Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912804675518464 |
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| author | Chinthareddy, Manideep Reddy |
| author_facet | Chinthareddy, Manideep Reddy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02698 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study Chinthareddy, Manideep Reddy Software Engineering Cryptography and Security 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. |
| title | Enterprise Identity Integration for AI-Assisted Developer Services: Architecture, Implementation, and Case Study |
| topic | Software Engineering Cryptography and Security |
| url | https://arxiv.org/abs/2601.02698 |