This is blog #2 in a series of 6 discussing AI Agents, the Query Security Data Mesh, and why normalized data is the differentiator in AI for Security Operations. As part of this blog series, we’re introducing the release of six mission-specific AI Agents now available in preview to Query customers. These agents are designed to assist with core SOC workflows, bringing targeted automation to key areas like triage, investigation, and response.

Background: The “Why” – AI Agents and the Security Data Mesh

In the ever-escalating arms race of cybersecurity, the sheer volume of data has become both our greatest asset and our most significant challenge. Security Operations Centers (SOCs) are inundated with a deluge of information from a multitude of disparate sources: endpoint detection and response (EDR) systems, security information and event management (SIEM) platforms, cloud monitoring tools, threat intelligence feeds, and more.

The average enterprise uses dozens of security tools, creating a fragmented data landscape that hinders a unified defense. According to a 2023 report by the Enterprise Strategy Group (ESG), 67% of cybersecurity professionals feel that the complexity of their security environment has increased significantly over the past two years, with data overload being a primary contributor.

This is where the powerful combination of AI-driven agents and a security data mesh, such as Query Federated Search, emerges as a game-changer. Federation doesn’t centralize data; instead, it provides a unified query layer that can access data where it resides. This approach eliminates the costly and time-consuming process of data duplication, transformation and normalization, providing a real-time, holistic view of your security posture.

When you empower these specialized AI agents with a security data mesh, you unlock a new level of operational efficiency and proactive defense. These are not general-purpose AI chatbots; they are niche, task-oriented agents designed to solve specific, high-value problems. Think of them as digital specialists on your team, each with a deep understanding of their domain, capable of executing complex workflows at machine speed.

The synergy is clear: the data mesh provides the unified data access, and the AI agents provide the specialized intelligence to act upon that data. This combination directly addresses the “too much data, not enough time” problem that plagues modern SOCs, enabling them to move from a reactive to a proactive stance.

The “What”: Deconstructing the Vulnerability Intelligence Agent

The Query Vulnerability Intelligence Agent is a prime example of a niche AI agent designed to tackle one of the most critical and labor-intensive tasks in a SOC: vulnerability management. At its core, the agent is an AI-powered analyst dedicated to extracting, enriching, and contextualizing vulnerability data to determine its real-world impact on your specific environment.

Core Capabilities and Architecture

The agent is built upon a robust framework that integrates a large language model (LLM) with a suite of specialized tools and a curated knowledge base. Let’s break down its key components:

  • LLM Engine: The agent utilizes a powerful LLM, such as Google’s Gemini, as its reasoning engine. This allows it to understand natural language queries, process complex information, and generate human-readable reports and recommendations.
  • Specialized Tools: The agent is equipped with a variety of tools that allow it to interact with its environment and perform specific tasks. These include:
    • CVE and Vulnerability Retrieval: Tools to query for vulnerabilities based on CVE ID, severity, CVSS vector, operating system, and various asset identifiers (IPs, MACs, hostnames, etc.).
    • Data Parsers: The ability to parse information from various sources, including URLs, PDFs, and JSON feeds, to extract vulnerability information from security advisories and reports.
    • CVE Enrichment: A dedicated tool to retrieve detailed information about a CVE, including EPSS (Exploit Prediction Scoring System) data, which is crucial for prioritizing vulnerabilities based on their likelihood of being exploited in the wild.
    • Federated Search Integration: This is the architectural linchpin that makes the agent uniquely powerful. Traditional AI models often rely on massive, centralized datasets—a model that is slow, expensive, and insecure for real-time security operations.

      The Vulnerability Intelligence Agent, however, operates on a security data mesh. This means it doesn’t require data to be moved or duplicated. Instead, it uses Query Federated Search to ask questions of data where it lives.

      This is the critical distinction: the agent can, in a single workflow, correlate a newly announced CVE with live asset data from your EDR, vulnerability scan results from your scanner, and network telemetry from your SIEM. It provides a real-time, comprehensive view of your actual environment, not a stale snapshot.

      This architectural choice is what grounds the agent’s intelligence in reality, making its findings immediately actionable and contextually relevant to your specific organization. It’s the difference between knowing a vulnerability exists and knowing how it affects you, right now.
  • Curated Knowledge Base: The agent has access to a specialized knowledge base containing critical documents and standards, such as:
    • EPSS Documentation: To understand and interpret exploit probability scores.
    • CISA’s SSVC (Stakeholder-Specific Vulnerability Categorization): For a framework-driven approach to vulnerability management.
    • NIST Special Publications: Including guidelines on patch management and incident response.
    • CVSS User Guides: For a deep understanding of vulnerability scoring.

This combination of a powerful reasoning engine, specialized tools, and a curated knowledge base allows the Vulnerability Intelligence Agent to function as a true digital expert in vulnerability management.

The “So What”: Strategic, Operational, and Tactical Value

The true value of the Vulnerability Intelligence Agent lies in its ability to transform the vulnerability management lifecycle. It moves teams from a state of being overwhelmed by vulnerability scans to a state of proactive, intelligence-driven remediation.

Use Case 1: The Daily Threat Briefing

Scenario: A SOC analyst starts their day with a new security advisory from a major vendor. The advisory details several new CVEs.

Traditional Workflow:
  1. The analyst manually reads through the advisory, identifying the CVEs.
  2. They then pivot to their vulnerability scanner to see if any of these CVEs have been detected in their environment.
  3. For each detected CVE, they have to manually research its severity, potential impact, and whether a patch is available.
  4. Finally, they have to cross-reference the affected assets with their CMDB to determine asset ownership and criticality, a process that can take hours.
Workflow with the Vulnerability Intelligence Agent:
  1. The analyst provides the URL of the security advisory to the agent.
  2. The agent automatically parses the advisory, extracts the CVEs, and enriches them with CVSS and EPSS scores.
  3. It then queries the data mesh to identify all affected assets in the environment.
  4. The agent provides a concise report detailing the vulnerabilities, their exploitability, the affected assets, and recommended remediation steps, all within minutes.

Use Case 2: The High-Stakes Incident Response

Scenario: A new, critical vulnerability is being actively exploited in the wild. The CISO wants to know the organization’s exposure now.

Traditional Workflow:
  1. A frantic, all-hands-on-deck effort begins to manually search for any indication of the vulnerability across all systems.
  2. Multiple teams are pulled into conference calls to correlate information from different tools.
  3. The process is slow, prone to errors, and generates a high level of stress.
Workflow with the Vulnerability Intelligence Agent:
  1. The SOC lead asks the agent: “Find all assets vulnerable to CVE-2025-XXXX.”
  2. The agent queries the data mesh and returns a list of all vulnerable assets, along with their owners and business context, in near real-time.
  3. The SOC team can then immediately move to contain the threat and apply patches, drastically reducing the organization’s risk exposure.

Use Case 3: Proactive Threat Hunting

Scenario: A threat intelligence report details the TTPs (Tactics, Techniques, and Procedures) of a new threat actor, including the specific vulnerabilities they are known to exploit.

Traditional Workflow:
  1. A threat hunter manually extracts the vulnerabilities from the report.
  2. They then have to craft complex queries for their various security tools to search for any signs of these vulnerabilities or related activity.
Workflow with the Vulnerability Intelligence Agent:
  1. The threat hunter provides the report to the agent.
  2. The agent not only identifies the vulnerabilities but can also help generate FSQL (Federated Search Query Language) queries to hunt for related indicators of compromise across the entire data mesh.
  3. This allows the threat hunter to proactively search for signs of the threat actor before a full-blown incident occurs.

The “Now What”: The Future is Specialized

The Vulnerability Intelligence Agent is more than just a tool; it’s a new way of working. It represents a shift towards a more intelligent, automated, and proactive approach to cybersecurity. By combining the power of specialized AI agents with the unified data access of a data mesh, security teams can finally start to get ahead of the threats.

The key takeaway for cybersecurity engineers and SOC analysts is this: the future of security operations is not about replacing human experts with AI. It’s about augmenting human expertise with specialized AI agents that can handle the heavy lifting of data analysis and correlation, freeing up human analysts to focus on what they do best: making critical decisions and taking decisive action.

As you look to mature your security operations, consider the power of this new paradigm. The journey begins with understanding that you don’t need a single, monolithic AI to solve all your problems. You need a team of specialized agents, each an expert in their own domain, working together on a unified data foundation. The Vulnerability Intelligence Agent is just the beginning.

Query customers interested in accessing the Vulnerability Intelligence Agent should contact their Query Customer Success Manager. Reach out if you’re not yet a Query customer interested in learning more.