In Security Operations? You Must Understand the Power of MCP

Amit Sheps
Amit Sheps Director of Product Marketing LinkedIn

If your days are spent tracking down vulnerabilities in your organization’s websites, APIs, or cloud services—think exposed databases or weak server headers—you know the grind. Manually scanning assets, validating issues, and deploying fixes is time-consuming and error-prone. Enter agentic AI: intelligent tools that automate these tasks, verify fixes live, and let you focus on strategy. With Anthropic’s Model Context Protocol (MCP), this isn’t just for big-budget teams anymore—it’s within reach for every security professional.

Back in 2023, Microsoft Security Copilot set the stage for AI in security operations. It promised to streamline tasks like analyzing threats across systems, triaging alerts, and suggesting fixes using natural language. Security teams could ask, “What’s wrong with this server?” and get actionable insights. But for many, the reality fell short. Integration with diverse tools was complex, and costs could be prohibitive for smaller organizations or those who didn’t go all-in with the Microsoft security stack. While groundbreaking, it wasn’t accessible for many security operations teams.

MCP: Democratizing Agentic AI

Fast forward to 2025, and Anthropic’s MCP is changing the game. Launched in November 2024, MCP is an open standard that lets AI models securely connect to external tools, APIs, and data sources. It’s like giving your AI a passkey to your security ecosystem, allowing it to fetch data, validate issues, and take actions—all with human oversight. Unlike proprietary systems, MCP is simple to implement and widely adopted.

With thousands of MCP servers already live, numerous IT/security vendors (and countless community developers) are building MCP integrations for everything from vulnerability scanners to ticketing systems. You can even create your own MCP server to expose your APIs, making it a flexible, low-cost way to build your own “Security Copilot” tailored to your needs.

Tools You’re Already Using

No need for fancy new software—MCP works with tools you likely already have. Claude Desktop and GitHub Copilot in VS Code support MCP out-of-the-box. For example, you can chat with Claude on Claude Desktop and have it interact with MCP servers at no extra cost, making it a breeze to start automating. These tools let AI agents interact with your IT and security stack, whether it’s pulling data from a vulnerability tool or opening a Jira ticket.

A Real-World Example: IONIX and MCP

At IONIX, we’ve experimented with [MG1] a first internal version of the IONIX MCP server, that can let security teams offload some external exposure management workflows to AI models.

For example, a security engineer might use VSCode and GitHub Copilot in agent mode to give the agent a high-level instruction to simply “handle pending issues related to website www.mywebsite.com” and go grab a cup of coffee while the agent handles the following workflow (without the engineer needing to spell anything out):

  1. The agent pulls all the website’s pending issues from the IONIX MCP server (e.g. bad server headers or problematic inclusion of some Javascript from a hijackable 3rd party domain).
  2. The agent checks if the issues are still there by connecting to the site via another MCP server (e.g., Fetch).
  3. Using a browser-based MCP server, the agent captures screenshots or interacts with web elements to log the site’s behavior.
  4. The agent suggests code or server configuration changes through GitHub Copilot, editing files with the engineer’s approval.
  5. After approval, the agent deploys fixes to a staging environment, rescans with IONIX MCP to confirm the issues are gone, and checks the site’s functionality via the browser MCP again.
  6. For tasks needing review, the agent creates a Jira ticket with detailed issue info, reproduction steps, and fixes, formatted to your team’s standards.

Figure 1: The GitHub Copilot agent using the IONIX MCP servers along with other MCP servers to analyze and re-validate failed web security tests

Figure 2: The Cline VSCode extension using the browser use tool to visit the website and interact with it to get a sense of its functionality / run sanity tests.

Figure 3: A CSV table created by the agent to summarize its work.

For forward-thinking security teams, a workflow like this can be a reality today, potentially saving teams hours on repetitive tasks while keeping humans in the loop.

Why This Matters for External Exposure

Protecting public-facing assets is a race against time and the attackers who exploit misconfigurations in seconds. MCP-powered agentic AI gives you:

  • Speed: Automate scans and fixes, cutting tasks from hours to minutes.
  • Precision: Validate issues live to avoid false positives and ensure fixes work.
  • Flexibility: Integrate with your existing tools, from IONIX to custom APIs and 3rd party systems, without vendor lock-in.
  • Control: Approve all changes, keeping you in charge.

With the scarcity of cybersecurity staff, teams are stretched thin. AI bridges that gap, letting you get more done with less effort.

The Future Is Now

Agentic AI isn’t a distant promise—it’s here, and MCP makes it accessible to every security professional. Whether you’re securing websites, APIs, or cloud services, you can now build a smarter, faster workflow without the complexity or cost of yesterday’s solutions. Start small, automate big, and take control of your public-facing assets today.