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06/06/2026 1:41 am
TL;DR: AI coding assistants are diverging between editor-bound helpers and more agentic systems that plan across codebases, and a Descope comparison found Gemini Code Assist produced a partially correct JWT flow while Claude Code delivered a more complete implementation with stronger tests. The security lesson is that code generation still depends on human review, because authentication logic can look correct while leaking hashes, skipping migrations, or weakening token boundaries.
NHIMG editorial — based on content published by Descope: Developer's Guide to Claude Code vs. Gemini Code Assist
By the numbers:
- Claude Code leads adoption with over 10.1 million VS Code installs as of April 2026, compared to Gemini Code Assist's 3.7 million.
- 75% of developers use VS Code as their, their primary IDE.
Questions worth separating out
Q: How should teams govern AI-generated authentication code?
A: Treat AI-generated authentication code as identity-sensitive change, not ordinary development output.
Q: Why do AI coding assistants create IAM risk in application development?
A: They can generate plausible identity logic that still violates security boundaries, such as exposing password hashes, skipping migrations, or weakening token separation.
Q: How do you know if assistant-generated auth tests are actually working?
A: Look for explicit coverage of failure paths, not just successful login.
Practitioner guidance
- Review AI-generated auth code as identity-sensitive change Require code owners from IAM or application security to approve any generated login, token, or session logic before merge.
- Enforce negative-case tests for token boundaries Make tests for malformed credentials, token swapping, and wrong-token usage mandatory for assistant-generated authentication flows.
- Block unsafe defaults in seeded identity data Check that any sample users, password hashes, or starter databases created by an assistant are migrated, rehashed, and never exposed through profile endpoints.
What's in the full article
Descope's full blog covers the implementation details this post intentionally leaves at the governance level:
- A side-by-side walkthrough of the generated FastAPI authentication changes and route structure.
- The full test output and debugging notes for the JWT login, refresh, and profile flow.
- A closer look at the Descope Docs MCP Server and how documented context changes assistant output.
- The author's manual verification steps for comparing the two assistants in VS Code.
👉 Read Descope's comparison of Claude Code and Gemini Code Assist for JWT auth →
Claude Code vs Gemini Code Assist: what it means for IAM teams?
Explore further
06/06/2026 2:56 am
AI coding assistants are becoming identity governance actors, not just productivity tools. Once a model can generate login flows, token validation, and route protections, it is shaping identity control outcomes inside the delivery pipeline. That makes code-assist governance part of IAM governance, especially where authentication logic is generated faster than teams can inspect it. Practitioners should treat assistant output as identity-relevant change, not generic developer convenience.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
- Only 44% of organisations have implemented policies to govern AI agents, even though 92% agree that governance is critical to enterprise security.
A question worth separating out:
A: IDE-native assistants usually stay closer to the editor and are easier to scope, while terminal-native agents can inspect more of the repository and make broader changes. For security review, the key difference is not branding but how much cross-file identity logic they can alter in one session.
👉 Read our full editorial: AI coding assistants are exposing auth and review gaps in IDEs
