Securing MCP: Making the Model Context Protocol Enterprise-Ready

The Promise of the Model Context Protocol

Anthropic's Model Context Protocol (MCP) represents a groundbreaking advancement in how AI assistants connect to data sources and tools. As Anthropic describes it, MCP is "a new standard for connecting AI assistants to the systems where data lives," enabling AI models to produce better, more relevant responses by overcoming data isolation challenges.

The protocol addresses a fundamental problem in the AI landscape: current systems are constrained by their isolation from data, and every new data source requires its own custom implementation. MCP provides a universal protocol that replaces multiple custom integrations, supporting secure data connections while enabling AI systems to maintain context as they move between different tools and datasets.

Early adopters like Block, Apollo, Zed, Replit, Codeium, and Sourcegraph are already integrating MCP, with pre-built servers available for systems like Google Drive, Slack, GitHub, Git, and Postgres. The potential is enormous—but there's a critical challenge that's preventing widespread enterprise adoption.

The Security Gap: Current MCP Implementation Challenges

While MCP's vision is compelling, current implementations expose significant security vulnerabilities that make it risky for enterprise deployment. Organizations attempting to implement MCP are encountering three critical pain points:

These aren't theoretical concerns—they're blockers preventing security-conscious enterprises from adopting MCP at scale. Financial services firms, healthcare organizations, and other regulated industries simply cannot deploy systems with these vulnerabilities, no matter how powerful the underlying capabilities.

Introducing Strongly.AI's Secure MCP Hosting Solution

At Strongly.AI, we recognized that MCP's potential could only be realized with enterprise-grade security. That's why we've built a comprehensive MCP server hosting platform that addresses these critical security gaps while making MCP accessible through our intuitive drag-and-drop workflow builder.

Over 100 Custom-Built MCP Servers

We've built and deployed over 100 enterprise-ready MCP servers, each running in isolated Kubernetes containers with enterprise-grade security controls. This extensive library covers the most commonly needed integrations—from databases and cloud storage to development tools and business applications—all pre-configured and security-hardened.

Our containerization approach provides several key benefits:

• Isolation: Each MCP server runs in its own Kubernetes pod, preventing cross-contamination and limiting the blast radius of any potential security incident.
• Consistency: Containerized deployments ensure consistent behavior across environments and make it easy to replicate secure configurations.
• Scalability: Our Kubernetes orchestration automatically scales servers based on demand, ensuring performance without over-provisioning resources.
• Auditability: Every container interaction is logged and monitored, providing the audit trail required for compliance.
• Cloneable & Customizable: Users can clone any MCP server and customize it to their specific needs, with changes stored securely in isolated S3 directories.

Just-In-Time Credential Management

Rather than storing static API keys in configuration files, Strongly.AI implements just-in-time (JIT) credential provisioning. When an AI agent needs to access a resource through an MCP server, credentials are:

1. Dynamically Generated: Short-lived tokens are created on-demand for the specific task at hand.
2. Scoped to Minimum Permissions: Credentials grant only the exact permissions needed for the immediate operation.
3. Automatically Revoked: Tokens expire immediately after use or after a short time window, eliminating the risk of credential theft.
4. Never Persisted: No credentials are stored in configuration files or long-term storage.

Fine-Grained Access Control with Multi-Tenancy

Strongly.AI's MCP implementation includes comprehensive fine-grained access control that goes far beyond simple API key management. Our platform supports true multi-tenancy, where a single MCP server instance can securely serve multiple users, each with their own credentials and permissions:

• Workflow-Level Permissions: Each workflow can define its own access policies, allowing the same MCP server to serve multiple users with different credentials and permission boundaries. This means one GitHub MCP server can securely support dozens of users, each accessing their own repositories with their own credentials.
• User Isolation: Even when multiple users share the same MCP server instance, their credentials, data access, and operations remain completely isolated from one another.
• Role-Based Access Control (RBAC): Define roles for different users, teams, and AI agents, each with precisely scoped permissions—all within the same MCP server deployment.
• Resource-Level Permissions: Control access not just to MCP servers, but to specific resources within each server (e.g., particular databases, file directories, or API endpoints) on a per-user or per-workflow basis.
• Operation-Level Controls: Specify which operations (read, write, delete, execute) are permitted for each role and resource combination.
• Time-Based Restrictions: Implement temporal access controls that limit when certain operations can be performed.
• Context-Aware Policies: Apply different access rules based on factors like request origin, user identity, workflow context, or system state.

Internal MCP Hosting: Security Through Isolation

One of the most significant security advantages of Strongly.AI's approach is that all MCP servers run within your secure perimeter—whether that's your private cloud, VPC, or on-premises infrastructure. Each MCP server runs as a Kubernetes pod, giving you full control over orchestration, scaling, and security policies. This internal hosting model provides:

• Data Sovereignty: Your data never leaves your network, addressing regulatory requirements and data residency concerns.
• Network Isolation: MCP servers operate as Kubernetes pods within your existing security zones, protected by your firewalls, network policies, and service mesh configurations.
• Compliance Alignment: Meet stringent compliance requirements (HIPAA, PCI-DSS, SOC 2, etc.) that prohibit external data processing.
• Reduced Attack Surface: No external APIs or public endpoints—everything communicates through secure internal Kubernetes service endpoints.
• Container-Level Security: Apply Kubernetes security contexts, pod security policies, and network policies to enforce defense-in-depth.

Seamless Integration: Drag-and-Drop Workflow Builder

Security shouldn't come at the cost of usability. That's why Strongly.AI makes MCP accessible through our intuitive drag-and-drop workflow builder, which allows you to:

This visual approach democratizes MCP, making secure AI integrations accessible to business users, not just developers—while maintaining the security controls that enterprise teams require. Each MCP server node in your workflow runs as an isolated Kubernetes pod, with workflow-level permissions ensuring that multiple users can safely share the same MCP server infrastructure.

Real-World Use Case: Secure Financial Data Integration

Consider a financial services firm with 50 analysts who need AI-powered access to multiple data sources—trading platforms, market data feeds, internal databases, and regulatory filing systems. Each analyst should only access data they're authorized for, using their own credentials. With traditional MCP implementations, this would require:

• Deploying 50 separate MCP server instances (one per analyst) or sharing credentials across the team
• Storing hundreds of API keys in configuration files
• Granting broad, persistent access to sensitive systems
• Manually managing credential rotation and revocation for each analyst
• Limited visibility into which analysts accessed what data and when

With Strongly.AI's secure MCP platform, the same organization can:

1. Deploy Shared MCP Servers: Deploy a single PostgreSQL MCP server, a single Bloomberg API server, and other infrastructure that all 50 analysts can use—each with their own credentials and workflow-level permissions. No need for 50 separate deployments.
2. Configure Workflow-Level Access: Each analyst creates their own workflows with their own credentials. The same MCP server instance serves all analysts, but each only accesses data they're authorized for.
3. Customize When Needed: Any analyst can clone an MCP server to customize it for specific analysis needs, with customizations stored in isolated S3 directories.
4. Deploy Internally: Run all MCP servers as Kubernetes pods within their existing VPC, maintaining data sovereignty.
5. Use JIT Credentials: Rely on dynamically provisioned, short-lived tokens instead of static API keys—each analyst's credentials are automatically managed.
6. Build Workflows Visually: Analysts use the drag-and-drop builder to create sophisticated AI workflows without writing code—connect MCP server nodes directly to AI models.
7. Monitor and Audit: Track every analyst's access, credential usage, and data flows through comprehensive per-workflow logging with build, deployment, and runtime logs.

The result: Secure, compliant, auditable AI-powered financial analysis that meets the strictest security requirements—with efficient resource utilization and complete user isolation.

The Future of Enterprise MCP

The Model Context Protocol represents the future of AI system integration, but its enterprise adoption depends on solving the security challenges inherent in current implementations. Strongly.AI's approach—combining containerized MCP servers, just-in-time credential management, workflow-level fine-grained access control with multi-tenancy, and internal hosting—makes MCP enterprise-ready.

As organizations increasingly rely on AI agents to access and process sensitive data across multiple systems, the security model matters as much as the capabilities. With Strongly.AI, you don't have to choose between the power of MCP and the security your enterprise requires—you get both. And with our workflow-level permissions, you can efficiently serve hundreds of users with a shared MCP infrastructure while maintaining complete security isolation between them.

References

1. Anthropic. (2024). "Introducing the Model Context Protocol".
2. Model Context Protocol Documentation. https://modelcontextprotocol.io