
We apply a structured, risk-based and evidence-driven methodology to assess AI governance across the enterprise. The review covers governance and accountability, AI risk management, data and model controls, transparency, human oversight, regulatory compliance, monitoring and assurance across the AI lifecycle.
Identify AI systems, business use cases, risk classification, applicable regulations and governance responsibilities.
Review policies, controls and evidence; assess data and model governance, risk management, transparency, human oversight and lifecycle controls.
Identify governance gaps, prioritise risks and provide practical recommendations, remediation actions and a roadmap for improvement.
20-Domain Audit Assessment
Our assessment covers 20 AI governance domains aligned across six frameworks: EU AI Act, ISO/IEC 42001, NIST AI RMF, OECD AI Principles, Canadian AI governance requirements, and relevant RBI guidance. Domains include governance, accountability, risk, data, privacy, models, fairness, transparency, explainability, human oversight, security, robustness, safety, compliance, third-party risk, documentation, monitoring, incident management, audit/assurance and lifecycle governance—providing an integrated view of AI governance maturity and control gaps.
Audit Deliverables
Our engagements are designed to provide management with clear, evidence-based findings and actionable recommendations.
Deliverables may include an AI Governance Maturity Assessment, regulatory and standards gap analysis, risk and control observations, prioritised remediation recommendations, management-level dashboards and a comprehensive AI Governance Audit Report.

Flexible Engagement Models
Engagements may range from focused governance readiness assessments and regulatory gap reviews to comprehensive enterprise AI governance audits. We can also work alongside internal audit, risk, compliance and technology teams or collaborate with specialist technology and governance-platform providers where appropriate.
