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AI System Profiles

AI System Profiles provide a structured representation of AI systems and connect system characteristics to governance, risk, controls, lifecycle activities, evaluation, and assurance.

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Purpose

Profiles establish a consistent representation of an AI system across its governance lifecycle. They can support:
  • registration
  • classification
  • risk assessment
  • control selection
  • approval
  • monitoring
  • assurance
  • change management
  • retirement
  • governance evaluation
A profile provides the system-level context needed to relate governance activities and machine-readable records to a specific AI system.

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Governance context

An AI System Profile should provide enough context to establish:
  • what the AI system is
  • who is accountable for it
  • how and where it is used
  • what lifecycle state it is in
  • what governance requirements apply
  • what risks are associated with it
  • which controls apply
  • which assessments and approvals have been performed
  • what evidence and assurance information supports governance decisions
The profile therefore acts as an important reference point for traceability across the AIGO governance model.

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Machine-readable representation

AIGO provides machine-readable schemas for representing governed AI system information. The AI System Schema provides the structural representation of an AI system record. Other AIGO schemas can reference the system when representing related governance objects such as risks, controls, assessments, approvals, monitoring activities, incidents, changes, assurance activities, evidence, and improvements. The repository also provides validation capabilities for machine-readable governance artifacts. These validations help establish structural integrity, reference integrity, and consistency of governed records.

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Relationship to governance evaluation

AI systems can serve as the evaluation subject for AIGO governance evaluation. The repository currently includes machine-readable evaluation rules, evaluation fixtures, and orchestration validation. This allows an AI system to be used as the entity against which defined governance rules can be evaluated. The evaluation layer is separate from the profile itself: AI System Profile → governed entity context Rules → governance requirements to evaluate Evaluation → results produced from applying rules Orchestration → configuration of an evaluation request This separation allows the profile to remain a governance record while evaluation logic is maintained independently.

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Canonical source

The canonical AI System Profile material is maintained in: framework/09-profiles/AIGO-AI-System-Profiles-v0.1.md The corresponding machine-readable schema is maintained in: schemas/01-ai-system/01-AIGO-AI-System-Schema-v0.1.json Schema documentation is maintained in: schemas/01-ai-system/01-AIGO-AI-System-Schema-Documentation-v0.1.md The AI System Registration Procedure provides the operational process for establishing governed AI system records: guidance/02-procedures/02-AIGO-AI-System-Registration-Procedure-v0.1.md The AI System Profile Template provides an implementation-oriented template: guidance/03-templates/03-AIGO-AI-System-Profile-Template-v0.1.md

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Validation

AI system records are part of the broader AIGO repository validation and traceability model. Repository validation currently covers schema integrity, references, control coverage, evidence coverage, framework consistency, document integrity, repository health, rule validation, evaluation fixtures, and evaluation orchestration. These validators establish repository and artifact quality. They do not by themselves establish legal compliance, certification, accreditation, or regulatory conformity.