Darktrace detects stealthy APT attacks by focusing on behavioral analysis rather than signature-based detection. APTs often attempt to blend in with regular network activity by mimicking legitimate user behavior and using encryption to evade detection. Darktrace learns the normal behavior of users and devices and flags deviations from this baseline, such as unexpected access to sensitive data or unusual network traffic patterns. This helps identify APTs that try to remain hidden.

Darktrace uses Self-Learning AI to build a real-time understanding of what is normal across your digital environment — including users, devices, cloud workloads, and applications. This allows it to detect subtle deviations that may indicate the presence of novel or signatureless malware, without relying on known threat indicators or static rules.

Rather than focusing on what malware looks like, Darktrace focuses on what it does. This enables it to identify early-stage behaviors like command-and-control communication, internal reconnaissance, or unusual data access patterns — even if the malware has never been seen before.

Nation-state attackers often operate slowly, stay hidden for long periods, and use highly customized tools to evade detection. Darktrace is effective in these environments because it continuously monitors network, OT, and IT environments, building a unique baseline of normal behavior for each environment.

When an attacker attempts to move laterally, escalate privileges, or exfiltrate data, even using legitimate credentials or tools, Darktrace spots the behavioral anomalies that signal a compromise. This is especially important in critical infrastructure, where attackers may use "living off the land" techniques that go undetected by traditional tools.

What role does Darktrace play in defending against attacks where APT groups use email as a primary vector for socially engineered phishing attacks?

Darktrace extends its AI-led security capabilities to the email landscape with Darktrace / EMAIL. It analyzes tone, payloads, header anomalies, and historical communication patterns to detect and stop socially engineered phishing emails, even when they come from trusted accounts.