AI Governance courses

• Understand AI governance and why organizations need structured oversight to manage the development, procurement, deployment, use, monitoring, and retirement of AI systems.
• Explore accountability, transparency, fairness, safety, privacy, security, and meaningful human oversight as core principles of effective AI governance.
• Understand the complementary responsibilities of executive leadership, business functions, technical teams, legal, compliance, risk, security, privacy, and other organizational stakeholders.
• Build the foundations of an organization-wide AI governance framework that supports responsible innovation while maintaining clear ownership, oversight, and control.

• Understand the evolving international regulatory landscape for artificial intelligence and how emerging legal requirements can affect organizations operating across different jurisdictions.
• Explore major regulatory approaches, including the EU AI Act and other risk-based frameworks that establish obligations according to the characteristics and potential impact of AI systems.
• Identify governance and compliance obligations according to system type, organizational role, industry, jurisdiction, intended purpose, affected stakeholders, and level of risk.
• Translate applicable legal and regulatory requirements into practical governance activities, organizational responsibilities, documentation, controls, assessments, and evidence.

• Identify technical, ethical, legal, operational, financial, security, privacy, and reputational risks associated with the development and use of AI systems.
• Classify AI use cases according to factors such as potential impact, level of autonomy, data sensitivity, affected stakeholders, business criticality, and likelihood of harm.
• Conduct structured AI risk and impact assessments throughout the system lifecycle to identify risks before deployment and reassess them as systems or operating conditions change.
• Define mitigation measures, control requirements, escalation mechanisms, residual-risk decisions, and risk-acceptance criteria appropriate to the organization's risk appetite.

• Develop organizational policies that establish acceptable, restricted, and prohibited uses of AI for employees, business functions, technical teams, and external providers.
• Establish standards governing AI development, procurement, testing, deployment, monitoring, data use, security, documentation, and employee interaction with AI systems.
• Translate high-level governance principles into practical procedures, checklists, control requirements, approval gates, review criteria, and operational guidance.
• Create consistent organizational rules that enable responsible experimentation and innovation while maintaining appropriate control, accountability, and risk management.

• Build and maintain a centralized inventory of AI models, applications, vendors, services, and use cases so that the organization can understand where and how AI is being used.
• Classify AI systems according to their purpose, data sensitivity, level of autonomy, affected stakeholders, business impact, regulatory relevance, and overall risk.
• Establish governance checkpoints from initial ideation and assessment through development or procurement, testing, approval, deployment, monitoring, modification, and retirement.
• Create clear traceability and ownership across the complete AI lifecycle by documenting system owners, responsible teams, dependencies, approvals, controls, and material changes.

• Govern the collection, processing, storage, access, sharing, retention, and use of data across AI models, applications, and supporting infrastructure.
• Address privacy, confidential information, intellectual property, data quality, provenance, third-party data, and other information risks associated with AI systems.
• Establish access controls, security requirements, retention policies, data-classification rules, and responsible data practices appropriate to AI use cases and organizational risk.
• Integrate AI-specific data, privacy, and security governance with existing cybersecurity, privacy-management, information-governance, and enterprise data-governance frameworks.

See also: AI Security courses →

• Evaluate external AI models, platforms, APIs, cloud services, software products, and technology providers before adoption or integration into organizational processes.
• Assess vendor risks involving security, privacy, data usage, intellectual property, model transparency, reliability, service continuity, subcontractors, and regulatory compliance.
• Define contractual requirements, due-diligence procedures, assurance expectations, monitoring obligations, incident responsibilities, data-handling conditions, and exit strategies.
• Build a structured third-party AI governance process that manages vendor risk from initial selection and approval through ongoing monitoring, renewal, replacement, and termination.

• Address governance challenges specific to large language models, generative AI, Retrieval-Augmented Generation systems, copilots, AI assistants, and autonomous or semi-autonomous agents.
• Govern risks involving hallucinations, prompt injection, sensitive-data exposure, retrieval vulnerabilities, tool access, persistent memory, insecure actions, and excessive autonomy.
• Define permissions, guardrails, validation requirements, monitoring, human approval points, escalation rules, and acceptable levels of agent autonomy according to use-case risk.
• Establish adaptable governance controls that can manage rapidly evolving generative and agentic AI capabilities without unnecessarily preventing responsible experimentation and innovation.

• Design processes for continuously monitoring AI performance, risk, compliance status, incidents, control effectiveness, and material changes after deployment.
• Establish documentation practices including model and system records, decision logs, test results, risk assessments, approvals, monitoring evidence, and audit trails.
• Define governance KPIs, risk indicators, thresholds, incident-reporting requirements, review frequencies, escalation procedures, and management reporting mechanisms.
• Prepare AI systems and governance processes for internal assurance reviews, external audits, customer or partner assessments, and regulatory scrutiny through consistent and traceable evidence.

• Design an end-to-end AI governance operating model that connects executive leadership, business functions, technology teams, data, risk, legal, privacy, security, compliance, procurement, and internal assurance.
• Establish governance committees, ownership models, decision rights, approval workflows, organizational policies, risk frameworks, lifecycle controls, and monitoring structures.
• Define governance maturity levels, KPIs, reporting mechanisms, assurance activities, review cycles, and continuous-improvement processes that allow governance capabilities to evolve with organizational AI adoption.
• Build a practical enterprise roadmap for scaling AI responsibly while maintaining innovation, regulatory compliance, risk control, transparency, and clear organizational accountability.