Inferensys

Glossary

OpenID Connect (OIDC)

An identity layer built on OAuth 2.0 that verifies end-user identity and obtains basic profile information in a standardized manner, providing federated single sign-on for AI development platforms.
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IDENTITY FEDERATION

What is OpenID Connect (OIDC)?

An identity layer built on OAuth 2.0 that verifies end-user identity and obtains basic profile information in a standardized manner, providing federated single sign-on for AI development platforms.

OpenID Connect (OIDC) is an authentication protocol that extends OAuth 2.0 by adding an identity layer, enabling clients to verify an end-user's identity based on the authentication performed by an authorization server. It introduces a standardized ID Token, a JSON Web Token (JWT) containing claims about the user, allowing AI platforms to securely federate logins without managing passwords.

In zero-trust content architectures, OIDC enables federated identity and continuous access evaluation for AI agents accessing proprietary data. By issuing session-bound tokens and supporting just-in-time authorization, OIDC ensures that retrieval-augmented generation systems only access enterprise resources after cryptographically verifying the requesting entity's identity and session integrity.

IDENTITY FEDERATION

Key Features of OpenID Connect

OpenID Connect extends OAuth 2.0 with a standardized identity layer, enabling AI development platforms to verify user identity and obtain profile information through interoperable single sign-on.

01

ID Token (JWT-Based Identity Assertion)

The ID Token is the core artifact of OIDC, a cryptographically signed JSON Web Token (JWT) that asserts the end-user's identity. Unlike OAuth 2.0 access tokens, which are opaque to the client, the ID Token contains structured claims about the authenticated user, such as sub (subject identifier), iss (issuer), aud (audience), iat (issued at), and exp (expiration).

  • Verification: Clients must validate the token's signature using the provider's public JWKS endpoint, confirm the issuer matches the discovery document, and check that the audience claim matches the client ID.
  • Nonce Parameter: Mitigates replay attacks by binding the token to a client-generated random value sent in the initial authentication request.
JWT
Token Format
RS256/ES256
Signature Algorithm
02

UserInfo Endpoint (Standardized Profile Retrieval)

The UserInfo Endpoint is a protected OAuth 2.0 resource that returns claims about the authenticated end-user in a standardized JSON structure. After obtaining an access token, a client sends a GET or POST request to this endpoint to retrieve profile attributes such as name, email, picture, and email_verified.

  • Scope-Driven Claims: The claims returned are governed by the scopes requested during authentication (openid, profile, email, address, phone).
  • Sub Claim Consistency: The sub claim returned from UserInfo MUST match the sub in the ID Token, providing a stable, pairwise or public identifier for the user across sessions.
5
Standard Scopes
GET/POST
HTTP Methods
04

Authentication Flows (Authorization Code & Hybrid)

OIDC defines three primary authentication flows built on OAuth 2.0 grant types, each suited to different client architectures:

  • Authorization Code Flow: The most secure flow for server-side applications. The client receives a one-time code exchanged for an ID Token and Access Token at the token endpoint, with PKCE (Proof Key for Code Exchange) required to prevent authorization code interception.
  • Implicit Flow: Deprecated in OAuth 2.1; tokens were returned directly in the redirect URI fragment.
  • Hybrid Flow: Allows the ID Token to be returned in the front-channel authorization response while the Access Token is retrieved via the back-channel, enabling immediate identity verification without waiting for the token endpoint.
PKCE
Required Extension
3
Defined Flows
06

Claims & Scopes (Granular Identity Data)

OIDC structures identity data through a hierarchy of scopes and claims. Scopes are coarse-grained permission bundles, while claims are the individual attributes returned.

  • Standard Scopes: openid (mandatory, returns sub), profile (name, picture, locale), email (email, email_verified), address (postal address), phone (phone_number, phone_number_verified).
  • Individual Claims Requests: The claims request parameter allows clients to request specific voluntary claims or specify that certain claims are essential, enabling fine-grained data minimization.
  • Aggregated & Distributed Claims: Claims can reference external sources via URLs, allowing the OP to compose identity data from multiple authoritative systems without centralizing all attributes.
5
Standard Scopes
sub
Mandatory Claim
IDENTITY LAYER

Frequently Asked Questions

Clear answers to the most common technical questions about OpenID Connect, its relationship to OAuth 2.0, and its role in securing enterprise access for AI development platforms.

OpenID Connect (OIDC) is an identity layer built on top of the OAuth 2.0 authorization framework that allows clients to verify the identity of an end-user based on the authentication performed by an authorization server, as well as to obtain basic profile information about the end-user in an interoperable and REST-like manner. It works by introducing a new token type called the ID Token, which is a JSON Web Token (JWT) that contains claims about the authentication event and the user. When a user authenticates, the authorization server issues an ID Token alongside the standard OAuth 2.0 access token. The client can then parse and validate this ID Token to confirm the user's identity, eliminating the need to build and maintain a proprietary identity database. This federated model enables Single Sign-On (SSO) across multiple applications, making it the standard protocol for securing access to AI development platforms and enterprise data repositories.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.