Faysal AzizAI Governance Overview
Risk, responsibility and practical organisational controls
A one-day introduction to AI fundamentals, limitations, responsible-use principles, governance controls and organisational responsibilities.
Why this course
This one-day introductory course connects AI literacy with governance. Participants examine how model-based systems learn and generate outputs, why errors and bias occur, and how organisational controls can support responsible use.
The course surveys policies, risk review, lifecycle oversight, documentation, third-party risk and generative-AI challenges. It provides an initial governance vocabulary and roadmap, not a full implementation, legal-compliance determination or assurance certification.
Learning outcomes
The course teaches participants to:
- Explain basic AI, machine learning and generative AI concepts.
- Recognise common limitations, risks and failure scenarios.
- Distinguish governance, ethics, voluntary guidance and legal obligations.
- Describe responsible-use principles, human oversight and accountability.
- Identify policy, lifecycle, documentation, monitoring and vendor-review controls.
- Recognise prompt-injection, data-leakage and intellectual-property questions.
- Outline initial governance responsibilities and next steps.
Prerequisites
- Basic understanding of business and digital technologies
- No programming or prior AI experience required
8 modules
011 — AI fundamentals6 topics
- What is AI
- Types of AI systems
- Machine Learning fundamentals
- How AI models are trained
- Introduction to Generative AI and Large Language Models
- Illustrative AI uses across industries and the evidence needed to assess them.
022 — Risks and limitations6 topics
- Hallucinations and inaccurate outputs
- Bias and fairness concerns
- Privacy and security risks
- Data quality issues
- Explainability and transparency challenges
- Risks of overreliance on AI
033 — Governance principles5 topics
- What is AI governance
- Why AI governance matters
- Distinguish governance processes, ethical principles, legal requirements and voluntary frameworks.
- Responsible AI principles
- Human oversight and accountability
044 — Frameworks and controls5 topics
- AI policies and governance structures
- AI risk management concepts
- AI lifecycle governance
- Monitoring and audit considerations
- AI documentation and accountability
055 — Generative AI governance5 topics
- Risks associated with Generative AI
- Prompt injection and data leakage
- Intellectual-property and permitted-use questions for appropriate specialist review.
- Acceptable use policies
- Governing enterprise AI adoption
066 — Organisational responsibilities5 topics
- Roles and responsibilities
- Governance operating models
- Cross-functional collaboration
- Managing third-party AI risks
- Building a governance roadmap
077 — Emerging considerations4 topics
- Regulatory developments: identify applicable context and verify current requirements.
- Risk-management standards and frameworks, including voluntary NIST guidance; avoid assuming certification or automatic compliance.
- Tool-using agents and configured autonomy: permissions, oversight and accountability.
- Future governance challenges
088 — Review and discussion3 topics
- Key takeaways
- Initial practical recommendations and unresolved organisational questions.
- Discussion and participant questions
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