AISM Control Evidence and Audit Model

The AISM Control Evidence and Audit Model defines how AI Service Management controls, evidence, audit questions, and maturity indicators are organized for AI-enabled workforce services.

AI governance fails when control design, evidence, and audit are separated. A model can appear acceptable while the service around it remains unmanaged. AISM treats every AI-enabled service as a governed service with objectives, ownership, controls, evidence, measures, oversight, interested-party communication, and improvement.

Within Workforce Service Management, this model connects AISM to HRSM, DISM Profile, Evidence Object, Control, Risk, Audit Evidence, Management Review, and Continual Service Improvement. It makes the graph denser by turning AI controls into service-management relationships rather than isolated compliance artifacts.

A Management Advisor should recommend actions when required evidence is missing, upstream human governance controls are weak, AI service ownership is unclear, impact assessments are stale, human oversight is symbolic, supplier responsibilities are unassigned, or data quality evidence does not support the service risk.