Next-Generation Firewalls (NGFW) control traffic at your network perimeter, but modern adversaries blend into encrypted and trusted paths beyond those boundaries. Vectra AI works alongside your NGFW investment, adding AI-driven threat detection across network, cloud, SaaS, and identity layers to close those visibility gaps.
Firewalls are essential for enforcing traffic policies, yet attackers use encrypted tunnels, legitimate SaaS apps, and stolen credentials to slip past those controls. To see what happens once access is granted, you need AI-driven detection that complements your firewall’s edge enforcement.
Attackers use encrypted tunnels or legitimate SaaS apps to bypass deep packet inspection
Firewalls allow authenticated users, even if their credentials are stolen.
Firewalls control ingress and egress traffic but fail to see attacker movement inside hybrid environments.
In the Scattered Spider scenario below, firewalls enforce perimeter rules—but encrypted communications, valid credentials, and API-based SaaS calls all appear legitimate. Vectra AI’s real-time analytics would flag each stage as attackers move through hybrid environments.
Firewalls enforce who and what can cross your perimeter, but they don’t monitor what happens after access is granted. To catch credential abuse, lateral movement, and cloud-native tactics, you need continuous, AI-driven behavior monitoring across network, cloud, and identity layers.
Firewalls filter, inspect, and block traffic, but:
Firewalls secure network boundaries, but they don’t detect attackers once inside. The Vectra AI Platform identifies threats in real time across network, cloud, and identity layers, filling the detection gaps firewalls leave behind.
With Vectra AI, you can stop threats that firewalls miss—before they escalate into a breach.
Firewalls focus on traffic control, while Vectra AI detects active threats beyond perimeter defenses. Here’s how they compare:
Vectra AI doesn’t replace firewalls, it enhances them by detecting the threats that traffic controls miss.