Every SOC analyst knows the feeling: another morning, another queue of hundreds of alerts, and the gnawing question of which ones actually matter. The volume of internet background noise — automated scanners, research probes, vulnerability crawlers — hasn’t slowed down. If anything, it’s accelerating. And as adversaries adopt AI to move faster, the cost of chasing the wrong signals isn’t just frustrating — it’s dangerous.
That’s the problem GreyNoise was built to address. We operate one of the largest passive sensor networks on the internet — more than 5,000 sensors across 80 countries, analyzing up to one billion sessions per day and tracking over 50 million IPs. That scale lets us classify internet-wide scanning and reconnaissance activity with confidence: which IPs are known benign scanners, which are actively malicious, and which are unknown — meaning we haven’t observed them scanning the internet indiscriminately.
That classification data is now available across the CrowdStrike Falcon platform — in Next-Gen SIEM, Falcon Fusion SOAR, and the agentic workflows that are defining the next era of security operations.
GreyNoise Intelligence Across CrowdStrike Falcon
For teams running Falcon, GreyNoise intelligence is operationalized across three integrated capabilities — inline investigation context in Next-Gen SIEM, automated enrichment and response in Falcon Fusion SOAR, and agentic collaboration through Charlotte AI.
Falcon Next-Gen SIEM: GreyNoise Classification Inside Your Existing Queries
The GreyNoise Foundry App — available directly on the CrowdStrike Marketplace — is the operational core of the integration. Once installed, it automatically imports a fresh GreyNoise indicator lookup file into Next-Gen SIEM every day. No manual feed management. No stale data.
That lookup file contains GreyNoise’s full dataset of classified IPs — benign scanners, malicious actors, CVE-targeting sources, and tagged threat infrastructure. Inside Next-Gen SIEM, analysts use the match() function to incorporate that data directly into their searches and analytics. GreyNoise classification columns — classification, observed activity, exploited CVEs — surface right alongside event data in the query view, with no pivot to an external tool required.
Detections tied to IPs that GreyNoise has identified as active exploit sources or malicious infrastructure stand out. Teams can build correlation rules and dashboards that weight GreyNoise-validated threats higher. And IPs that GreyNoise has classified as benign — known research scanners, internet measurement services, well-documented security vendors — carry that context right in the query results, giving analysts the information they need to make confident triage decisions.
The Foundry App ships with a pre-built app template containing GreyNoise threat intelligence actions, ready to deploy in Foundry and extend into Fusion SOAR workflows.
Falcon Fusion SOAR: Automated Enrichment and Response
Knowing an IP is malicious is useful. Acting on that intelligence automatically is where the efficiency gain lives.
The GreyNoise Foundry App includes a native Falcon Fusion SOAR integration that puts GreyNoise enrichment directly into workflow logic. Security teams can build — or extend — automated playbooks that take action based on GreyNoise IP context:
- Alert on malicious IPs — trigger high-priority notifications when GreyNoise identifies adversary activity at the perimeter
- Prioritize vulnerability response — surface CVE exploitation data to inform which vulnerabilities need immediate patching attention
- Initiate threat hunts — automatically kick off hunt workflows when GreyNoise identifies coordinated scanning tied to known threat infrastructure
- Automate blocking or containment — close the loop on confirmed malicious IPs
GreyNoise’s benign classification is particularly valuable here. Because GreyNoise classifies known-good IPs — security researchers, CDN health checks, legitimate vulnerability scanners — SOAR workflows have a higher-confidence basis for automated routing decisions. That confidence is grounded in what our sensor network directly observes, not aggregated from third-party sources.
Charlotte AI: GreyNoise as a Trusted Ecosystem Participant
CrowdStrike’s blog on building an agentic security workforce names GreyNoise among the trusted ecosystem participants supported in Charlotte AI’s Agentic Response Collaboration capability — alongside Corelight, ExtraHop, Proofpoint, Google, Abnormal AI, and Zscaler. These integrations provide what CrowdStrike describes as “deep cross-domain context to drive faster, more accurate analysis.”
Charlotte AI’s use of ecosystem data is still maturing, and we’ll share more as it develops. But the direction is clear: as agentic workflows become a core part of how SOC investigations run, GreyNoise intelligence can be part of the reasoning loop.
Here’s what that looks like in practice. An alert fires on a suspicious external IP. Charlotte AI’s Detection Triage Agent is working the case. As part of its investigation, GreyNoise context is available: Is this IP part of a known mass scanner campaign? Has it been observed exploiting the specific vulnerability that generated the alert? Is it tied to active threat infrastructure? That intelligence informs the agent’s triage decision — contributing internet-wide scanning context to a process that already draws from endpoint, identity, and cloud telemetry.
Charlotte AI’s agentic response can trigger workflows in Falcon Fusion SOAR, which means GreyNoise intelligence already available in your SOAR playbooks carries naturally into AI-driven triage. CrowdStrike’s mission-ready agents — covering detection triage, malware analysis, exposure prioritization, and threat hunting — are trained on years of expert decisions from Falcon Complete analysts. GreyNoise’s classification data adds internet-wide reconnaissance context to those workflows.
What Falcon Users Get
GreyNoise intelligence across the Falcon platform produces three specific outcomes:
- Higher-confidence triage — GreyNoise classification gives analysts a clear signal on which external IPs are known internet scanners and which warrant deeper investigation
- Contextualized alerts — every IP-based detection carries GreyNoise behavior, classification, and CVE context from the moment it fires
- Faster investigation and response — inline enrichment and automated SOAR workflows compress the time from alert to action
- Prioritized vulnerability response — CVE exploitation intelligence from GreyNoise’s sensor network informs which vulnerabilities are being actively targeted right now
Getting Started
The GreyNoise Foundry App is available on the CrowdStrike Marketplace for Falcon Next-Gen SIEM and Falcon Insight XDR customers. Installation takes minutes, and the daily automated indicator import requires no ongoing maintenance.
→ Install the GreyNoise Foundry App on the CrowdStrike Marketplace
Facts Only
GreyNoise operates a sensor network of over 5,000 sensors across 80 countries, analyzing up to one billion sessions daily.
The network tracks over 50 million IPs, classifying them as benign scanners, malicious actors, or unknown.
GreyNoise intelligence is now integrated into CrowdStrike’s Falcon platform, including Next-Gen SIEM, Falcon Fusion SOAR, and Charlotte AI.
The GreyNoise Foundry App, available on the CrowdStrike Marketplace, automates daily imports of classified IP data into Falcon Next-Gen SIEM.
The app includes pre-built templates for SIEM queries and SOAR workflows.
GreyNoise data is accessible via the `match()` function in SIEM queries, surfacing classification, observed activity, and exploited CVEs alongside event data.
Falcon Fusion SOAR can automate responses based on GreyNoise classifications, such as alerting on malicious IPs or prioritizing vulnerability patches.
Charlotte AI uses GreyNoise context in its agentic workflows to inform triage decisions.
The integration aims to reduce false positives by identifying known benign traffic (e.g., research scanners, CDN health checks).
CrowdStrike customers can install the GreyNoise Foundry App with minimal setup and no ongoing maintenance.
The solution is designed to accelerate triage, improve vulnerability prioritization, and enable faster response times.
Executive Summary
GreyNoise has integrated its internet-wide threat intelligence with CrowdStrike’s Falcon platform to help security teams prioritize alerts and reduce noise. The integration provides real-time classification of IPs—identifying benign scanners, malicious actors, and unknown threats—based on data from over 5,000 sensors analyzing a billion daily sessions. This intelligence is embedded across Falcon Next-Gen SIEM, Falcon Fusion SOAR, and Charlotte AI, enabling automated enrichment, contextualized alerts, and agentic workflows. Analysts can now filter out known benign traffic (e.g., research scanners) and focus on high-risk threats, while SOAR playbooks can trigger responses based on GreyNoise’s classifications. The collaboration aims to accelerate triage, improve vulnerability prioritization, and reduce false positives by leveraging direct observations of internet scanning behavior rather than third-party aggregations.
The integration is operationalized through the GreyNoise Foundry App, which automates daily indicator updates and provides pre-built templates for SIEM queries and SOAR workflows. Charlotte AI further incorporates GreyNoise data into its agentic reasoning, allowing AI-driven investigations to factor in internet-wide reconnaissance patterns. For CrowdStrike users, this means faster, more confident decision-making, with contextual IP data surfaced directly in alerts and investigations. The solution is available now via the CrowdStrike Marketplace, requiring minimal setup and no ongoing maintenance.
Full Take
**Steelman:** The integration of GreyNoise into CrowdStrike’s Falcon platform is a compelling step toward reducing alert fatigue in SOCs. By leveraging direct observations of internet scanning behavior—rather than relying on aggregated or inferred threat data—it provides a more empirical basis for triage. The automation of enrichment and response workflows addresses a real pain point: the overwhelming volume of alerts that often bury critical threats. The inclusion of GreyNoise in Charlotte AI’s reasoning loop also suggests a future where human analysts are augmented by context-aware automation, potentially improving both speed and accuracy.
**Pattern Scan:** The narrative leans heavily on the authority of scale ("one of the largest passive sensor networks") and the appeal of automation as a solution to complexity. While not inherently manipulative, this framing risks oversimplifying the challenges of threat intelligence—where context, intent, and evolving adversary tactics often defy neat classification. The emphasis on "known benign" versus "malicious" IPs could inadvertently reinforce a binary view of threat actors, ignoring the gray areas where legitimate tools are weaponized or where adversaries mimic benign behavior. No overt distortion or bad faith is detected, but the marketing tone ("dangerous," "frustrating") subtly amplifies the stakes to position the solution as indispensable.
**Root Cause:** The underlying paradigm assumes that the primary barrier to effective security operations is data overload, not structural limitations in detection logic or organizational workflows. This reflects a broader industry trend toward outsourcing contextual judgment to third-party intelligence feeds and AI-driven automation. The unstated assumption is that internet-wide scanning patterns are a reliable proxy for intent—a premise that may hold for commodity threats but could falter against sophisticated adversaries who adapt their tactics.
**Implications:** For human agency, this integration could empower analysts by reducing noise, but it also risks eroding their situational awareness if they over-rely on automated classifications. The cost-benefit balance favors large enterprises with the resources to integrate and fine-tune these tools, potentially widening the gap between well-resourced and underfunded security teams. Second-order consequences include the centralization of threat intelligence in a few dominant platforms, which could create single points of failure or bias if the underlying data models are flawed or manipulated.
**Bridge Questions:** How might adversaries adapt to evade GreyNoise’s classifications, and what safeguards exist to detect such evasion? What happens when benign infrastructure is misclassified, or when malicious actors hijack legitimate services? How does this integration account for the dynamic nature of threat actor behavior, where today’s "benign" scanner could be tomorrow’s exploit vector?
**Counterstrike Scan:** If this were part of a coordinated influence campaign, the playbook would emphasize fear ("dangerous," "accelerating threats") to create urgency, then position a proprietary solution as the only viable remedy. The actual content aligns with this pattern to some degree—framing the problem as existential and the solution as uniquely scalable—but stops short of outright manipulation. The focus on empirical data and interoperability with existing tools mitigates concerns, though the lack of discussion around limitations or edge cases is notable. No overt red flags, but the narrative’s confidence warrants scrutiny.