Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Heavy-handed governance drives teams toward Shadow AI, while ungoverned adoption exposes the enterprise to regulatory and reputational crises. This course teaches how to design a risk-proportionate Enterprise AI Governance Framework that gives low-risk experiments a fast track while enforcing rigorous controls on high-impact systems.

Topics include setting up a central AI system inventory (AI registry), lifecycle stage-gates, governance committee charters, policy hierarchy, exception handling and board-level reporting. In a tailored workshop, participants design an AI inventory schema and an end-to-end governance workflow for their organisation.

Who Should Attend?

  • AI governance committee members, executives and AI CoE leaders
  • Compliance, enterprise risk, legal and corporate governance heads
  • Enterprise architecture, IT governance (COBIT/ITIL) and data governance leads
  • Portfolio and programme management office (PMO) directors

Key Takeaways

  • Establish an enterprise AI asset inventory covering internal models, SaaS AI features and agents
  • Design risk-proportionate approval tracks (fast-track vs full review)
  • Define mandatory artefacts at intake, pre-deployment and periodic review gates
  • Integrate AI governance with existing data governance, InfoSec and enterprise risk processes
  • Build a board-level AI governance dashboard with key risk indicators (KRIs)

Curriculum

Risk-Proportionate AI Governance: Balancing Speed and Control
  • Why legacy IT or data governance alone does not cover AI-specific risks
  • Tiered governance intensity: fast-track sandboxes versus full committee review
  • Centralised, federated and embedded governance topologies
Building the Enterprise AI Asset Inventory (AI Registry)
  • Scope of the registry: custom models, LLM APIs, embedded SaaS AI features and agents
  • Core metadata fields: purpose, business owner, data sources, risk tier, vendor and oversight mode
  • Keeping the inventory evergreen via procurement and architecture hooks
Lifecycle Stage-Gates and Approval Workflows
  • Gate 1 (Intake & Triage), Gate 2 (Design & Data Sign-off), Gate 3 (Go-Live Authorization)
  • Post-deployment periodic re-validation and material change triggers
  • Conditional approvals, formal risk acceptance and exception handling
Governance Committee Charter and Cross-Functional Integration
  • AI Governance Board mandate, cadence and escalation authority
  • Avoiding duplicate reviews by integrating with InfoSec, privacy and procurement workflows
Board Reporting and Key Risk Indicators (KRIs)
  • Executive dashboard metrics: registry coverage, overdue reviews, incidents and compliance posture
  • A pragmatic 90-day rollout plan for standing up AI governance

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