Certificate in AI Governance

As Artificial Intelligence becomes embedded across business functions, organizations face increasing governance challenges related to accountability, transparency, ethical use, data privacy, security, regulatory compliance, and operational risk. Without proper governance frameworks, AI systems may expose organizations to reputational damage, legal liabilities, biased decision-making, and control failures.

Recognizing the need for structured oversight of emerging technologies, BOOST Consulting & Training has developed the Certificate in AI Governance program to equip professionals with a practical and risk-based approach to governing AI across the organization. This advanced-level program explains how to establish AI governance frameworks, policies, roles and responsibilities, risk assessment methodologies, monitoring controls, and assurance processes.

The course focuses on evaluating AI systems throughout their lifecycle—from design and development to deployment and continuous monitoring—ensuring responsible use, compliance, and alignment with organizational values and risk appetite. Participants will also explore how AI governance integrates with broader Governance, Risk, and Compliance (GRC) structures.

Through structured frameworks, case discussions, and applied exercises, participants will gain the skills to support AI governance initiatives that enable innovation while strengthening control, trust, and accountability.

• Understand the concept, purpose, and key pillars of AI governance
•  Identify governance risks related to AI such as bias, lack of transparency, privacy, and security exposure
•  Establish AI governance structures including oversight committees, ownership models, and escalation paths
•  Develop AI policies, standards, and procedures aligned with risk and compliance requirements
•  Apply AI risk management methods including risk assessment, control mapping, and monitoring indicators
•  Evaluate AI lifecycle controls including data governance, model training, testing, validation, and change management
•  Strengthen AI compliance readiness through documentation, traceability, and audit evidence management
•  Design governance mechanisms for third-party and vendor AI systems
•  Implement AI governance reporting dashboards, KPIs, and assurance practices