Secure MCP Server

🎯 What is the Secure MCP Server? The Secure MCP Server is an enterprise implementation of the Model Context Protocol (MCP) - a standardized protocol that allows AI assistants to safely access and use tools, data sources, and execute code in a controlled environment. 🔄 How It Works Core Concept Think of it as a secure bridge between AI models (like Claude, GPT, etc.) and your organization's tools and data: AI Assistant ←→ MCP Server ←→ Your Tools/Data/Systems ↑ (Security Layer) Key Components Flow sequenceDiagram participant AI as AI Assistant participant MCP as MCP Server participant Auth as Auth Service participant Sandbox as Container Sandbox participant Tool as Tool/Resource AI->>MCP: Connect via WebSocket MCP->>Auth: Authenticate (JWT/MFA) Auth-->>MCP: Token validated AI->>MCP: Request tool execution MCP->>MCP: Validate permissions MCP->>Sandbox: Create isolated container Sandbox->>Tool: Execute in sandbox Tool-->>Sandbox: Return results Sandbox-->>MCP: Sanitized output MCP-->>AI: Send response 💼 What It Does 1. Secure Tool Execution The server allows AI assistants to safely run tools and code: - Code Analysis: Run linters, security scanners, test suites - Data Processing: Query databases, process files, generate reports - System Operations: Deploy services, manage infrastructure, run diagnostics - Integration Tasks: Call APIs, sync data, trigger workflows Example Use Case: // AI requests to analyze code for vulnerabilities { "tool": "security-scanner", "input": { "repository": "https://github.com/company/app", "scanType": "full" } } // MCP Server: // 1. Validates request // 2. Spins up sandboxed container // 3. Runs security scanner // 4. Returns sanitized results 2. Resource Access Control Provides controlled access to organizational resources: - Databases: Query with permissions and rate limits - File Systems: Read/write with access controls - APIs: Proxied access to internal/external services - Cloud Resources: Managed access to AWS/GCP/Azure Example: // AI requests database query { "resource": "customer_database", "operation": "query", "sql": "SELECT COUNT(*) FROM orders WHERE status='pending'" } // MCP Server enforces: // - User has permission for this database // - Query is read-only // - Results are sanitized for PII // - Rate limits are applied 3. Multi-Layer Security The server protects your infrastructure through: Container Sandboxing Tool Execution Request ↓ [Security Validation] ↓ [Container Creation] → gVisor/Kata (hardware isolation) ↓ [Resource Limits] → CPU/Memory/Network restrictions ↓ [Execution] → Isolated from host system ↓ [Output Scanning] → DLP/Malware detection ↓ Clean Response Authentication & Authorization - JWT tokens with automatic refresh - Multi-factor authentication (TOTP/SMS) - Single Sign-On (SAML/OIDC) - Role-based permissions (RBAC) 4. Real-World Scenarios Scenario 1: DevOps Automation An AI assistant helps deploy a new microservice: 1. AI connects to MCP server 2. Requests deployment tool execution 3. MCP validates permissions (user can deploy to staging) 4. Spins up sandboxed container with kubectl 5. Executes deployment commands 6. Returns deployment status and logs Scenario 2: Data Analysis AI analyzes customer data for insights: 1. AI requests database access 2. MCP checks data access permissions 3. Executes query in controlled environment 4. Applies PII redaction rules 5. Returns sanitized results 6. AI generates insights report Scenario 3: Security Incident Response AI investigates security alert: 1. AI requests log analysis tool 2. MCP validates security team membership 3. Runs log parser in isolated container 4. Scans for attack patterns 5. Returns findings without exposing sensitive data 🎮 How to Use It For Developers // Connect to MCP server const client = new MCPClient({ url: 'wss://mcp.company.com', auth: { token: 'your-jwt-token' } }); // Execute a tool const result = await client.execute({ tool: 'python-runner', code: 'print("Hello from sandboxed Python")' }); // Access a resource const data = await client.query({ resource: 'metrics_db', query: 'SELECT * FROM performance_metrics LIMIT 10' }); For AI Assistants The AI can request various operations: Available Tools: - code-executor: Run code in various languages - database-query: Query databases safely - api-caller: Make HTTP requests - file-processor: Read/write files - deployment-manager: Deploy services - security-scanner: Scan for vulnerabilities - log-analyzer: Analyze system logs 🏗️ Architecture Benefits Why This Architecture? 1. Separation of Concerns - AI doesn't have direct access to systems - All operations go through security layers - Clean audit trail of all actions 2. Defense in Depth Layer 1: Authentication (Who are you?) Layer 2: Authorization (What can you do?) Layer 3: Validation (Is this request safe?) Layer 4: Sandboxing (Execute in isolation) Layer 5: Scanning (Check output for threats) 3. Scalability - Handles 10,000+ concurrent AI connections - Auto-scales based on load - Distributes work across container pool 4. Compliance - Every action is logged - Data access is controlled - PII is automatically protected - Audit reports generated automatically 📊 Real Impact Without MCP Server: - ❌ AI has no access to tools/data - ❌ Or AI has unsafe direct access - ❌ Manual intervention required - ❌ No audit trail - ❌ Security risks With MCP Server: - ✅ AI can safely use any tool - ✅ Automatic security enforcement - ✅ Full automation possible - ✅ Complete audit logging - ✅ Enterprise-grade security 🚀 Example Enterprise Setup Organization: TechCorp Use Case: AI-Powered DevOps Tools Available: - GitHub integration (code management) - Jenkins (CI/CD pipelines) - Kubernetes (deployment) - Datadog (monitoring) - PagerDuty (alerting) - Jira (issue tracking) Workflow: 1. Developer asks AI: "Deploy feature-xyz to staging" 2. AI connects to MCP server 3. MCP validates developer's permissions 4. AI orchestrates: - Checks GitHub for latest code - Triggers Jenkins build - Deploys to Kubernetes staging - Monitors Datadog metrics - Updates Jira ticket 5. Developer gets: "Deployed successfully, monitoring shows normal" Security Enforced: - Developer can only deploy to staging - Sensitive configs remain hidden - All actions logged for compliance - Automatic rollback if issues detected This system essentially makes AI assistants useful and safe in enterprise environments by providing them controlled access to real tools and data while maintaining security, compliance, and audit requirements.