AI Governance

Artificial IntelligenceExecutiveGovernanceAllGovernment of Canada
Written By Stephen Davies

Audience: Executive Leadership

Duration: 0.5 Day

Delivery: Virtual or in-person

Course description:

This course equips senior leaders with a clear understanding of how to govern AI responsibly. Covering ethics, bias, risk, security, sovereignty, compliance, and the disciplined management practices required to oversee AI projects in defence environments.

Course overview:

  • Ethics and bias in AI — how to identify, mitigate, and govern unintended impacts.

  • Algorithmic Impact Assessment (AIA) — assessing the impact of autonomous decision making. What it is, when it applies, and how it strengthens oversight and accountability.

  • AI risk management — thinking about operational, legal, reputational, and mission‑critical risks that are unique to AI.

  • Legislation, directives, and policy obligations — the Government of Canada requirements that shape compliant AI adoption.

  • AI security — protecting models, data, and systems from adversarial threats, manipulation, and misuse.

  • AI sovereignty — ensuring control over data, models, supply chains, and decision‑support systems.

  • Managing AI projects — governance structures, roles, controls, and lifecycle practices that enable safe, accountable, and mission‑aligned AI delivery.

By the end of this course, participants will be able to:

  • Explain the core principles of responsible AI governance and why they matter in defence and federal environments

  • Identify, assess, and mitigate ethical risks and unintended impacts, including bias in data and models

  • Understand when Algorithmic Impact Assessments (AIA) apply and how they strengthen oversight and accountability

  • Evaluate operational, legal, reputational, and mission‑critical risks associated with AI systems

  • Interpret the legislation, directives, and policy obligations that govern AI adoption across the Government of Canada

  • Recognize key AI security considerations, including protecting models, data, and systems from adversarial threats

  • Assess AI sovereignty requirements, including control over data, models, supply chains, and decision‑support systems

  • Understand AI project governance structures, roles, controls, and lifecycle practices that ensure safe, compliant, mission‑aligned delivery

Stephen Davies
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