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AI Architecture

Model Context Protocol (MCP)

Model Context Protocol standardizes how AI applications exchange context and invoke exposed capabilities, but security still depends on the host, server, and authorization design.

What You Need to Know

MCP defines a client-server protocol for exposing resources, prompts, and tools to an AI host. It can reduce custom integration work by giving hosts a consistent discovery and invocation model. It does not make a tool safe, grant business authorization, or guarantee portability on its own. The host decides which servers are available, the server defines its capabilities, and the underlying system remains responsible for enforcing identity and permissions.

Enterprise design begins with the trust boundary. A remote MCP server needs transport security, authenticated clients, explicit authorization, scoped credentials, tenant isolation, and a clear token audience. Tool descriptions and returned content are untrusted inputs to the host. A model must not be allowed to convert persuasive text into broader authority. High-consequence tools should use typed contracts, schema validation, idempotency keys, bounded side effects, approval gates, and denial-path tests.

MCP is appropriate when multiple hosts need a governed interface to reusable capabilities. A direct service API or deterministic workflow is often better when there is one caller, a stable contract, strict latency constraints, or no need for model-directed discovery. Production evaluation should cover tool selection, argument correctness, authorization failures, prompt injection through tool metadata or results, timeouts, retries, partial completion, audit correlation, and operator revocation.

How We Handle It

We first decide whether MCP is the right boundary. Where it is, we define tool contracts, identity propagation, authorization checks, credential scope, approval requirements, audit correlation, timeouts, retries, and revocation before exposing a capability. We test denied and malformed requests as deliberately as successful calls and keep consequential authorization outside model instructions.

Primary sources
Services
Service
Agentic AI Engineering
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AI Platform Engineering
Service
Data Engineering & Analytics
Related Frameworks
SOC 2NIST AI RMFHIPAA
DECISION GUIDE

Compliance-Native Architecture Guide

Design principles and a structured checklist for building software that is compliant by default — not compliant by retrofit. Covers data architecture, access controls, audit trails, and vendor due diligence.

Apply Model Context Protocol (MCP) in regulated industries
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Related
Service
Agentic AI Engineering
Service
AI Platform Engineering
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Data Engineering & Analytics
Related Framework
SOC 2
Related Framework
NIST AI RMF
Related Framework
HIPAA
Platform
ALICE Compliance Engine
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Compliance Infrastructure
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