Nozomi Networks Platform

Aggregates related network alerts into structured incidents that link the hosts, accounts, and detections of one attack, reducing alert volume and giving analysts one case to investigate and respond to instead of disconnected events.

Learns per-entity baselines of normal network behavior for devices, users, and applications, typically with unsupervised or self-learning models that need little manual tuning, and detects deviations that reveal insider threats, external attacks, and advanced persistent threats, including novel command-and-control, data staging, and lateral movement. Detection quality separates products: self-learning models with minimal tuning versus rule-primary engines with limited machine learning.

Executes containment automatically on confirmed detections: isolating infected hosts, blocking malicious traffic, or disabling compromised accounts, either natively (for example through the vendor's own switches, firewalls, or inline sensors) or through integrations with firewalls, NAC, EDR, SASE or SSE, and SOAR platforms. Whether enforcement is native or integration-dependent is the primary buying distinction.

Discovers and inventories cyber-physical system assets (OT, ICS, IoT, and medical devices) and their communication channels from the same sensors that monitor IT traffic, baselines normal CPS activity and alerts on deviations, and parses industrial protocols (Modbus, DNP3, EtherNet/IP, PROFINET, IEC 61850, OPC UA) for deep inspection, so IT and CPS attacks are detected and correlated in one NDR console. Protocol depth and CPS asset detail vary widely across NDR products.

Matches observed traffic against continuously updated threat-intelligence feeds, both the vendor's global intelligence and customer-imported internal or third-party feeds, to recognize malicious infrastructure, command-and-control patterns, and known indicators, and enriches detections with the matching intelligence context.

Provides a natural-language search assistant over network metadata, detections, and entities, so analysts can ask hunting questions in plain language, receive generated queries and summarized results, and pivot across hosts, accounts, and sessions without writing query syntax. Distinct from AI-assisted triage, which qualifies detections rather than answering analyst queries.

Runs traditional detection alongside behavioral analytics: intrusion-detection signatures (Suricata or Zeek rule sets and vendor IPS signatures), rule-based heuristics, and threshold alerts, with support for importing community rules and authoring custom rules, so known exploits and indicators are caught deterministically and analysts can codify their own detections.

Runs behavioral detection, machine-learning models, and AI assistance entirely on local sensors and management appliances, with no cloud-tethered analysis or external data sharing, so detection quality is undiminished in air-gapped, sovereign, or disconnected environments.

Assigns a risk score to each detection and affected entity from threat severity, detection certainty, and asset or account importance, with adjustable scoring, so response effort goes to the highest-risk hosts and accounts first rather than to the newest alert.

Parses raw traffic with deep packet inspection into structured, protocol-level metadata records, such as Zeek-style connection, DNS, HTTP, and TLS logs, and enriches them at collection or analysis time with asset, user, geolocation, and threat-intelligence context, producing hunt-ready evidence that is retained far longer than packets and exportable to a SIEM or data lake.

Discovers every device communicating on the network and assembles a continuously updated inventory with device type, role, protocols in use, and communication paths, grouping and tracking entities across address changes (for example through a knowledge graph) and tagging criticality and exposure, so risk scoring and investigations start from an accurate map of what is on the network.

Maps network topology, identified vulnerabilities, and detected anomalies to IEC 62443 zone and conduit requirements and security level targets.

Monitors ICS network traffic by analyzing span port or tap data without injecting any traffic, critical for environments where active probing can cause PLC faults or safety system trips.

Inspects industrial protocols (Modbus, DNP3, IEC 61850, EtherNet/IP, PROFINET, OPC-UA, BACnet) at function-code level for commands and configuration changes. Coverage breadth and inspection depth (command-level function code analysis vs. packet-level header parsing) both vary across ICS security products and are primary evaluation criteria.

Provides a single platform, or integration paths, for monitoring enterprise IT and industrial control networks together, forwarding ICS alerts and asset data into SIEM, SOAR, ITSM and CMDB tooling with control-system context (asset criticality, Purdue level, process impact) preserved so that a unified SOC can act without separate ICS tooling or ICS-specialized analysts.

Identifies device vulnerabilities by fingerprinting asset type, firmware version, and protocol implementation from passive traffic observation, no active scan that could disrupt device operation.

Models expected behavior of safety-instrumented systems (SIS) separately from process control systems, preventing false alerts on normal SIS state machine transitions.

Builds ICS asset inventories (PLCs, RTUs, HMIs, engineering workstations and nested devices behind controllers) with model, firmware and version detail, primarily from passive network observation and, where supported, OT-safe active queries and controller project-file parsing that cannot disrupt operations. Passive-only versus multi-method discovery is the main difference between products.

Discovers and identifies OT assets, including nested devices behind controllers, with manufacturer, model, serial number, firmware and version detail, using passive traffic analysis first and, where the product supports them, OT-safe methods such as selective active querying, controller project-file parsing, lightweight host executables and switch or firewall telemetry. Passive-only versus multi-method discovery and the depth of identification vary widely.

Dissects OT protocol payloads at the function code level, detecting unauthorized read/write operations, unusual register ranges, and firmware upload commands in Modbus, DNP3, EtherNet/IP, PROFINET, and OPC-UA traffic.

Baselines normal device communication patterns (command frequency, connection pairs, timing) and operational state, alerts on deviations that indicate reconnaissance, manipulation or lateral movement, and rates severity by asset criticality and process impact rather than by anomaly size alone. Products differ in whether baselines self-tune over time to operational and environmental changes or require ongoing manual tuning.

Maps actual traffic flows between IT and OT zones and between Purdue model levels, revealing unauthorized cross-zone connections and segmentation failures.

Classifies discovered assets and traffic flows into Purdue Model levels (Level 0-4), supporting IEC 62443 zone and conduit documentation and compliance assessment.

Connects OT security to enterprise security operations either as a single converged console for IT and OT or through integration paths into SIEM, SOAR, ITSM, CMDB, NAC and firewall tooling, forwarding alerts and asset data with OT context (asset criticality, Purdue level, process impact) preserved so that SOC analysts can act without OT specialization. Assign only when integrations preserve OT context or run bidirectionally; basic syslog forwarding is standard across the niche.

Identifies and prioritizes vulnerabilities across discovered OT and ICS assets using device, firmware, and exposure context, recommending safe, operationally feasible remediation or compensating controls for environments where patching is constrained.

Models operational risk for each asset, zone and site by combining device vulnerabilities, network access paths, real-world exploitability, detected threats, operational errors and asset criticality, and ranks exposures by their potential impact on safety systems and crown-jewel operational assets rather than by raw vulnerability counts, reflecting that most OT assets cannot be patched on IT timelines. Risk inputs such as controller logic, device lifecycle stage and peer benchmarking vary by product.

Detects and responds to threats on OT hosts such as engineering workstations, HMIs and SCADA servers and, where supported, on controllers through lightweight embedded agents for PLCs and RTUs, complementing network-based detection with host-level visibility and response. Agentless-only versus agent-capable coverage is the main split between products.

Tracks OT security posture against IEC 62443, NIS2, NERC CIP and other sector regulations by mapping discovered assets, zones, vulnerabilities and controls to specific requirements and producing audit-ready compliance reports and gap lists. Usability of the tracking workflow varies widely.

Monitors control networks without adding latency or traffic, using passive SPAN or TAP collection and out-of-band sensors, and keeps full detection, analysis and reporting working at disconnected, air-gapped or intermittently connected sites through fully on-premises operation. Cloud-reliant products lose function at isolated sites; isolated-site-capable products do not.

Provides curated intelligence on adversary groups, malware and vulnerabilities that specifically target industrial control systems, with detections, playbooks and recommended actions tied to that intelligence and, in some products, community sharing of threats observed across other OT environments.

Captures baselines of controller logic, firmware versions and device configurations, alerts on unauthorized changes such as logic downloads, mode changes and firmware updates, and feeds configuration drift and weak settings into risk scoring.

Discovers and fingerprints purpose-built connected devices (printers, cameras, infusion pumps, smart meters, building systems), classifying make, model, OS, firmware, and function, including unmanaged devices that cannot run an endpoint agent.

Identifies, prioritizes, and helps remediate device vulnerabilities, including outdated firmware and exposed network services, across the connected-device fleet.

Assesses overall device-ecosystem risk (device trustworthiness, exposure, and operational context) as a continuous posture, distinct from per-CVE vulnerability management.

Generates and enforces least-privilege network segmentation and microsegmentation policies for devices, with pre-deployment impact assessment so new policies do not break device operations.

Monitors network traffic and individual device behavior to detect anomalies, exploits and threats targeting connected devices, baselining each device class and rating severity by device criticality and business function. Products differ in whether they cover both network-level and device-level monitoring and whether baselines self-tune over time or need manual tuning.

Forwards device alerts and inventory into SIEM, SOAR, ITSM, CMDB and NAC tooling with device identity, owner, location and business function attached, so that security operations can triage and act on connected-device incidents without a separate device console.

Maps connected-device inventory, vulnerabilities and controls to regulatory and framework requirements such as HIPAA, PCI DSS, NIS2 and IEC 62443, producing audit-ready evidence and gap reports for device fleets.

Executes automated responses to device incidents, such as quarantine through NAC or firewall policy, ticket creation and device-owner notification, through native playbooks or SOAR integration, with impact checks so that automated actions do not take critical devices offline.