You scope an AIMS by defining organizational context and boundaries, then setting the AIMS scope so policies, risks, controls, and operations match what is in-scope.
Scoping is the step that prevents an AI management system from becoming either too vague or unrealistically broad. It starts with understanding the organization and its context, then defining the scope of the AIMS so everyone knows which AI activities, teams, and AI operations the system applies to.
Once scope is set, implementation work becomes more concrete: you can analyze the existing system, define an AI policy, and structure AI risk management around the in-scope AI use cases. The scope also drives what documented information must be maintained and what monitoring and audit activities will cover.
A well-defined scope is also foundational for certification readiness because it clarifies what external auditors should evaluate and what evidence should exist for in-scope activities.
Teams often fail by scoping around "AI" as a technology instead of scoping around AI use in business processes. Clear scope prevents gaps where AI is used but unmanaged, and it prevents wasted effort on out-of-scope initiatives.
“AIMS scope is the boundary that makes governance workable.”
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A Statement of Applicability documents which controls are selected for the AIMS and why they apply, creating traceability between risks, requirements, and controls.
The audit should be led by a competent auditor, supported by AI specialists when needed to evaluate technical context and risks.
AIMS scope defines which AI activities, systems, and organizational units are covered. Context analysis examines stakeholders, legal requirements, and organizational objectives to ensure the AIMS is fit for purpose.
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