In this module, we will examine how AI can affect individuals, organizations, and society through privacy, bias, discrimination, security, and operational risks. We will explore harm taxonomies, OECD guidelines, ethical considerations, and the characteristics of trustworthy AI. We will also learn how governance controls, organizational culture, and responsible AI practices can help mitigate these risks.

In this module, we will explore how organizations can establish effective AI governance structures, policies, roles, and accountability mechanisms. We will examine stakeholder engagement, leadership support, training, and alignment between AI initiatives and enterprise risk strategies. We will also apply risk assessment techniques and explore the NIST AI Risk Management Framework and ARIA Program.

In this module, we will examine governance across the AI development lifecycle, from defining business problems and scope to testing, validation, and monitoring. We will explore stakeholder engagement, impact assessments, data governance, lineage, preparation, privacy-enhancing technologies, and feature engineering. We will also examine documentation practices such as model cards and conformity documentation that strengthen transparency and accountability.

In this module, we will explore the governance controls required to deploy and operate AI systems responsibly, including privacy, security, vendor, and third-party considerations. We will examine monitoring, model drift, periodic assessments, incident response, auditing, and transparency obligations. We will also compare deployment approaches such as cloud, on-premises, edge, fine-tuning, RAG, and agentic AI architectures.

In this module, we will examine the EU AI Act, including its scope, exemptions, terminology, AI literacy requirements, and risk-based classification system. We will explore obligations for providers, deployers, importers, and distributors, along with requirements for high-risk and general-purpose AI. We will also address transparency, documentation, human oversight, quality management, enforcement, and penalties.

In this module, we will explore the wider legal and standards landscape influencing AI governance, including U.S. federal, state, and local requirements and international regulatory developments. We will examine privacy principles, automated decision-making rules, bias audit requirements, and practical obligations for AI providers and deployers. We will also explore ISO/IEC 22989, ISO/IEC 42001, and ISO/IEC 42005 and their roles in strengthening AI governance.

In this module, we will consolidate the key concepts, frameworks, regulations, and governance practices covered throughout the course. We will reinforce essential knowledge spanning responsible AI, risk management, lifecycle controls, the EU AI Act, and other relevant laws and standards. We will connect these concepts to practical AI governance responsibilities and focused preparation for the IAPP AIGP certification exam.