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Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study

Manideep Reddy Chinthareddy

TL;DR

The paper addresses secure integration of MCP-enabled AI developer tools with enterprise identity and access management to enforce least-privilege access and strong auditability. It proposes a reference architecture that combines OAuth 2.0/OIDC-based SSO, MCP discovery/authorization, and public IDE clients, with PKCE and JWKS-based token validation. An implementation blueprint using Keycloak, a Python-based MCP server, and a VS Code extension is provided, along with a case study evaluating authentication latency, token validation, and operational considerations. The results show negligible runtime overhead and substantial improvements in identity assurance and governance for AI-assisted IDE workflows in production environments.

Abstract

AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI assistants to retrieve structured internal context, but its specification provides only a minimal authorization model and lacks guidance on integrating enterprise SSO. This article presents a practical architecture that incorporates OAuth 2.0 and OpenID Connect (OIDC) into MCP-enabled developer environments. It describes how IDE extensions obtain and present tokens, how MCP servers validate them through an identity provider, and how scopes and claims can enforce least-privilege access. A prototype implementation using Visual Studio Code, a Python-based MCP server, and an OIDC-compliant IdP demonstrates feasibility. A case study evaluates authentication latency, token-validation overhead, operational considerations, and AI-specific risks. The approach provides a deployable pattern for organizations adopting AI-assisted developer tools while maintaining identity assurance and auditability.

Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study

TL;DR

The paper addresses secure integration of MCP-enabled AI developer tools with enterprise identity and access management to enforce least-privilege access and strong auditability. It proposes a reference architecture that combines OAuth 2.0/OIDC-based SSO, MCP discovery/authorization, and public IDE clients, with PKCE and JWKS-based token validation. An implementation blueprint using Keycloak, a Python-based MCP server, and a VS Code extension is provided, along with a case study evaluating authentication latency, token validation, and operational considerations. The results show negligible runtime overhead and substantial improvements in identity assurance and governance for AI-assisted IDE workflows in production environments.

Abstract

AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI assistants to retrieve structured internal context, but its specification provides only a minimal authorization model and lacks guidance on integrating enterprise SSO. This article presents a practical architecture that incorporates OAuth 2.0 and OpenID Connect (OIDC) into MCP-enabled developer environments. It describes how IDE extensions obtain and present tokens, how MCP servers validate them through an identity provider, and how scopes and claims can enforce least-privilege access. A prototype implementation using Visual Studio Code, a Python-based MCP server, and an OIDC-compliant IdP demonstrates feasibility. A case study evaluates authentication latency, token-validation overhead, operational considerations, and AI-specific risks. The approach provides a deployable pattern for organizations adopting AI-assisted developer tools while maintaining identity assurance and auditability.
Paper Structure (24 sections, 3 figures, 1 table)

This paper contains 24 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Motivation
  3. Contributions
  4. Background and Context
  5. Model Context Protocol (MCP)
  6. Enterprise Identity and Access Management
  7. AI-Assisted Developer Services in IDEs
  8. Architecture for Enterprise Identity Integration
  9. High-Level Components
  10. Authentication and Authorization Flow
  11. Authorization Models
  12. Threat Model and Limitations
  13. Implementation
  14. Identity Provider Configuration
  15. MCP Server Implementation
  16. ...and 9 more sections

Figures (3)

  • Figure 1: High-level architecture for enterprise identity integration with MCP servers, IDE AI extensions, and OAuth/OIDC identity providers.
  • Figure 2: End-to-end MCP authorization sequence including metadata discovery, OAuth token issuance, token validation, and protected resource access mcp-authorization.
  • Figure 3: Representative OAuth/OIDC experience for developers and administrators when VS Code authenticates an MCP server against an enterprise identity provider.