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COMPEL Body of Knowledge

COMPEL Glossary / GL-13

Shadow AI Inventory

A structured catalogue of AI tools, models, and automated systems already in use across the organization that were deployed outside formal governance channels.

What this means in practice

It surfaces unsanctioned AI activity so it can be assessed, governed, or retired before the organization proceeds with sanctioned AI transformation.

Context in the COMPEL framework

Produced in the Calibrate stage. Shadow AI represents both a risk (ungoverned systems) and an opportunity (proven use cases) that must be surfaced before portfolio planning begins.

Where you see this

Shadow AI Inventory is most commonly referenced when teams work across the Calibrate stage — especially within the Operational Readiness layer . It appears in governance artifacts, assessment instruments, and delivery playbooks wherever COMPEL is operationalized.

Related COMPEL stages

Related domains

Synonyms

unsanctioned AI inventory , shadow IT AI audit , rogue AI catalogue

Related Terms Network

This diagram shows how Shadow AI Inventory connects to other concepts in the COMPEL body of knowledge. Each linked node is a term you can explore in turn.

Related terms for Shadow AI Inventory Network graph showing 3 related glossary terms connected to Shadow AI Inventory. Shadow AI Inve… AI System … Use-Case P… Risk Appet…
3 related concepts connected to Shadow AI Inventory.
Accessible list of related terms
  • AI System Classification Register — A formal register that classifies every AI system in scope according to risk tier, autonomy level, data sensitivity, regulatory applicability, and criticality — producing a system-level risk profile that determines which governance controls, review processes, and compliance requirements apply.
  • Use-Case Portfolio Canvas — A structured prioritization tool that maps candidate AI use cases across value potential, feasibility, risk, and strategic alignment dimensions to produce a ranked, resource-constrained portfolio for the transformation program.
  • Risk Appetite Statement — A formally approved document that defines the types and levels of AI-related risk the organization is willing to accept in pursuit of its AI ambition, covering operational, reputational, regulatory, and ethical risk dimensions.

See also

  • AI System Classification Register — A formal register that classifies every AI system in scope according to risk tier, autonomy level, data sensitivity, regulatory applicability, and criticality — producing a system-level risk profile that determines which governance controls, review processes, and compliance requirements apply.
  • Use-Case Portfolio Canvas — A structured prioritization tool that maps candidate AI use cases across value potential, feasibility, risk, and strategic alignment dimensions to produce a ranked, resource-constrained portfolio for the transformation program.
  • Risk Appetite Statement — A formally approved document that defines the types and levels of AI-related risk the organization is willing to accept in pursuit of its AI ambition, covering operational, reputational, regulatory, and ethical risk dimensions.

Related Terms

Other glossary terms mentioned in this entry's definition and context.