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AI System Registration

AI system registration establishes a controlled record for AI systems that are subject to organizational governance.

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Purpose

Registration provides a consistent starting point for governance activities and creates traceability throughout the AI system lifecycle. A registration record may include:
  • system identity
  • owner and accountable roles
  • intended use
  • deployment context
  • lifecycle status
  • relevant stakeholders
  • governance information
  • classification information
  • risk relationships
  • applicable controls
  • approval status
  • relationships to supporting governance records
The resulting system record provides the identity and context against which subsequent governance activities can be performed.

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Why registration matters

Without a reliable inventory, organizations may not know:
  • which AI systems exist
  • who is accountable for them
  • how they are being used
  • what governance requirements apply
  • what risks are associated with them
  • which controls apply
  • what governance decisions have been made
Registration therefore provides a foundation for classification, risk assessment, controls, monitoring, assurance, evaluation, and lifecycle management.

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Registration and traceability

AIGO treats the AI system as a central governed entity. Other governance records can establish relationships to the registered system, including:
  • risks
  • controls
  • assessments
  • approvals
  • monitoring activities
  • incidents
  • changes
  • assurance activities
  • evidence
  • improvements
  • retirement activities
This allows governance information to remain distributed across specialized records while retaining traceability to the governed AI system.

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Registration and evaluation

A registered AI system can also be used as an evaluation entity. The current AIGO evaluation layer supports rule-based governance evaluation against an entity such as an AI system. Evaluation rules are maintained separately from the registration record, allowing governance requirements to evolve without changing the identity record itself. An evaluation request can identify the entity being evaluated and the rules requested for evaluation. The repository currently validates evaluation fixtures and evaluation orchestration independently from the AI system registration process.

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Machine-readable representation

The machine-readable AI System Schema provides the structural representation of governed AI system records. Canonical 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

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Operational procedure

The canonical implementation procedure is maintained in: guidance/02-procedures/02-AIGO-AI-System-Registration-Procedure-v0.1.md The corresponding implementation template is maintained in: guidance/03-templates/02-AIGO-AI-System-Registration-Template-v0.1.md

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Validation

Registration records participate in the broader AIGO validation model. The repository currently includes validation for schemas, references, controls, evidence, framework consistency, document integrity, repository health, rules, evaluation fixtures, and evaluation orchestration. Validation provides repository and artifact quality assurance. It does not constitute legal compliance, certification, accreditation, or regulatory conformity.