Exeon.NDR

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.

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.

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.

Attributes network activity and detections to users and accounts by ingesting identity provider, directory, and SSE or SASE telemetry, correlating network anomalies with user behavior and distinguishing on-premises users from remote workers, so lateral movement and insider activity are traced to an identity rather than only to an IP address.

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.

Learns from analyst dispositions and confirmed benign patterns to suppress recurring false positives and adjust detection thresholds automatically after the initial learning period, keeping the alert stream trustworthy enough to drive automated response.

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.