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AI System Schema

The AI System Schema provides the machine-readable structural representation of an AI system within the AIGO Framework.

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Purpose

The schema supports consistent recording of AI system information needed for governance and lifecycle management. It provides the structural foundation for information that may be used by:
  • registration
  • classification
  • risk management
  • control management
  • approval
  • monitoring
  • assurance
  • change management
  • incident management
  • retirement
  • governance evaluation

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Role in the AIGO architecture

The AI System Schema represents the governed AI system itself. Other AIGO schemas represent related governance objects and can establish relationships to the system. This creates a layered model: AI System → Risks → Controls → Assessments → Evidence → Decisions → Assurance The exact relationships are represented through the AIGO machine-readable relationship and governance structures.

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Typical information

A governed AI system record may include information about:
  • system identity
  • ownership
  • accountability
  • intended use
  • deployment context
  • lifecycle status
  • governance context
  • stakeholders
  • classification
  • risk relationships
  • control relationships
  • related governance records
The precise permitted structure and data types are defined by the canonical JSON Schema rather than by this overview page.

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Relationship to evaluation

The AI System Schema provides the entity context for governance evaluation. The evaluation layer defines rules separately from the system schema. This allows an AI system record to remain a representation of the governed entity while evaluation rules define what should be checked about that entity. The current repository includes:
  • machine-readable evaluation rules
  • evaluation fixtures
  • evaluation validation
  • orchestration validation
This establishes the foundation for applying defined governance evaluation logic to an AI system.

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Validation

The repository validates machine-readable artifacts as part of its broader validation framework. Current repository validation includes:
  • schema validation
  • reference validation
  • traceability validation
  • control coverage validation
  • evidence coverage validation
  • framework consistency validation
  • document integrity validation
  • repository health validation
  • rule validation
  • evaluation fixture validation
  • evaluation orchestration validation
A passing repository validation result indicates that the tested repository and artifacts satisfy the implemented validation checks. It does not establish legal compliance, certification, accreditation, or regulatory conformity.

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Canonical source

Machine-readable schema: schemas/01-ai-system/01-AIGO-AI-System-Schema-v0.1.json Schema documentation: schemas/01-ai-system/01-AIGO-AI-System-Schema-Documentation-v0.1.md Framework profile material: framework/09-profiles/AIGO-AI-System-Profiles-v0.1.md