Darktrace for Behavioral Threat Detection

In-Depth Review

Darktrace pioneered the application of unsupervised machine learning to cybersecurity, launching from Cambridge, UK in 2013 with technology inspired by the human immune system. Rather than telling the system what threats look like, Darktrace learns what normal looks like and flags everything that deviates from that baseline.

What Sets Darktrace Apart

Darktrace’s self-learning approach is fundamentally different from the detection methodology used by EDR platforms, SIEMs, and traditional intrusion detection systems. Where those tools rely on rules, signatures, or supervised ML trained on labeled attack data, Darktrace uses unsupervised machine learning to build a probabilistic model of normal behavior for every device, user, and network flow. This means it can detect threats that have never been seen before, because it is looking for behavioral anomalies rather than known indicators.

This approach gives Darktrace a genuine advantage in two scenarios that other tools handle poorly: insider threats and novel attack techniques. A compromised employee account that starts accessing unusual file shares at unusual hours, or an attacker using legitimate system tools in ways that deviate from established administrative patterns — these are the scenarios where Darktrace excels.

The Cyber AI Analyst module addresses one of the biggest pain points in modern SOC operations. It automatically investigates clusters of related anomalies, determines which ones represent coherent incidents, and produces investigation reports that mirror what a human Tier 2 analyst would write. This has measurably reduced investigation times for organizations that deploy it effectively.

Limitations to Understand

Darktrace’s biggest challenge is the false positive problem during and after the initial learning period. In environments with frequent legitimate changes — new applications, infrastructure migrations, seasonal business pattern shifts — the model can flag normal activity as anomalous, creating alert fatigue that undermines its value. Organizations need analysts who understand both the technology and the business context to distinguish genuine threats from model noise.

The platform is also not a replacement for endpoint protection. Darktrace operates primarily at the network level and excels at detecting threats in transit, but it does not provide the prevention, containment, and remediation capabilities of an EDR agent running on each endpoint. Most organizations deploy Darktrace alongside, not instead of, tools like CrowdStrike or SentinelOne.

The Bottom Line

Darktrace is a powerful detection layer for organizations that have already invested in prevention and are looking for visibility into threats that bypass their existing controls. It is at its best in complex environments with diverse device types, where its agentless approach and behavioral modeling provide coverage that agent-based tools cannot match. Budget for adequate tuning and experienced analysts to extract full value.