GreyNoise unveils MCP Server to power AI-driven SOC workflows

GreyNoise Intelligence introduced the GreyNoise Model Context Protocol (MCP) Server to enable MCP-compatible LLMs and agents to query GreyNoise APIs directly, providing real-time, actionable threat intelligence for AI agents.

“AI Agents represent a major shift in cybersecurity, moving beyond simple workflow automation to autonomous reasoning, planning, and executing. This will radically change every security workflow, from case management to full playbook automation,” said Ash Devata, CEO, GreyNoise. “The GreyNoise MCP Server provides a quick and easy way for AI agents to access highly accurate, near-real-time threat intelligence required for all agentic SOC workflows.”

Agentic AI promises to augment the SOC, by enabling more proactive protections and accelerating the time required to detect, respond, and recover. Instead of just following predefined playbooks, agents can adapt in real time by connecting multiple actions as a situation changes. This will allow the SOC to become more proactive and dynamic, helping defenders keep up with the speed of automated attacks.

The GreyNoise MCP Server provides AI models and agents with access to accurate, real-time threat intelligence, so they can remain grounded in trusted, up-to-date data as they reason about security issues. Through MCP, agents can query GreyNoise in real-time to determine whether an IP is benign, malicious, suspicious, or unknown, and to identify vulnerabilities actively being exploited in the wild.

This capability allows AI-driven SOC workflows to reduce false positives, accelerate investigation and response times, prioritize remediation of real threats, and automate defensive actions such as dynamic blocking.

By embedding GreyNoise intel natively into agent reasoning, the MCP Server ensures that AI agents operate with the same accurate, timely, and contextual data trusted by human analysts—unlocking both speed and precision at scale for:

• Noise reduction & alert triage. Agents can cross-reference alerts against live threat intel to separate benign from malicious traffic. This cuts false positives and prevents analysts from wasting cycles on irrelevant activity.
• Automated threat investigation. Agents can pivot across threat data without manual analyst queries. They arrive at the correct conclusion with proper supporting context within seconds.
• Prioritized vulnerability remediation. With real-time intel, agents can identify which vulnerabilities are actively exploited in the wild versus theoretical risks. Security teams can patch what’s being attacked in the moment, aligning resources to real-world threats.
• Dynamic response & blocking. Agents can feed intel into firewall, IPS, and SOAR systems to block malicious IPs or quarantine compromised assets either with or without humans in the loop.
• Continuous monitoring and hunt support. Agentic AI can monitor intel feeds 24/7 and alert when an organization’s tech stack is at greater risk. Agents can proactively suggest hunt queries or detection rules based on emerging threats.
• Analyst augmentation, not replacement. Agents draft reports, summarize intel, and highlight anomalies, giving analysts quality drafts so they can focus on judgment calls. This reduces burnout and allows SOC teams to scale effectively.

“For AI to be truly effective for security, it requires a foundation of timely and reliable data,” said Bob Rudis, VP of Data Science and Research, GreyNoise Intelligence. “With accurate, real-time intelligence from GreyNoise, security teams leveraging agentic SOC technologies can make the right decisions even faster. In today’s world where mass exploitation is fast, cheap, and automated, speed matters.”