Gatewatcher NDR Platform

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.

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.

Extends network detection to cloud VPC traffic using VPC flow log analysis, cloud-native sensors, or mirroring, covering east-west traffic between cloud workloads.

Detects threats inside TLS-encrypted sessions either without decryption, through JA3, JA4, and certificate fingerprinting plus behavioral analysis of encrypted flows, or through on-appliance decryption where keys are available for full payload inspection. Fingerprint-only analysis is now standard across NDR; on-appliance decryption, JA4 support, and detection quality on encrypted command-and-control are the differentiators.

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.

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.

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.

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.

Captures and retains full packets (PCAP) at scale alongside flow and metadata records, with long-term retention and session reconstruction or replay, so analysts can pivot from an alert to the exact underlying packets and run retroactive investigations against historical traffic. Metadata-only products that retain no packets do not qualify.

Extracts files and payloads from network sessions and analyzes them in a built-in sandbox and layered malware engines, combining static and dynamic analysis, so suspicious downloads and attachments are classified inside the NDR platform without handing them to a separate third-party sandbox.

Uses an AI assistant to qualify and triage network detections inside the NDR console, explaining each anomaly in plain language, assembling related detections into an incident narrative, and recommending the next investigation or response step, so analysts spend less time on first-pass triage of network alerts.

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.

Renders the network events, entities, and detections of an incident on an interactive timeline or attack graph, so analysts can reconstruct the sequence of an intrusion across hosts and time and see the path an attacker took through the environment.

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.