CrowdStrike, a global leader in cybersecurity, has announced the general availability of CrowdStrike Signal, a revolutionary class of AI-powered detection engines designed to uncover the threats that often go undetected by conventional tools. This cutting-edge solution enhances the Falcon® platform’s AI capabilities by leveraging self-learning models on every host, enabling a deeper understanding of what constitutes normal behavior across systems, users, and time ultimately allowing security teams to detect subtle, early-stage attack activity before it escalates.
Unlike traditional rule-based or reactive detection systems, CrowdStrike Signal proactively surfaces weak signals and links them to related suspicious behavior to generate prioritized, high-confidence leads. This empowers cybersecurity teams to hunt, investigate, and neutralize threats earlier in the kill chain, enhancing overall threat response efficiency.
“CrowdStrike pioneered AI-native cybersecurity, and continues to deliver the innovation driving the industry forward. Signal is our latest breakthrough, built to detect how modern adversaries actually operate,” said Elia Zaitsev, chief technology officer, CrowdStrike. “Today’s attackers spread subtle signals over time to stay under the radar. Signal is designed to catch what others overlook, connecting the dots across systems and time to paint the full picture.”
Detecting the Undetectable
Modern cyber threats are becoming increasingly stealthy, often initiating through seemingly benign actions that slip past traditional detection systems. These initial low-signal behaviors are typically overlooked because they lack the context to appear suspicious on their own. While many AI tools apply scoring methods after a threat is detected, Signal takes a more proactive approach.
CrowdStrike Signal continuously learns what is “normal” within each environment and dynamically adjusts its understanding as that environment evolves. This allows it to identify deviations that indicate malicious activity and correlate early behaviors with downstream tactics. The result is a concise set of AI-generated leads that cut through the noise and fast-track response times.
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Powering a New Era of Threat Detection
At the core of Signal is a powerful family of statistical time series models capable of processing billions of daily events within a customer’s ecosystem. By connecting behaviors across systems and timeframes, Signal identifies truly anomalous activity delivering unparalleled visibility into stealthy adversary operations.
Key capabilities include:
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Self-learning AI for Adaptive Detection
Signal models behavioral patterns across users, hosts, and processes continuously learning and evolving to highlight meaningful deviations. Unlike rigid rule-based tools or static pre-trained models, Signal provides early-stage detection without the need for manual tuning or ongoing reconfiguration. -
Real-time Insight into Stealth Tactics
Signal effectively identifies low-signal behaviors often used in advanced attacks such as living-off-the-land (LOTL) techniques or the execution of applications from temporary directories. While these actions may seem harmless in isolation, Signal’s ability to analyze them over time and in context enables the detection of hidden threats. -
Actionable Intelligence with Fewer Alerts
By condensing millions of behavioral events into a focused set of high-confidence leads, Signal minimizes false positives and eliminates alert fatigue. It provides early indicators of compromise and intelligently groups related activity into unified leads helping security teams prioritize faster and respond with precision.