Accountability & ownership
Understand who proposes, approves, oversees and reviews AI use, with clear responsibilities and escalation routes.
AI GOVERNANCE · IN DEVELOPMENT
Responsible AI begins
with accountable people.
IDPI is developing education and assessment pathways to connect AI governance principles with the decisions, oversight and evidence organisations need in practice.
Proposed learning direction. Named credentials, final syllabuses, assessment formats, eligibility, fees and launch dates have not been announced.
PROPOSED LEARNING THEMES
Understand the system.
Govern the lifecycle.
Risk & impact
Examine intended use, affected people, limitations and potential harms before and during deployment.
Data & privacy
Connect data sourcing, purpose, quality and lifecycle decisions with privacy and governance responsibilities.
Human oversight
Explore meaningful review, appropriate intervention, transparency and communication of system limitations.
Lifecycle controls
Consider proportionate checks across procurement, development, deployment, change and retirement.
Evidence & monitoring
Document decisions, test assumptions, monitor outcomes and track corrective action.
CONNECTING DISCIPLINES
Privacy and AI governance
share a practical foundation.
Both require ownership, sound decisions, proportionate controls and evidence. AI governance also raises distinct questions about system behaviour, human oversight and impact.
CPGA and CPIP currently describe privacy-focused competencies. They are not presented as comprehensive AI governance credentials. AI-specific learning and assessment information will be published as it is confirmed.
Explore AI governance and faculty enquiries ↗YOUR NEXT STEP