Comparisons
Last reviewed August 19, 2026 · Mitchell Sipus, PhD, Founder & CEO
Most of this market is either probabilistic at the detection layer, asset-centric rather than behavior-centric, or dependent on content inspection that encryption is steadily closing off. CyPhER is deterministic, behavior-centric, and delivered as Fusion for log analysis, Rx for real time traffic analysis, Edge for the switch, and OT for operational technology equipment.
How is CyPhER different from behavioral AI network security tools?
The behavioral AI category learns a statistical model of normal network behavior and alerts on deviation, and CyPhER instead builds a complete factual record and reasons deterministically over it. The distinction produces three practical differences. First, a learned baseline can be poisoned by an adversary already present during learning or patient enough to drift the model, and a factual record cannot. Second, behavioral tools express findings as confidence scores that an analyst must trust, and CyPhER expresses findings as evidence an analyst can inspect. Third, behavioral platforms are widely associated with alert volume and tuning burden, and our design goal is collapsing volume before a human sees it. Behavioral analytics have a legitimate place in a defense-in-depth stack, and we would simply argue they belong on top of ground truth rather than in place of it.
How is CyPhER different from OT asset inventory and visibility platforms?
The dominant OT security platforms are fundamentally asset-centric, meaning their core product is an inventory of devices, enriched with vulnerability data and protocol identification, and their detection is built around that inventory. CyPhER is behavior-centric. We watch the conversations rather than cataloguing the machines, which means our value concentrates precisely where inventory-centric tools are thinnest, in detecting active adversary movement across the network in progress. The two approaches are genuinely complementary, since knowing what a device is and knowing what it is currently doing answer different questions, and several of our deployment conversations involve environments that already run an inventory platform. What we would caution against is mistaking a rich inventory for detection coverage, because an accurate catalogue of your devices tells you nothing about the lateral movement happening between them.
How is CyPhER different from signature-based intrusion detection?
Signature-based detection matches traffic against catalogued patterns of known attacks, and it is only as current as its catalogue. It remains genuinely useful against commodity threats, and it fails structurally against novel, targeted, or modified tooling, which describes essentially all serious adversary activity against defense and critical infrastructure targets. CyPhER is signature-independent, working from complete observed behavior, so previously unseen techniques are detectable the moment they produce network activity. We do not position this as a replacement argument, since signature engines are cheap, mature, and worth keeping for what they catch. We position it as a coverage argument, because the attacks that matter most are the ones no signature exists for yet.
How is CyPhER different from flow-based monitoring and sampled telemetry tools?
Flow-based and sampled telemetry tools accept incomplete observation as a design constraint and build analytics on top of the gaps, and CyPhER removes the gaps. Sampling made sense as an engineering compromise when complete collection at scale was considered impractical, but a compromise adopted for engineering reasons has security consequences, since the traffic most likely to be missed is precisely the low-volume, patient traffic that characterizes serious intrusions. The research behind CyPhER made complete observation practical at scales where sampling was previously assumed necessary, and that is the core of our differentiation in this comparison. An analytics layer, however sophisticated, cannot recover information the collection layer never captured.
How is CyPhER different from network detection and response platforms built on content inspection?
Content-inspection NDR platforms derive detection from reading the substance of network communications, and their visibility degrades structurally as encryption spreads, which it is doing everywhere. Their mitigations, including in-line decryption and certificate-metadata inference, each carry cost, operational risk, or privacy complications. CyPhER Rx detects from communication behavior at layers 2 through 4 at real time speed, so pervasive encryption does not degrade it. Content inspection retains real value in environments where decryption is acceptable and message context matters for investigation, and for organizations facing the encrypted-traffic problem specifically, behavior-based detection is the approach that does not fight the direction the internet is moving.
How is CyPhER different from Darktrace?
Darktrace is a network detection and response platform built on self-learning AI, layering multiple techniques including unsupervised Bayesian anomaly detection, supervised machine learning, natural language processing, and graph analysis into a hierarchical system that models normal behavior for every device and user and surfaces deviation from it. It is a mature product with a large install base, and we agree with a great deal of how Darktrace describes the problem. Signature-based detection is inadequate, novel threats are the ones that matter, analysts are drowning in low confidence alerts, and unverifiable anomalies are a genuine failure mode of this market. Those are our arguments too. Where we differ is the architecture proposed to solve them. Layering additional models on top of a probabilistic foundation produces a more sophisticated estimate rather than a fact, because every layer inherits the epistemic status of the layer beneath it, and the foundational technique in that stack is a learned baseline of normal behavior. CyPhER inverts the order, establishing a complete factual record first and analyzing over facts rather than over inferences. The practical consequences are three. A learned baseline can be poisoned by an adversary already present when learning occurred or patient enough to drift the model over time, which is precisely the stealthy lateral movement threat class Darktrace describes, and a factual record cannot be trained around. Explainability in a layered model stack means a reconstructed account of why something scored highly, where traceability in a deterministic system means the observed activity that produced the finding is attached and independently inspectable. And a probabilistic system's efficiency gains, however real, still deliver probabilistic output, so an organization can halve its analyst hours and remain unable to prove what actually happened. Organizations with well staffed enterprise security operations and an appetite for autonomous response often run Darktrace well, and organizations that must prove what happened, or that operate networks where acting on an uncertain finding is itself an operational risk, are the ones we built CyPhER for.
How is CyPhER different from Corelight?
Corelight builds network evidence and detection products on an open-source network analysis framework, producing rich protocol-level logs that security teams use for detection, hunting, and forensics. It is a credible product with strong roots in the network security community, and of the tools in this market it is philosophically closest to ours, since both companies believe network evidence is the foundation of defense. The differences are architectural. Corelight's depth comes from protocol and content-level analysis, which is powerful where message contents are visible and becomes harder as encryption spreads, and CyPhER Rx analyzes communication behavior at layers 2 through 4 in real time, which encryption does not degrade. Corelight's output is a comprehensive log stream that skilled teams query and build detection on, and CyPhER's output is a small set of findings that have already collapsed volume before an analyst sees them, which in an operational United States Air Force evaluation meant 2.5 million alerts reduced to 5 findings in under 20 minutes. Teams with deep engineering benches sometimes prefer the raw material, and teams accountable for outcomes at scale tend to prefer the findings.
How is CyPhER different from Claroty?
Claroty is a cyber-physical systems security platform whose core strength is asset-centric, meaning discovery and inventory of OT, IoT, and connected devices, enriched with vulnerability and exposure management and delivered through both cloud and on-premises configurations. It answers the question of what is on your network in considerable detail. CyPhER is behavior-centric and answers a different question, which is what is moving across your network right now. We watch the conversations rather than cataloguing the machines, so our value concentrates in detecting active adversary movement, the lateral progression between systems that an inventory, however accurate, does not surface. The two are complementary in principle, and several environments we work in run inventory platforms alongside us. Where a buyer must prioritize, the deciding question is whether the nearer risk is not knowing what you own or not seeing what an intruder is doing, and for organizations facing capable adversaries the second risk is usually the one that keeps operators up at night.
How is CyPhER different from Nozomi Networks?
Nozomi Networks provides OT and IoT visibility and detection through passive traffic observation, optionally extended by controlled active queries to devices, with anomaly detection built on learned behavioral profiles of the industrial process. It is a well-established platform in industrial environments. Two architectural distinctions define the comparison. First, CyPhER is exclusively passive with no active query capability at all, which matters to operators for whom any transmission toward production equipment is disqualifying regardless of how carefully it is engineered. Second, Nozomi's detection leans on learned baselines of normal industrial behavior, and CyPhER's detection is deterministic over a complete record, which changes both what can evade it and how findings are verified. An adversary patient enough to be learned as normal defeats a baseline and does not defeat a factual record. For inventory-rich industrial dashboards, Nozomi is a capable choice, and for complete, evidence-grade observation of network behavior, that is the ground we hold.
How is CyPhER different from Dragos?
Dragos is an OT cybersecurity platform distinguished by its threat intelligence practice, with deep knowledge of industrial protocols and named adversary groups targeting industrial infrastructure, combined with passive monitoring and optional controlled active collection from Windows-based devices. Its intelligence-led approach means detection is strongest against adversary behaviors Dragos has catalogued and characterized. CyPhER approaches the problem from the opposite direction, working from complete observation of what is actually happening rather than from intelligence about what known adversaries have done elsewhere, which means previously uncharacterized activity is detectable the moment it produces network behavior. Intelligence and observation are complements rather than substitutes, and the honest framing is that Dragos tells you what known industrial adversaries do, while CyPhER shows you what is happening on your network whether or not anyone has seen it before. Organizations that want both perspectives are not wrong to want both.
How is CyPhER different from Vectra AI?
Vectra AI is an enterprise detection platform centered on AI-driven attack signal intelligence, correlating detections across network, identity, and cloud surfaces and prioritizing them by model-scored urgency. It is an established product with a genuine strength in reducing which alerts an enterprise SOC looks at first. The architectural difference is at the foundation. Vectra's detections are probabilistic, produced by models scoring behavior against learned patterns, and CyPhER's findings are deterministic, derived from a complete factual record of network communications at layers 2 through 4. A prioritization engine, however good, inherits the blind spots of the signal underneath it, and signal built on models can be evaded by patience and absorbed drift in ways a complete record cannot. For enterprises whose core problem is triage across an existing detection estate, Vectra addresses that directly, and for organizations whose core problem is knowing with certainty what happened on the network, certainty is the product we build.
How is CyPhER different from ExtraHop?
ExtraHop is a network detection and response platform built on real-time analysis of network transactions, including capabilities for decrypting traffic to inspect contents, with strong investigative workflows for enterprise IT environments. Its transaction-level depth is genuinely useful where decryption is acceptable and message context drives investigation. The comparison with CyPhER turns on two choices. ExtraHop's richest visibility depends on access to message contents, which pervasive encryption makes progressively more expensive to obtain and which many defense and regulated environments prohibit outright, and CyPhER Rx analyzes communication behavior at layers 2 through 4 in real time regardless of what encryption is in use. ExtraHop is an enterprise IT product first, and CyPhER was built against defense and critical infrastructure constraints, meaning full passivity, on-premises operation, and networks full of equipment no agent or decryption architecture will ever touch. Where those constraints do not apply, ExtraHop competes well, and where they do, they usually decide the evaluation.
How is CyPhER different from Armis?
Armis is an agentless asset intelligence platform covering IT, OT, IoT, and medical devices, with a cloud-delivered architecture and one of the broadest device inventories in the market. Its strength is answering what is connected across a converged estate, enriched with vulnerability and exposure context. CyPhER answers a different question, which is what is moving across the network right now, and the distinction is the same one we draw across the asset-centric category. An inventory tells you what you own and what could be exploited, and a complete behavioral record tells you what an adversary is actually doing. There is also a structural difference that matters to our customers specifically, since Armis is cloud-first and CyPhER operates fully on premises with no cloud dependency, which is decisive for air-gapped, classified, and sovereignty-constrained environments. Organizations wanting a converged device census are well served by Armis, and organizations that must see and prove adversary movement are the ones we built for.
How is CyPhER different from Forescout?
Forescout is a device visibility and control platform with heritage in network access control, meaning its distinctive capability is not just seeing devices but enforcing policy against them, quarantining, segmenting, and gating access across IT and OT. It is a mature platform with broad protocol coverage. The difference is that Forescout's center of gravity is knowing and controlling what is on the network, and CyPhER's is producing a complete, deterministic record of what is happening across it. Enforcement is only as good as the detection informing it, and detection built on device identity and policy state does not surface the thing our customers most need surfaced, which is adversary movement between systems that are each individually authorized and policy-compliant. Lateral movement travels over legitimate credentials and permitted paths, and it is visible as behavior, not as a policy violation.
How is CyPhER different from Microsoft Defender for IoT?
Microsoft Defender for IoT provides agentless OT and IoT monitoring integrated with the broader Microsoft security ecosystem, and its distinguishing value is exactly that integration, flowing OT signals into Sentinel and the Defender portal alongside connectors that now ingest asset context from other OT platforms. For organizations standardized on Microsoft security operations, the convenience is real. The architectural comparison is the one that runs through this whole section. Defender for IoT is a visibility and alerting module inside an IT-centric ecosystem whose detection approaches are signature-informed and behavioral, and CyPhER is a purpose-built deterministic sensor producing a complete record of network activity independent of any vendor ecosystem. Ecosystem integration is a genuine operational benefit and it is not a detection property, and an adversary is not easier to see because the alert about them lands in a familiar console. We integrate with existing security stacks, including Microsoft-centered ones, as a source of ground truth rather than another opinion.
How is CyPhER different from Cisco Cyber Vision?
Cisco Cyber Vision provides OT asset visibility by embedding sensing into Cisco industrial networking hardware, analyzing traffic directly at the switch, and it pairs naturally with Cisco's segmentation and policy enforcement portfolio. For organizations heavily invested in Cisco industrial infrastructure, the embedded approach reduces deployment friction. The tradeoffs are the ones embedding implies. Cyber Vision's reach follows the Cisco hardware footprint, its analytical role is primarily visibility feeding Cisco's enforcement stack, and its detection is inventory and anomaly oriented. CyPhER is infrastructure-independent, observing from mirror and tap points regardless of whose equipment moves the traffic, and its purpose is not enriching a vendor's segmentation policy but producing a complete deterministic record from which adversary behavior is detected and proven. A sensor tied to the network vendor also shares fate with the network vendor, and for defenders who want their evidence source independent of the infrastructure being attacked, independence is a security property, not a procurement preference.
How is CyPhER different from Tenable OT Security?
Tenable OT Security extends Tenable's vulnerability management heritage into industrial environments, discovering OT assets and prioritizing their vulnerabilities, configuration weaknesses, and exposure through risk scoring. It answers what could be exploited, and it answers that question well for organizations building remediation programs. CyPhER answers what is being done, right now, on the network. The gap between those two questions is where intrusions live, because a fully patched environment can still be traversed with valid credentials, and an unpatched one is not necessarily under active attack. Vulnerability posture and behavioral ground truth are different security functions, and conflating them leaves the detection function unstaffed. We would describe Tenable OT as complementary rather than competitive in most environments, with the caution we apply to the whole exposure-management category, which is that a prioritized list of theoretical risk is not a record of actual adversary activity, and only one of those exists when an incident review asks what happened.
How is CyPhER different from Palo Alto Networks' OT security offering?
Palo Alto Networks approaches OT security through its firewall platform, extending next-generation firewall inspection, AI-driven threat prevention, and zero trust access policy into industrial environments. It is a leader-quadrant enterprise platform, and for organizations consolidating on Palo Alto infrastructure the OT extension is a natural motion. The architectural distinction is a firewall's vantage point. Firewalls see traffic at the boundaries they sit on, and enforcement-centered architectures are strongest at controlling crossings and weakest at observing the east-west movement inside zones where no firewall stands. CyPhER's purpose is precisely that interior, a complete passive record of communications across the whole observed environment, independent of enforcement topology. There is also the paradigm difference that runs through this page, since prevention platforms are probabilistic and signature-informed at the detection layer, and CyPhER is deterministic over complete observation. Boundary enforcement and interior ground truth are complements, and treating the first as if it delivered the second is how east-west intrusions go unseen.
How is CyPhER different from Fortinet's OT security offering?
Fortinet provides OT security through its unified security fabric, using ruggedized next-generation firewalls for segmentation, industrial protocol awareness, and a mix of passive discovery and active scanning for visibility. For organizations wanting one hardware vendor across IT and OT enforcement, the fabric approach has real consolidation appeal. Two architectural differences define the comparison. First, Fortinet's visibility model includes active scanning, and CyPhER is exclusively passive, which matters wherever transmitting toward production equipment is unacceptable regardless of how carefully the scan is engineered. Second, a firewall-centric architecture observes at enforcement points, and adversary lateral movement happens predominantly in the interior spaces between them, over permitted paths and valid credentials, where CyPhER's complete east-west record is designed to look. Segmentation is a genuinely valuable control and we do not argue against it. We argue that segmentation without interior ground truth is a set of walls with no record of what is moving between them.
How is CyPhER different from open-source network monitoring tools?
Open-source network monitoring frameworks give skilled teams free, transparent, and deeply customizable network visibility, and some of the best security engineering teams in the world run them well. We respect the lineage, since parts of the commercial market are built on exactly these foundations. The honest differences are operational and architectural. Open-source deployments produce raw material, meaning logs and events that a team must engineer into detection, storage, and workflow, and that engineering investment is substantial and permanent. CyPhER produces finished findings from a complete deterministic record, collapsing volume before an analyst sees it rather than adding a stream for the team to manage. There is also the scale question, since complete observation of large environments is a hard systems problem that our research lineage exists specifically to solve. For a well-staffed team monitoring a modest environment, open source is a defensible choice, and for organizations accountable for large, sensitive networks without an engineering bench to spare, the total cost comparison usually surprises people.
Why not just use the biggest platform vendor's OT security module?
Large platform vendors bundle OT visibility modules into broader security suites, and the bundle is genuinely convenient, but bundled modules inherit the architectural assumptions of their parent platforms, which were built for IT environments and cloud delivery. The recurring gaps are full passivity, since many bundled approaches involve some active interrogation, on-premises operation without cloud dependency, and detection that survives encryption and evades neither sampling nor baseline drift. Purpose-built sensing is what we do rather than a line item in a portfolio, and for defense and critical infrastructure operators whose constraints are hard rather than preferential, purpose-built is usually what the constraints end up requiring.