Organizations monitor hybrid cloud networks by collecting and analyzing traffic and activity across on-premises, virtual, and multi-cloud environments under a single model, rather than relying on separate monitoring tools for each environment that don't share context with one another.

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Darktrace / HYBRID NETWORK does this by passively ingesting traffic from on-premises and virtual networks, multi-cloud environments, OT, endpoints, and identities, building one continuously evolving behavioral profile across the full hybrid estate rather than siloed views per domain.

Securing hybrid networks is challenging because every connection between environments – on-premises, cloud, OT, identity, and endpoint – creates a potential path for attackers to move laterally, and most tools monitor individual domains well but struggle to connect activity across them. Rising vulnerability volumes add further pressure to prioritize which exposures matter most.

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Darktrace / HYBRID NETWORK addresses this by applying one behavioral model across domains, so cross-domain threats are visible as a single connected incident rather than fragmented, isolated alerts.

AI-powered network detection and response (NDR) improves on traditional NDR by identifying threats through behavioral deviation rather than relying solely on known signatures or attack patterns, which allows it to catch novel, insider, and previously unseen threats. It also supports automated, context-aware response rather than requiring manual triage for every alert.

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Darktrace / HYBRID NETWORK's Adaptive AI learns each organization's network from scratch, and its approach is proven to detect zero-day threats an average of 8 days before public CVE disclosure.

NDR protects hybrid networks by analyzing network traffic across environments to detect malicious or anomalous activity, then containing it through automated response actions, giving security teams visibility and control that host-based tools alone can't provide across unmanaged or cross-domain assets.

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Darktrace / HYBRID NETWORK extends this to on-premises, cloud, OT, identity, and endpoint environments under one behavioral model, using autonomous, configurable response actions to contain threats while minimizing disruption to normal business operations.

Detecting lateral movement requires visibility into how systems, identities, and workloads interact across domains, since an attacker's individual actions often look legitimate in isolation and only become suspicious when viewed in sequence and context. Network-layer visibility is essential here, since host-based tools alone can miss movement between unmanaged assets.

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Darktrace / HYBRID NETWORK correlates behavior across network, cloud, identity, and OT activity to reveal when a sequence of otherwise-legitimate actions indicates lateral movement, rather than evaluating each event independently.

Behavioral analysis improves network threat detection by identifying activity that's unusual for a specific environment, rather than checking only for known indicators of compromise, signatures, or attack patterns. This allows detection of unknown, insider, and AI-accelerated threats that have no prior classification.

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Darktrace / HYBRID NETWORK builds this understanding through a Unique Behavioral Profile learned from each organization's own network traffic, analyzing both encrypted and decrypted data to identify deviations that indicate risk.