Model Context Protocol Server: The Universal Remote for AI Agents

The Model Context Protocol (MCP) is emerging as a foundational interoperability layer for agentic AI, embraced by major platform providers. MCP simplifies how AI models connect to external tools and data. Think of it as a universal remote for security platforms: Instead of building fragile, one-off integrations, MCP allows AI to discover and use capabilities dynamically.

For SIEM and detection providers, this shift is significant. MCP makes security data “agent-ready,” giving AI-powered systems the context they need to enrich detections, improve signal fidelity, and accelerate investigations. Just as cloud computing reshaped how organizations stored and shared data, MCP is positioned to do the same for how AI accesses and exchanges it.

This blog explores how various organizations may use MCP servers to enhance detection workflows, how they enable other platforms to use security data, and what this means for security vendors like Exabeam as they navigate the next wave of AI-driven interoperability. We will cover key use cases and invite you to evaluate the initial phase of the Exabeam MCP server.

What is an MCP server?

An MCP server acts as an air traffic controller for AI systems, guiding many different tools and data sources to work together in a coordinated and predictable way. Instead of needing unique integrations for each connection, MCP gives AI agents a common language to discover and use external capabilities. This removes much of the engineering overhead tied to building and maintaining custom APIs and shifts the focus to improving outcomes.

For security platforms, this is a meaningful change. SIEM environments depend on dozens or even hundreds of data feeds, a process that traditionally requires significant integration work. MCP servers streamline this by serving as a universal interface between AI agents and security tools. This makes it easier to add new types of context, onboard new data sources, and adapt to new use cases without extensive rework.

As MCP adoption grows, this kind of interoperability will make security data more fluid and useful. Rather than staying locked inside a single platform, critical context can move across systems in real time, supporting faster detections and investigations, and more efficient SOC operations.

The Inverse: When Other Platforms Use Security Data

MCP is not just a one-way street for pulling context; it also makes it easier for other systems to access and use security data. Traditionally, external tools need custom-built APIs or middleware to tap into SIEM signals. MCP changes that by providing a standard way for AI agents, to consume security data directly.

This opens the door for more connected and flexible security ecosystems. For example, an investigation copilot agent could query SIEM data to add context to a case, or a business risk dashboard could surface relevant security findings without manual handoffs. Partners and internal teams can build around the same interface, reducing friction and accelerating adoption.

Another clear use case is automated threat hunting. With MCP, an AI agent can launch targeted hunts across security data without requiring predefined integrations or custom scripts. For example, if a threat intelligence feed identifies a suspicious domain, an agent can use MCP to pull related authentication logs, endpoint activity, and network traffic in seconds. The agent can then correlate that data and flag patterns for further investigation.

This dynamic hunting removes the manual step of pivoting between tools or waiting for engineering teams to build new connectors. Analysts can focus on validating findings and responding, rather than chasing data across multiple systems. Over time, this approach can make proactive threat hunting faster, more consistent, and easier to scale across large environments.

The result is a two-way value exchange. MCP gives detection platforms richer inbound context and enables them to project outbound value across a broader range of tools and environments. This is what turns a SIEM from a data destination into a true ecosystem hub.

Agentic AI: Opportunity and Risk

MCP is closely tied to the rise of agentic AI, which brings both new capabilities and new security challenges. By giving AI agents structured, real-time access to security data and tools, MCP makes it possible for these agents to automate investigations, enrich detections, and accelerate threat response at a scale difficult to achieve manually. This creates opportunities for faster triage, more precise prioritization, and stronger operational resilience.

At the same time, this increased power comes with greater responsibility. MCP endpoints effectively act as privileged access paths into sensitive systems. If they are not secured, monitored, and governed through agent-to-agent (A2A) protocols, they can become an attractive target for attackers or a pathway for unintended actions. Strong authentication, access controls, and detailed audit logging are essential to keeping these interfaces trustworthy.

The organizations that benefit most from MCP will be the ones that pair innovation with discipline. Treating MCP with the same rigor as any other critical security boundary ensures that agentic AI can accelerate defense rather than create new security gaps. This balance of enablement and control will be a defining factor in successful MCP adoption.

Lessons from the Cloud Compute Playbook

The security industry has been here before. When cloud computing first emerged, many organizations viewed it as risky and unproven. Early adopters worried about control, visibility, and data exposure. Over time, as governance frameworks matured and best practices became standard, cloud adoption became the norm. MCP is following a similar early-stage trajectory.

Just as the cloud required shared responsibility models and strong identity controls, MCP demands thoughtful security architecture. The protocols themselves are not the hard part. The real challenge lies in how they are implemented, secured, and scaled in complex environments.

Security teams that adopt MCP early and build strong governance foundations will have an advantage. They will be able to innovate faster, integrate more broadly, and set their own security standards rather than reacting to those set by others. MCP, like the cloud, will reward early strategic investment combined with operational discipline.

Operational and Cost Considerations

While MCP can make security data more accessible and useful, it also introduces practical considerations. Because MCP acts as an active access layer, it can increase the amount of compute, network traffic, and data movement tied to enrichment, hunting, and response workflows. This can have a measurable impact on operating costs, especially in environments with large data volumes or frequent AI-driven queries.

Security and governance are another major factor. Each MCP endpoint represents a new access surface that must be authenticated, authorized, and monitored. Without clear guardrails, there is a risk of overexposure or accidental overuse of resources. Strong policy enforcement, usage quotas, and detailed audit logging can help contain both security and cost risks.

Some vendors frame MCP with narratives around cost predictability. This can be true, but only with disciplined design. Proactive planning for cost management and security is key to making MCP sustainable and powerful.

Strategic Implications for Security Vendors

MCP signals a shift in how security vendors will build, integrate, and compete in the next phase of AI-driven operations. For years, SIEM and detection platforms have focused on building proprietary APIs and connectors. MCP changes that dynamic by offering a shared, open standard for tool discovery and interaction. In this model, interoperability becomes an expectation rather than a differentiator.

For vendors, the strategic question is how to participate in this emerging ecosystem. Those who move early can position themselves as central nodes within the MCP network, where their data and detections become context for others. This can strengthen partnerships and create new value for customers, especially as AI-driven tools rely more heavily on live, shared context.

At Exabeam, we are embracing this shift with a practical, tiered approach. Our current integration can be thought of as “MCP for Developers.” It enables AI assistants to interact directly with the Exabeam API surface to:

• Browse and query all API endpoints
• Retrieve detailed parameter and schema information
• Generate code snippets for faster integration
• Assist in troubleshooting and development workflows

This initial offering is ideal for developers and partners building custom integrations or automations. It also lays the groundwork for our future, purpose-built “MCP for Analysts/Operations,” which will expand these capabilities to support live queries and interactive operational workflows. This positions MCP to evolve from a developer tool into a core component of the analyst experience.

MCP also challenges vendors to rethink their integration and pricing strategies. With more open data movement, value will shift from owning data pipelines to delivering outcomes such as enriched detections, reduced triage time, and adaptive automation. Vendors that align their platforms to operate in an MCP-connected world will be better equipped to lead in an increasingly agentic, interconnected market.

The Future Is Interoperable

MCP represents a turning point for how AI, data, and security operations work together. What started as a technical framework for connecting models to tools is evolving into a foundation for more adaptive, collaborative, and intelligent security ecosystems. Just as cloud adoption redefined scalability, MCP is redefining interoperability by making security data instantly usable across platforms and agents.

For detection and response platforms, the benefits are clear. MCP makes data more dynamic, detections more context-aware, and investigations faster. It can also expand the reach of that data, enabling other tools and partners to draw value from it in new ways. The result is a more efficient, connected, and proactive defense.

The key is to adopt and build MCP servers with a security-first mindset. Security teams that balance innovation with control will help shape how this standard matures across the industry. Those who begin preparing now will be ready for the next phase of agentic AI, where making data truly agent-ready will define leadership in the years ahead. If you would like to evaluate the Exabeam MCP server to see how you could expand interoperability, download it now to see what you can do with it in its current version.

To see how AI agents can shape the future of SOC operations, read our guide, Eight Ways Agentic AI Will Reshape the SOC.