HUGS ID™ Technical Specification v1.0b | Archived Public Working Draft

Status of This Document

This document is the authoritative technical reference for the HUGS ID™ platform. It defines the architecture, identity model, governance framework, consent framework, AI permission model, trust framework, and application programming interfaces of the HUGS ID standard.

Archived Public Working Draft. This document is Version 1.0b of the HUGS ID™ Technical Specification. It has been superseded by Version 1.1, which incorporates the accepted findings of the independent security and architecture review. Version 1.0b remains available for transparency, citation history, and comparison. New implementation work should use Version 1.1.

Normative terms such as MUST, MUST NOT, SHOULD, SHOULD NOT, MAY, and OPTIONAL are interpreted according to RFC 2119. Requirement identifiers use the format REQ-<section>-<number>.

1. Vision

HUGS ID™ exists to create a globally interoperable identity and governance standard that allows individuals to manage the lifecycle of their verified digital identity while enabling trusted collaboration between institutions and artificial intelligence systems.

HUGS ID does not create another centralized database. It establishes a governance layer that determines how identity, permissions, provenance, consent, verification, and trust interact across independent systems.

Core objectives

Define a canonical, machine-readable identity object.
Give individuals enforceable authority over the use of verified information.
Make every AI action attributable to an identity, authorization, purpose, and policy.
Integrate with existing institutional systems rather than replace them.
Evolve as an independently implementable open technical standard.

2. Problem Statement

Digital identity is fragmented across governments, schools, hospitals, employers, sports organizations, financial institutions, and technology platforms. Each system holds only a partial view and rarely gives the person meaningful authority over how that information is reused.

Artificial intelligence intensifies this problem by summarizing, classifying, predicting, recommending, generating, and deciding from data that people often do not control or understand.

HUGS ID makes identity, consent, provenance, verification, governance, and trust first-class, machine-readable objects with defined semantics.

3. Architecture

The framework uses seven independent layers. Requests flow upward; verification and trust flow downward.

Applications

AI Services

Governance Layer

Consent Layer

Verification Layer

Identity Layer

Evidence Layer

Version 1.0 specifies Athlete Intelligence in detail. Recognized future domains include education, healthcare, employment, research, creative works, digital assets, and AI agents.

4. Identity Layer

Canonical Identity Object: the single authoritative representation of a subject within the HUGS ID framework. It contains no personal records; it anchors references, verifications, consents, and governance decisions to one resolvable identifier.

HUGS identifiers use the form:

hugs:id:<type>:<namespace>:<unique-id>

Supported identity types are individual, organization, ai-agent, and digital-asset.

5. Governance Layer

Governance is expressed through policies, authority chains, institutional roles, and decision records. Every governed operation must be traceable to a complete authority chain.

More specific policies override broader policies, while prohibitions always override permissions. Delegated authority may be narrowed but never amplified beyond its parent authority.

7. Athlete Intelligence

Athlete Intelligence is the first governed domain under HUGS ID. It applies identity, verification, consent, governance, and trust to sports data across clubs, schools, leagues, federations, agents, testing providers, and media platforms.

Data class	Examples
Identity registration	Verified identity, age band, registration status, federation membership.
Eligibility	Clearances, transfer status, amateur or professional standing.
Performance	Verified results, statistics, rankings, and milestones.
Development	Training benchmarks, combine results, and linked academic standing.
Media rights	Identity-linked media, likeness use, and licensing terms.

8. AI Permissions

Every AI system operating against governed information must hold a resolvable ai-agent identity with an accountable organizational controller.

Permission classes include read, derive, train, generate, distribute, and decide.

Every AI action must be attributable to an identity, an authorization, a purpose, and a governance policy.

9. Trust Framework

Trust is computed from verification depth, provenance completeness, authority weight, recency, and dispute state. Trust applies to a claim and a purpose; it is not a universal label attached to a person.

Level	Name	Basis
T0	Unverified	Self-asserted only.
T1	Basic	Single-source recognized verification.
T2	Verified	Multi-source or institutional verification.
T3	Certified	Governing-body attestation through a complete authority chain.
T4	Canonical	Continuously verified, complete provenance, and uncontested over time.

10. APIs

HUGS ID exposes versioned REST resources over HTTPS. JSON resources are namespaced under /v1/. The API enforces the same governance, consent, and trust rules as the rest of the framework.

/v1/identities
/v1/consents
/v1/governance/policies
/v1/trust/scores/{identityId}
/v1/events

11. Roadmap

Version 1.0: Identity, governance, consent, verification, and Athlete Intelligence.

Version 2.0: Full AI-agent lifecycle, cross-domain trust exchange, institutional federation, and reputation models.

Version 3.0: Global identity federation, autonomous AI governance, real-time compliance, and decentralized trust networks.

12. References

The full PDF includes the normative and informative references informing this draft, including RFC 2119, W3C Verifiable Credentials, W3C Decentralized Identifiers, OAuth 2.0, OpenID Connect, ISO/IEC 27001, NIST SP 800-63, and the GDPR.

Open the Complete Technical Specification