Claroty vs Ordr
The deployed standard against the enforcement engine. Claroty is the scale incumbent: its Medigate derived platform reports 20 million plus devices across 2,000 plus hospital facilities with a reported 500 plus protocols, named Best in KLAS across multiple years. Ordr is built around the specific failure Claroty's scale does not by itself solve, that segmentation projects stall because teams do not trust their asset data enough to enforce policy. Ordr generates segmentation policy from behaviour learned across a stated 100 million plus devices, earning an A on AI centrality against Claroty's B, and its autonomy design is the best in the lane, simulating enforcement and showing the blast radius before any rule changes with a human gate. Claroty's record here is thinner on those axes, graded on fewer of them. If your failure mode is the largest proven install base and protocol breadth, start with Claroty. If your failure mode is turning asset visibility into enforced segmentation without a multi year project, start with Ordr.
- The largest proven footprint in the lane: a reported 20 million plus devices across 2,000 plus hospital facilities, with discovery across a reported 500 plus protocols through the Medigate derived platform.
- A long independent track record: named Best in KLAS for Healthcare IoT Security across multiple years, where Ordr's reported KLAS score in the 2026 report is 89.4.
- Purpose built healthcare device security graded A on setting coverage, spanning IoMT, IoT, and building management systems with clinical context added to threat prioritisation.
- The strongest AI centrality case in the lane: behavioural fingerprinting on a stated 100 million plus devices generates segmentation policy from learned behaviour rather than templates, graded A against Claroty at B, where the models do the primary work.
- The best autonomy design in the category: policies are simulated and the blast radius shown before enforcement with a human approval gate, the design this index credits most because automatically isolating a misidentified clinical device is a patient safety event.
- Falsifiable deployment that closes the gap between knowing and acting: initial discovery within 48 to 72 hours and segmentation enforcement in weeks rather than the multi year projects this work usually becomes.
Side-by-Side
| Axis | C Claroty |
O Ordr |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | — | |
| Model and Technology Transparency | — | |
| Clinical and Operational Evidence | ||
| AI Safety and PHI Stewardship | — | |
| HIPAA and BAA Posture | — | |
| Security Certifications and Trust Center | — | |
| FDA and Regulatory Status | — | |
| AI Governance and Bias Disclosure | — | |
| EHR and Interoperability Depth | — | |
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Setting and Specialty Coverage |
Claroty grades B on AI centrality against Ordr's A, and a structural caveat matters for reading the rest: Claroty's record on this index is less fully assessed, carrying graded axes on AI centrality, setting, clinical evidence, and deployment but not on autonomy, model transparency, or interoperability, so several axes where Ordr is graded show as not rated for Claroty rather than as an absence of capability. Ordr's autonomy grade of A rests on documented enforcement simulation and a human gate. A source caution on Ordr: it publishes its own comparative rankings of competing platforms, so favourable comparative material from the vendor is self interested and this index relies on it only for Ordr's own product claims. Device and facility counts are vendor reported. Neither publishes a security attestation or pricing.