When ORDR introduced ML-driven device intelligence in 2018, it challenged how organizations thought about asset discovery. Customers were intrigued—curious, even hopeful—but not yet fully confident. At the same time, competitors who lacked the technology or vision worked to slow adoption rather than move the industry forward.
Asset discovery forms the foundation of any effective cybersecurity strategy. Without visibility into what devices exist on your network—especially unmanaged and shadow IT devices—security teams operate blind. Traditional discovery methods like network scanning and SNMP polling provide incomplete pictures, missing devices that don't respond to standard protocols or that hide in air-gapped operational technology environments.
Machine learning transforms asset discovery by analyzing network traffic patterns, device behavior, and communication protocols to identify devices that conventional tools overlook. ORDR's ML and AI approach continuously learns from network activity, distinguishing between legitimate devices and anomalies without requiring manual configuration or constant rule updates. This enables organizations to maintain an accurate, up-to-date inventory across enterprise, IoT, OT, and medical device environments.
Proactive security depends on knowing what you're protecting. Once asset discovery reveals unknown devices, security teams can assess risk, apply appropriate segmentation policies, and prioritize remediation efforts. Devices that would have remained invisible—potentially serving as entry points for attackers—become manageable assets with clear security profiles and compliance requirements.