AI EdgeLabs has enhanced its autonomous AI runtime protection platform with the launch of a new Compliance Center and Linux Audit capabilities. Designed to help organizations meet stringent regulations like NIS2 and the Cyber Resilience Act (CRA), the new features replace manual reporting with continuous visibility, automated control coverage, and real-time compliance posture insights. The update extends the platform's ability to secure regulated AI workloads, GPU clusters, and edge environments with deeper runtime-layer protection.
AI EdgeLabs adds a Compliance Center and Linux Audit to its AI runtime platform.
The features target continuous compliance for NIS2, CRA, and critical infrastructure mandates.
Provides a single risk score, real-time compliance visibility, and prioritized asset insights.
Extends support to firmware, custom OS, and real-time operating systems (RTOS).
Automates framework-specific checklists and incident reporting.
Aims to replace manual, scan-based compliance with automated, runtime evidence.
The launch directly addresses the operational burden of proving compliance in complex, distributed AI and edge environments. CEO Inna Ushakova highlighted the shift from manual processes to continuous proof: “This release provides real-time compliance posture, instant detection of vulnerabilities and misconfigurations... It finally gives teams the operational evidence regulators require and the autonomy distributed AI environments demand.” The Compliance Center is designed to deliver automated mapping, proof, and reporting aligned with frameworks like NIS2 and CRA.
The new capabilities offer a unified view of security and compliance health. Organizations gain a single Risk Score representing their current posture, real-time compliance visibility specific to regulations, and prioritized asset lists based on impact and criticality. The Linux Audit functionality provides configuration hardening insights, while extended support covers firmware, custom OS, and RTOS environments. The system also automates framework-specific checklists and integrates incident reporting with automated response.
CTO Oleg Mygryn explained the architectural intent behind the update: to move beyond traditional, noisy security reporting. By integrating custom OS verification with Software Bill of Materials (SBOM)-based vulnerability detection, the platform provides a granular, context-aware view of risk. “Whether it’s a Linux misconfiguration or a firmware issue on a critical GPU node, our AI engine identifies the context and criticality, allowing teams to address the assets that matter most before they can be exploited,” said Mygryn.
With this release, AI EdgeLabs is deepening its focus on the unique security and compliance challenges of AI infrastructure. By embedding continuous compliance and risk assessment directly into the runtime protection layer, the platform aims to provide the automated evidence and prioritized remediation that security teams need to secure regulated AI workloads at scale and speed.
About AI EdgeLabs
AI EdgeLabs is an AI-native, autonomous runtime protection platform for regulated AI workloads, GPU clusters, edge, hybrid cloud, and sovereign environments. Built to secure regulated AI workloads and infrastructure in real-time, AI EdgeLabs delivers threat detection and response capabilities where CNAPPs can’t reach, ensuring the integrity of distributed infrastructure across the globe.
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