Ordr

The strongest AI centrality case in the cybersecurity lane, and graded above Asimily deliberately. The machine learning does the primary work rather than sitting on top of a lookup: behavioural fingerprinting trained on a stated 100 million-plus real-world devices performs classification, the system learns a behavioural baseline of normal communication per device, and segmentation policies are GENERATED from that learned behaviour rather than authored from templates.

A customer account describes the platform learning normal behaviour and then automatically generating VLAN, ACL and firewall policies for engineers to review, attributing this to ML brought in by co-founder Sheausong Yang. The company states AI is a foundation built over a decade rather than a feature added recently, and that models are built on real device behaviour rather than synthetic data. Remove the models and there is no policy generation, no baseline and no anomaly detection.

The best autonomy design in this lane, and directly responsive to the patient safety risk that defines it.

Policies are generated automatically but simulated and validated before enforcement, with the company describing a view showing every device group, allowed flow and policy gap, and the ability to simulate enforcement impact and see the blast radius before any rule changes. The customer account describes generated policies being presented for engineers to review and execute.

That is the correct architecture where automatically isolating a misidentified infusion pump is a patient safety event rather than an outage: automate the analysis and the policy authoring, keep a human approval gate before enforcement, and make the consequence visible in advance.

Ordr also differentiates from Armis and Claroty on this axis by combining intelligence and enforcement in one platform rather than requiring separate tooling, which is a claim buyers should verify but a coherent design position.

More specific than most security vendors. Training scale is quantified at over 100 million real-world devices, the input is named as proprietary device languages from thousands of manufacturers, the method is passive network traffic analysis with behavioural baselining, and the company states explicitly that models are built on real device behaviour rather than synthetic data.

Graded B rather than A because no accuracy, false positive or device classification precision figures were located, and the index's category editorial specifically requires named detection methodologies rather than marketing language, which Ordr partially but not fully satisfies.

The corpus is quantified and characterised more precisely than most vendors manage, and no party in the chain is named. Training scale is stated at more than one hundred million real world devices, the input is described as proprietary device languages drawn from thousands of manufacturers, the method is passive network traffic analysis with behavioural baselining, and the company states explicitly that models are built on real device behaviour rather than synthetic data.

That last statement is worth crediting because it is falsifiable in principle and because synthetic training is a common and rarely disclosed shortcut in device classification, where real traffic from rare equipment is hard to obtain. On enumeration there is nothing: no model or model family, no hosting arrangement and no sub processor list was located, and no statement of whether customer environments contribute to the corpus, which matters given that the corpus is the product.

The same residual applies as to peers using this architecture. Passive analysis of clinical network traffic means the platform observes protocol payloads that can carry protected health information, particularly from imaging and monitoring devices, and nothing published states how incidental content of that kind is handled or whether any of it leaves the customer environment. Ask whether customer traffic feeds the corpus, what is retained from observed payloads, and for a sub processor list.

Named customer deployments with stated outcomes, which is better than most in this lane, though not independently audited. Healthcare implementations cited at Dayton Children's Hospital and Freeman Health System with documented risk reduction, plus reported protection of nearly two million connected devices across more than 500 organisations.

Deployment claims are unusually specific and falsifiable: initial discovery completing within 48 to 72 hours and segmentation enforcement in weeks rather than months or years. Third party reference points exist including a KLAS 2026 Healthcare IoT Security score reported at 89.4, placing it credibly but below Asimily at 96.6.

Graded B rather than A because outcome claims are vendor-reported without published methodology, and because a portion of the favourable comparative material originates from Ordr's own ranking content.

Structurally favourable for the same reason as Asimily: passive network traffic analysis with no agents and no active scanning means the platform observes device behaviour and communication metadata rather than ingesting clinical content, and it explicitly protects devices that cannot run security agents at all. Avoiding active scanning matters clinically, since scanning has historically crashed legacy medical devices. Graded B rather than A because clinical network traffic can carry PHI in protocol payloads and no published statement on handling incidental PHI was located.

HIPAA is addressed as a product capability, with continuous posture monitoring and automated evidence collection aligned to NIST, CIS, HIPAA and PCI frameworks to maintain audit readiness. Graded B rather than A because that is customer compliance tooling rather than a published statement of the vendor's own posture, and no BAA terms were located.

A clear and honestly stated position, though narrower than the strongest in this category. SOC 2 Type II is held with the type specified, supported by a trust center, a dedicated page explaining the SOC 2 programme, and a published security policy, alongside stated GDPR and CCPA compliance. One detail deserves particular credit: the company states plainly that ISO 27001 certification is under evaluation and not yet completed.

Publishing what is not yet held is uncommon and is the opposite of the more usual practice of naming a standard without saying whether certification was achieved. Held below the top of the band on breadth. There is no ISO 27001, no HITRUST, no federal authorisation, and no penetration test report or software bill of materials published, where the leading vendors in this category carry eight to fifteen compliance programmes. Worth requesting the SOC 2 Type II report and asking where ISO 27001 sits in the timeline.

This is a security platform rather than a regulated device, so no FDA clearance applies and none is needed. Against the frameworks that do govern procurement, the coverage is broad: dedicated compliance and regulatory framework resources, continuous compliance monitoring with audit ready reporting, and named support spanning HIPAA, PCI DSS and FERPA in healthcare and education, and SOX, GLBA and DORA in financial services. HIPAA support is asserted directly for healthcare deployments.

Held below the top of the band because the breadth is horizontal rather than deep in this sector. The medical device regulatory frame specifically is unaddressed: there is no FDA recall handling, no reference to the IEC 80001 risk framework for medical device networks, no ingestion of manufacturer security disclosure statements, and no mapping to federal medical device cybersecurity requirements or health sector performance goals. A competitor in this category covers all four. Worth asking how recall notices and device manufacturer advisories are handled operationally.

One of only two vendors in this category to address AI governance as a distinct subject rather than leaving it implicit. A dedicated resource on generative AI use is published, and the oversight model is stated explicitly rather than implied: the system investigates, explains and recommends while the operator approves, with actions described as governed, traceable and controlled.

That matters here because the platform enforces segmentation policy, and policies are validated before enforcement rather than applied directly. An accuracy figure of 99.8 percent is published, which is more than most competitors offer at all. Held below the top of the band because the supporting rigour is absent.

No methodology, scope or measurement period accompanies the accuracy claim, no false positive or false negative rates are given despite reduced false positives being a stated benefit, and there is no AI management system certification. The platform is also described as trained on behaviour across more than a hundred million device profiles, with nothing published on whose environments contributed or whether a customer can be excluded. Worth asking all three.

Two passes located no accuracy figure, no false positive rate, no device classification precision and no warranty, indemnity or remediation commitment. Classification precision is the number that matters most and its absence has a compounding consequence this index has recorded on a peer: device identification feeds the risk rating, and the risk rating feeds the segmentation or policy decision, so a device typed wrongly at the first step produces a wrong rating and then a wrong network policy, with each stage inheriting the error while appearing to be an independent judgement.

Both error directions are consequential in a hospital. A clinical device typed as low risk and treated permissively is an opening nobody is watching, and one typed as high risk and isolated can lose the connectivity it needs to function, which is a patient safety event arising from a security control.

One design decision deserves credit and belongs to safety rather than to this axis: the platform avoids active scanning entirely, which matters clinically because scanning has historically crashed legacy medical devices, and choosing a method that cannot do that is a real protection rather than a policy about it.

Ask for classification precision by device class, the false positive rate at the recommended configuration, and what proportion of devices are typed by inference rather than by a definitive identifier.

Correctly targeted at the systems that matter in this category and explicitly additive rather than replacing. The company states it enhances existing security infrastructure by integrating with firewalls, NAC, SIEM and other tools already in place, and the ranking criteria in this lane weight integration with CMMS, CMDB and SIEM specifically.

Ordr automatically enriches the customer's CMDB with behaviour-based device intelligence, which is the practical difference between a security inventory and an authoritative asset record the whole organisation trusts. It also identifies devices lacking EDR or MDM coverage, closing the gap between the security tool estate and reality. Ordr IQ extends access beyond security teams, letting a facilities manager query maintenance schedules or IT pull end-of-life device lists from the same verified data.

Passive and agentless with no active scanning, which is the safest deployment posture available in clinical environments and is essential for the legacy and unmanaged devices that constitute much of the risk. Deployment timelines are stated and falsifiable: initial discovery within 48 to 72 hours, segmentation enforcement in weeks rather than the multi-year projects that characterise this work.

The company also states policies are continuously refined as device behaviour evolves rather than being a one-time configuration. No data residency disclosure located, which is the only qualification on an otherwise strong axis.

No pricing published and no pricing basis disclosed, consistent with the rest of this lane. Given the platform combines discovery, risk prioritisation and segmentation enforcement in one product where competitors require additional tooling, the total cost comparison is genuinely difficult for buyers to construct from public information, and the vendor does not assist with it.

Broad across asset classes and industries while retaining genuine healthcare depth. Covers IT, IoT, OT, clinical and cloud assets in one platform, with healthcare-specific attention to connected medical equipment, IV pumps and imaging systems that cannot run agents. Serves more than 500 organisations spanning healthcare, banking, manufacturing, government and critical infrastructure.

Functional span runs from discovery and inventory through vulnerability prioritisation, threat detection, segmentation policy generation and enforcement, to compliance evidence collection, which is a wider single-platform footprint than most competitors that stop at visibility.