MCP Servers: The New AI Superpower Transforming Cybersecurity.
Learn how MCP is enabling AI agents to safely interact with firewalls, SIEMs, APIs, identity infra and other systems across the stack.
MCP servers are rapidly emerging as the connective tissue between AI and cybersecurity tools. By standardizing how AI agents interact with systems like firewalls, SIEMs, and cloud platforms, MCP is unlocking faster investigations, automated responses, and secure workflows. This blog breaks down what MCP is, why it matters, and how vendors like Palo Alto, Wiz, Google, and others are putting it into action.
Lets explore this further.
What Are MCP Servers?
Model Context Protocol (MCP) is an open standard (originating from Anthropic in late 2024) that lets AI systems like large language models securely connect to external tools and data sources.In simple terms, an MCP server acts as a bridge or “universal adapter” between an AI assistant and other applications, databases, or services. This is often compared to a USB-C port for AI – it provides a standard interface so that any AI model (the “MCP client”) can plug into various tools through a common protocol
By using MCP, an AI-powered assistant is no longer limited to its built-in knowledge; it can pull in real-time context, query databases, invoke security tools, or update systems securely, all through the MCP server. For example, instead of hard-coding a custom integration for each tool, an AI agent can talk to an MCP server which exposes functions (APIs) of a tool in a standardized way. The result is that AI gains the ability to perform actions on behalf of users – like retrieving a cloud configuration, scanning a log for threats, or creating a support ticket – by asking the MCP server to do it.
The MCP design follows a client–server model The AI assistant (MCP client) maintains connections to one or more MCP servers (often called tools or plugins in other contexts). When a user asks the AI a question or gives a task, the MCP client presents the AI with a list of available tool capabilities (from the MCP servers it’s connected to). The AI’s underlying model then decides if any tool is needed and how to use it. If yes, the AI (via the MCP client) calls the MCP server, which executes the action and returns results Finally, the AI uses those results to craft a helpful answer for the user. In summary, the MCP server is where the actual work happens (retrieving data, executing an API call, etc.), while the AI (MCP client) figures out which work to request and interprets the results. Finally, the AI uses those results to craft a helpful answer for the user. In summary, the MCP server is where the actual work happens (retrieving data, executing an API call, etc.), while the AI (MCP client) figures out which work to request and interprets the results.
Src: https://modelcontextprotocol.io/introduction
This process is illustrated by a simple flow:
1) A user asks something;
2) The MCP client supplies the query and available tool info to the LLM, which picks an appropriate tool and parameters ;
3) The client sends that to the MCP server;
4) The MCP server performs the task and returns output;
5) The AI presents the output to the user . The beauty of MCP is that it makes these steps uniform for any tool – be it a database lookup or a firewall rule change – as long as there’s an MCP server for it
Without MCP, connecting an AI like a chatbot to enterprise systems is tedious and custom. MCP establishes a common language for integration so that security teams and developers don’t have to reinvent connectors for every model or product. If a tool offers an MCP server, any AI that supports MCP can use that tool in a plug-and-play fashion.
This dramatically lowers the barrier to building and using AI-driven workflows, because one MCP server can be reused by many different AI assistants and platforms . It also means organizations can swap out the underlying AI model or host (Claude, ChatGPT, Google Gemini, etc.) without losing the integrations – MCP is model-agnostic. In short, MCP servers let AI systems safely “reach out” beyond their own brains and interact with the outside world in a controlled, standardized way.
MCP introduces a new layer in your architecture that you’ll want to design and secure carefully. At a high level, you’ll deploy MCP servers as lightweight adapters on top of your tools and data. These servers expose specific actions (often called “tools” in MCP terminology) via a standardized schema. Meanwhile, your AI system (the MCP host/client) maintains connectivity to these servers and orchestrates calls based on user prompts
Key architectural components:
MCP Host & Client: The AI application or agent (host) that needs external data runs an MCP client internally. This client is responsible for discovering available servers and relaying information. For example, a security chatbot in an analyst’s console would have an MCP client that knows how to talk to various servers (SIEM, EDR, etc.) .
MCP Server: A program or service wrapping a particular system or domain (firewall API, cloud platform, ticketing system, database, etc.), presenting its functions in a standardized way to any MCP client . The server advertises what “tools” or actions it can do (e.g., fetch incident logs, list cloud VMs, block an IP address) and handles requests from the AI. Think of it as an API translator that speaks both the tool’s native language on one side and the MCP protocol on the other.
Connections: MCP supports different connection modes (local, remote, streaming, etc.). Originally, many MCP servers ran locally (on the same machine as the AI) for simplicity, but now there’s momentum toward remote MCP servers accessible over secure HTTP(S) . Remote MCP servers allow cloud-based deployment (and multiple users or AIs to share one server), but they introduce extra security considerations (network exposure, authentication) that must be managed.
MCP Servers in Cybersecurity: Key Vendors
MCP’s rapid rise has led many cybersecurity vendors to roll out their own MCP-compatible servers.
These allow their products and data to plug into AI-driven workflows with minimal fuss. Below is a list of prominent vendors (and new products) offering MCP server capabilities, and what they bring to the table:
Palo Alto Networks – Prisma AIRS MCP Server (preview): An MCP server focused on embedding security checks into AI agents. It integrates with Palo Alto’s AI Runtime Security (AIRS) platform to scan AI workflows for malicious content, phishing, data leakage, and policy violations in real time . This server acts as a security layer, allowing AI assistants to safely use enterprise data by filtering out threats (e.g. prompt injection attempts, malware links) and enforcing compliance (DLP, toxic content blocks). Launched: Public preview as of June 2025, available via PyPI/GitHub .
Wiz – Wiz Model Context Protocol Server (preview): Wiz (a cloud security platform) built an MCP server to plug its security graph and tools into any AI assistant . The Wiz MCP Server lets an LLM query cloud configurations, vulnerabilities, and risk insights from Wiz in plain English – and even trigger actions like opening code in an IDE or issuing cloud commands for remediation . It powers Wiz’s own Mika AI assistant and enables integrations in IDEs (for developers to fix issues directly) and SOC workflows (for analysts to investigate and respond faster). Launched: April 2025 in preview, including an AWS Marketplace listing for easy deployment.
Google Cloud – Security MCP Connectors (open-source): Google has released MCP server implementations for its security portfolio – Chronicle Security Operations (SIEM/SOC platform), Mandiant Threat Intelligence, and Security Command Center (SCC) for cloud posture . These are reference Python MCP servers (Apache 2.0 licensed) that anyone can deploy to let AI agents query security alerts, pull threat intel on IoCs, or get cloud vulnerability findings from Google’s tools . Google’s goal is to encourage AI-driven workflows across products: an analyst’s AI helper could seamlessly gather context from Chronicle, Cloudflare, CrowdStrike, Okta, Wiz, etc., all via MCP . Launched: April 2025 (RSA Conference), code available on GitHub
CrowdStrike – Falcon Platform MCP Server (in development):
CrowdStrike announced a collaboration with Google to develop an MCP server for the CrowdStrike Falcon platform . This will allow AI systems to interface with Falcon’s EDR/XDR capabilities – for example, an AI copilot could retrieve endpoint detections, check IOC statuses, or isolate a device via Falcon, all through standardized MCP calls . CrowdStrike emphasizes secure integration: by authoring its own MCP server, it can ensure proper authentication and guardrails so customers can safely harness AI with their sensitive security data . Status: Announced April 2025 – not generally available yet, but demonstrates CrowdStrike’s commitment to an open, AI-driven security ecosystem.
Cloudflare:
Cloudflare MCP (Remote): Cloudflare stepped forward as one of the first to offer remote MCP servers hosted on its global network . In April 2025 they made it possible to deploy MCP servers on Cloudflare Workers (serverless platform), removing the need for local hosting. They also launched 13 ready-made MCP servers for common tasks and Cloudflare services – including servers to fetch Cloudflare analytics, query DNS records, manage Zero Trust policies, and even a CASB integration to find SaaS app misconfigurations . All of these can be plugged into AI agents like Anthropic’s Claude with a few clicks. A key feature of Cloudflare’s approach is built-in OAuth support for third-party servers, allowing users to safely grant an AI agent access to, say, their email or GitHub without exposing credentials .
Okta Identity MCP Integration (planned):
Identity provider Okta joined Google’s MCP initiative to enable AI access to identity and access management data . While full product details aren’t public, the idea is an MCP server that allows an AI assistant to pull user risk scores, authentication logs, and perform identity actions (like disabling a compromised account) via Okta . This means a SOC analyst’s AI agent could, for instance, detect a suspicious user session (via Okta signals) and automatically trigger an Okta workflow to lock the account – all in natural language. Okta’s leadership has underscored the importance of interoperability so that AI can bridge across “best-of-breed solutions” seamlessly.
Orca Security (a cloud security platform) built an MCP server prototype that lets Claude and other AI agents query Orca’s cloud telemetry and findings without using the UI .
Netskope (SSE vendor) has indicated MCP servers will be key in its NetSkope AI ecosystem, for example to manage Netskope’s Secure Private Access configurations via AI assistants . Even network and automation firms like Cisco and Itential are looking at MCP to drive network operations by AI (for instance, Cisco published a reference architecture for MCP servers in NetDevOps) – showing that MCP’s influence extends to adjacent domains like networking and IT operations . The focus across the board is leveraging MCP to break down silos between tools and enable AI-driven automation in a secure, standardized way.
It’s important to note that while MCP servers solve integration and automation challenges, they introduce a new challenge: securing the MCP ecosystem itself. The industry is aware of risks like malicious MCP servers or misuse of powerful actions . Thus, a parallel effort is ongoing to establish best practices for MCP security – from code signing and source verification , to sandboxing servers, to robust authentication. Many vendors (and community groups) are contributing to improving the MCP specifications for enterprise needs . The good news is that awareness is high, and early adopters are building security in from the start. For example, Microsoft and OpenAI’s support for MCP comes with guidelines around approved tools, and companies like SentinelOne have published guidance on using MCP safely (such as avoiding “MCP sprawl” and monitoring AI-tool interactions).
In summary, MCP servers are helping cybersecurity teams work smarter, not harder. They break down data silos, inject real-time intelligence into AI, and let machines handle routine tasks – all under a framework where security teams can maintain control. We’re seeing use cases from automated SOC triage to self-healing cloud infrastructure emerge. As one security researcher put it, MCP is a gamechanger, heralding a new era of intelligent SecOps . With major vendors on board and robust security measures in development, MCP servers are poised to become a foundational element of secure, AI-driven workflows in the cybersecurity industry.
Sources:
https://mcp.so/ : Find awesome MCP Servers. Build AI Agents quickly.
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