Establishes the foundations of AI governance, including its purpose, scope, principles, accountability, trustworthiness, human oversight, lifecycle context and relationship with risk, ethics, compliance and assurance.

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Examines how organizations design and operate AI governance, including strategy, leadership, decision rights, governance bodies, roles, policies, controls, accountability models, AI literacy and governance culture.

Covers the identification, assessment, treatment and ongoing management of AI risk, including risk taxonomies, appetite, impact assessments, due diligence, emerging risks, systemic risks and integration with enterprise risk management.

Explores how AI systems and use cases are governed throughout their lifecycle, from discovery, assessment and portfolio prioritization through design, acquisition, deployment, monitoring, change, suspension and retirement.

Examines the governance of data and information used by and generated through AI, including data quality, provenance, privacy, sensitive information, access, retention, intellectual property and lifecycle controls.

Explores responsible and ethical AI governance, including fairness, bias, human rights, transparency, explainability, meaningful human oversight, contestability, stakeholder participation and wider workforce and societal impacts.

Covers the methods and governance practices used to evaluate AI performance, reliability, safety, fairness, explainability and human oversight, and to establish evidence-based assurance throughout the AI lifecycle.

Examines AI-specific security threats, defensive controls, resilience and incident governance, including adversarial attacks, prompt injection, data leakage, model security, secure deployment, monitoring, recovery and organizational learning.

Covers governance of external AI providers, products and dependencies, including procurement, vendor due diligence, contracts, licensing, consumption, concentration risk, foundation models, open-source AI and AI value-chain accountability.

Examines the distinctive governance requirements of generative and agentic AI, including foundation models, prompts, grounding, generated content, agent autonomy, authority, tool use, human accountability, memory and multi-agent systems.

Explores the legal and regulatory environment for AI, including compliance frameworks, jurisdiction, regulatory classification, documentation, privacy, discrimination, consumer and employment law, intellectual property and major global AI regulatory regimes.

Examines how organizations monitor, measure, audit and continually improve AI governance, including controls, metrics, reporting, governance technology, internal and external assurance, maturity assessment and corrective action.

Provides a structured reference to the international standards, government frameworks, regulatory sources, security guidance and professional assurance practices underpinning the Affarico AI Governance curriculum, with mappings to relevant subjects and articles.