Where these traditional tools stay silent, CrowdStrike Signal takes action. It identifies activity that deviates from the norm early in an attack chain, then correlates this suspicious behavior across time and systems. Fragmented events and indicators become high-confidence, automated leads that expose threats buried in the noise and automatically update over time, painting the full real-time picture others easily miss.
Catching What Others Overlook
Behind CrowdStrike Signal is a new family of statistical time-series models that analyze billions of daily events within each customer’s environment. Unlike static rules or pre-trained models, CrowdStrike Signal continuously learns what’s normal across each environment and updates its understanding of standard activity as conditions change. It identifies what deviates, and links early behaviors with downstream activity to create high-fidelity leads without tedious manual tuning or constant adjustment.
CrowdStrike Signal represents a fundamental shift in how organizations detect and respond to modern threats. Using self-learning AI to build models for every host, CrowdStrike Signal understands what’s normal in each environment across time, systems, and users. It pinpoints subtle, early-stage threat activity and notifies analysts with a single high-confidence lead, while legacy tools wait for more obvious signs.
Security teams don’t need more alerts. They need the ability to detect what others miss. That’s why we’re excited to announce the general availability of CrowdStrike® Signal, a new class of AI-powered detection that surfaces the stealthy threats others often overlook — before they escalate.
Self-Learning AI that Adapts to Each Environment
Modern attacks often begin with ambiguous activity that in isolation can appear benign. Today’s attackers make subtle moves over time to stay under the radar — for example, by using legitimate tools for reconnaissance or running applications from a temporary directory. Traditional detection technology often ignores these behaviors because it lacks the broader context to tell what’s suspicious and what’s just noise.
By intelligently grouping indicators and detections across time and systems, CrowdStrike Signal filters out benign activity and surfaces what’s truly unusual. This correlation builds high-confidence patterns that reveal stealthy attacker behavior, giving defenders a clear starting point to begin investigating without sifting through a multitude of individual alerts. The longer CrowdStrike Signal observes an environment, the more accurate its detection becomes. This leads to fewer false positives and a greater chance of catching subtle or emerging threats before they escalate.
