AI EdgeLabs Vulnerability Detection is a continuous, AI-driven vulnerability management solution designed for distributed and remote environments, including edge, cloud, and OT/IoT deployments. It uses a lightweight SBOM-based asset collection approach to minimize bandwidth impact while enabling broad scanning coverage across hosts, container images, and firmware. Key functional areas include: **Host Vulnerability Detection:** Scans hosts using full SBOM data format support, continuously identifies active runtime assets, and applies exploitation scoring to verify real-world exploitability. Vulnerabilities are prioritized based on host exposure and exploitability. **Container Vulnerability Detection:** Supports Docker, Kubernetes, Podman, and ContainerD runtimes. Performs layered analysis at image, layer, and package levels, validates base OS vulnerabilities, and works with device-local images without external dependencies. **Risk Estimation and Remediation:** Provides real-time exploitability scoring based on asset criticality, generates prioritized remediation playbooks, and produces audit-ready compliance documentation for frameworks such as CRA and NIS2. **Firmware Vulnerability Detection:** Supports firmware from Windows, Linux, RTOS, and embedded systems. Ingests firmware SBOMs to detect vulnerabilities in libraries and dependencies. Vulnerability databases are updated multiple times per day to reflect the latest CVEs. SBOMs can be analyzed either in the cloud or locally to support sovereign or offline environments. Supported operating systems include Ubuntu, RHEL, CentOS, Debian, Alpine, and Amazon Linux.