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

Source: arXiv - 2601.02698v1

Overview

Enterprises are rapidly adopting AI‑assisted developer tools—think code‑completion bots, automated refactoring assistants, and context‑aware documentation generators—directly inside their IDEs. While these assistants boost productivity, they also raise a red flag: how do they respect an organization’s existing single‑sign‑on (SSO), access‑control, and audit policies? This paper proposes a concrete architecture that plugs OAuth 2.0 and OpenID Connect (OIDC) into the Model Context Protocol (MCP), the de‑facto standard for feeding AI assistants with structured project context. The authors validate the design with a VS Code extension, a Python‑based MCP server, and a real OIDC identity provider, then measure latency and security trade‑offs.

Key Contributions

• MCP‑centric security pattern: Extends the minimal MCP auth model with a full‑featured OAuth 2.0/OIDC flow, enabling token‑based, least‑privilege access.
• IDE‑extension workflow: Shows how a VS Code plugin can transparently acquire, refresh, and present access tokens to the MCP server without disrupting developers.
• Scope‑and‑claim mapping: Demonstrates a systematic way to translate enterprise roles/claims into MCP‑specific permissions (e.g., read‑only project metadata vs. write‑access to secret files).
• Prototype implementation: Open‑source reference code (VS Code extension, Python MCP server, OIDC client) that can be dropped into existing CI/CD pipelines.
• Empirical case study: Quantifies authentication latency (≈ 120 ms on average), token‑validation overhead (≈ 30 ms per request), and discusses AI‑specific risk vectors such as prompt injection and data leakage.

Methodology

1. Design Phase – The authors mapped the MCP request/response cycle onto the OAuth 2.0 authorization code grant, using OIDC for identity verification. They defined a set of custom scopes (mcp:read, mcp:write, mcp:admin) and claim requirements (e.g., department, project_id).
2. Implementation Phase –
   • IDE side: A VS Code extension leverages the Microsoft Authentication Library (MSAL) to trigger the SSO login flow, cache tokens, and inject the Authorization: Bearer <token> header into every MCP call.
   • Server side: A lightweight Python Flask app validates tokens against the OIDC provider’s JWKS endpoint, extracts claims, and enforces scope checks before serving context data.
3. Evaluation Phase – The prototype was deployed in a corporate sandbox with 20 developers. The authors measured:
   • End‑to‑end authentication latency (login + token refresh).
   • Per‑request token verification cost.
   • CPU/memory impact on the MCP server.
   • Security posture via threat‑model walkthroughs (e.g., token replay, scope escalation).

Results & Findings

Overall, the study shows that integrating enterprise‑grade OAuth/OIDC into MCP adds minimal performance overhead while delivering strong identity assurance and fine‑grained access control.

Practical Implications

• Plug‑and‑play security: Organizations can adopt AI assistants without building custom auth layers; the pattern works with any OIDC‑compliant IdP (Azure AD, Okta, Keycloak, etc.).
• Compliance‑ready audit trails: Every context request is logged with user ID, scopes, and timestamps, simplifying SOC 2, ISO 27001, and internal governance reporting.
• Least‑privilege by default: By exposing only the scopes an AI assistant truly needs, firms reduce the blast radius of a compromised token.
• Scalable to large teams: The modest CPU/memory footprint means the MCP server can be containerized and autoscaled alongside existing developer tooling stacks.
• Risk mitigation: The architecture isolates token handling to the IDE and MCP server, limiting exposure of raw credentials to the AI model itself—crucial for preventing prompt‑injection or data‑exfiltration attacks.

Developers can now enable AI‑powered code suggestions, automated test generation, or security linting while staying within the familiar SSO workflow they already trust.

Limitations & Future Work

• Scope granularity: The current prototype defines only a handful of coarse scopes; richer, resource‑level scopes (e.g., per‑repository) need further design.
• Multi‑tenant scenarios: The paper focuses on a single‑tenant enterprise; extending the model to SaaS platforms serving many customers will require tenant isolation mechanisms.
• Token revocation latency: Revoking a compromised token relies on short‑lived access tokens and periodic JWKS refresh; real‑time revocation (e.g., via introspection) was not evaluated.
• AI model privacy: While the architecture secures the transport of context, it does not address downstream privacy concerns when the AI model itself stores or caches data. Future work could explore encrypted payloads or on‑device inference to further reduce data exposure.

By addressing these gaps, the community can evolve the pattern into a robust, enterprise‑grade foundation for the next generation of AI‑assisted developer services.

Authors

• Manideep Reddy Chinthareddy

Paper Information

• arXiv ID: 2601.02698v1
• Categories: cs.SE, cs.CR
• Published: January 6, 2026
• PDF: Download PDF