The Model Context Protocol (MCP) is emerging as a standard for enabling large language models (LLMs) to interact with external services through structured, declarative requests. MCP provides a consistent way for agents to describe what they want to do—such as retrieve a customer record or initiate a workflow—without being tightly coupled to backend implementation details.
The protocol defines how LLMs interact with external systems, but it doesn’t prescribe how those systems are exposed, governed, or secured. Most implementations include MCP servers, which expose services in an MCP-compliant format, and MCP gateways, which help route and manage agent requests across servers.
As organizations adopt MCP, they're increasingly encountering challenges related to trust and control. A recent CyberPress report highlighted how improperly secured MCP servers, especially when exposed publicly, can be abused by unauthorized agents. These risks don't stem from the protocol itself, but from the assumption that all MCP servers are safe to trust by default.
The takeaway isn’t that MCP is insecure—it’s that assuming any MCP-compliant server is safe by default can create real exposure.
Why MCP gateways exist—and where they fall short
To help manage complexity, many organizations implement MCP gateways as a way to centralize access across multiple MCP servers. These gateways typically:
Provide a single endpoint for agents
Route requests to distributed MCP servers
Enforce basic access control and rate limiting
Offer service discovery or metadata lookup
This structure is useful in theory. It abstracts away multiple backends and gives AI agents a simpler, unified interface.
But in practice, most MCP gateways don’t address the deeper issues:
They assume that each underlying MCP server is properly secured, governed, and maintained.
They treat service orchestration as the problem, not data access, policy enforcement, or semantic fidelity.
They still require organizations to build, deploy, and maintain dozens of individual MCP servers, each connecting to different backend systems.
Open frameworks like Supergateway and MintMCP are valuable for routing across independently deployed MCP servers, and some also support capabilities like LLM-to-LLM coordination. CData Connect AI doesn't aim to replace that class of routing infrastructure. Instead, it provides query routing and semantic access across a wide range of enterprise data sources—all through a single managed MCP interface. For organizations focused on governed access to enterprise data, Connect AI addresses a different, and often more foundational, layer of the MCP stack.
A gateway can route requests. It can’t fix what’s behind them.
The hidden costs of building and managing your own MCP stack
Many teams start their MCP journey with a few simple services: an agent that queries a knowledge base, a script that calls an API, or a prototype interface to a SaaS platform. But scaling this into a real production environment means building—and securing—everything behind the gateway.
That includes:
MCP servers for every system you want to expose (CRM, ERP, HRIS, data warehouse, etc.)
Authentication, authorization, and audit logging for every request
Metadata modeling so agents understand object relationships and field semantics
Context tracking across workflows and sessions
High-availability infrastructure with monitoring, load balancing, and failover
As the number of agents, use cases, and data systems grows, so does the surface area for mistakes. A single misconfigured server can leak sensitive data or allow unintended actions.
Worse, most open-source MCP servers don’t come hardened out of the box. They assume trusted networks, cooperative agents, and prototype scenarios—not adversarial environments or compliance audits.
MCP gateway security risks in the wild
Security researchers and platform teams are already surfacing serious vulnerabilities in real-world MCP stacks:
Open MCP servers deployed without authentication controls
Static tokens and long-lived credentials reused across services
Lack of isolation between agent contexts
Gateway tools that rely on user-submitted URLs to connect to external servers—with no identity verification
These issues introduce both technical and supply chain risks. A malicious or compromised MCP server behind your gateway can act as a backdoor to your data or infrastructure. Without proper governance, it’s difficult to track which agents accessed what, when, and under what policy.
Enterprises can’t afford that kind of exposure.
What enterprise AI really needs: a secure, governed MCP platform
To adopt MCP safely, enterprises need more than just routing. They need infrastructure that’s built for scale, hardened for production, and governed by design.
That means:
Secure defaults, not optional safeguards
Centralized governance over who can access what, under which conditions
Semantic fidelity, so agents can interpret source systems accurately
Policy enforcement at the access layer—not just at the edge
Audit trails for every interaction
It also means removing the burden of building and maintaining every server, connection, and context manager internally.
CData Connect AI: the first managed MCP platform
CData Connect AI was built for exactly this purpose: to replace ad hoc MCP stacks with a fully managed, secure-by-default MCP platform.
It acts as both:
An MCP gateway: routing, governing, and managing requests across services
And an MCP server layer: exposing enterprise systems to LLM agents with semantic intelligence and full-fidelity access
Key platform capabilities
Live, governed access to 350+ enterprise systems: Prebuilt connectors to systems like Salesforce, SAP, Snowflake, NetSuite, and Workday—delivered as MCP-compliant endpoints.
Secure-by-default identity and access controls: Enforced authentication, role-based access, row/field-level policy support, and continuous monitoring.
Full metadata modeling: Agents get structured access to objects, fields, relationships, and custom schema—no need for prompt engineering hacks.
No infrastructure to manage: CData hosts the platform. We handle scalability, upgrades, patching, credential rotation, and observability.
Built for real enterprise needs: Not just demos. Not just tool routing. Connect AI brings production-grade semantics, security, and compliance to every request.
Trust your AI stack to secure infrastructure
The promise of MCP is real: modular, agent-based access to tools and data. But the cost of getting the implementation wrong is too high—especially when real data, users, and decisions are on the line.
Most MCP gateways are only as strong as the weakest server behind them. Without a secure platform, you're left managing a distributed patchwork of agents, tokens, services, and logs.
CData Connect AI gives you the control you need. It’s the fastest, safest, and most governed way to adopt MCP across your enterprise.
Start your free trial today and bring AI agents safely into your enterprise data environment.
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