Application Protection Platform (CNAPP) built on lightweight eBPF (Extended Berkeley Packet Filter) architecture. By capturing continuous, kernel-level telemetry across processes, system calls, and network paths, Upwind cuts through alert noise and maps live cloud execution, prioritizing risks based on actual runtime reachability rather than theoretical vulnerabilities.
Key Features
Key Capabilities
- Kernel-Level eBPF Telemetry: Collects high-fidelity Layer 3–7 network traffic, file modifications, and process executions directly from the OS kernel under 1% CPU overhead.
- Threat Stories (AI-Driven Correlation): Dynamically baselines infrastructure, identity, and network patterns to cluster distributed event logs into unified, single-pane incident timelines.
- Runtime SBOM & Reachability Analysis: Validates whether vulnerable software packages, open-source libraries, or exposed APIs are actively executing in memory before alerting teams.
- Application Detection & Response (ADR): Continuously monitors live code-execution paths to capture anomalies like reverse shell executions or memory injection attacks as they unfold.
Best Fit
Ideal Deployments
- Kubernetes & Container-Heavy Environments: Exceptional for dynamic, highly ephemeral microservices where traditional snapshot-only visibility misses short-lived workloads.
- SecOps Teams Experiencing Alert Fatigue: Best fit for organizations aiming to drop low-priority vulnerability debt by filtering for active, internet-reachable execution paths.
- GenAI & LLM Workload Footprints: Tailored for environments tracking real-time AI security risks, mapping GPU infrastructure metrics, prompt data flows, and external AI model endpoints.