AI Quality and Risk Management
About This Course
By the end of this course, participants will be able to:
1. Analyse AI systems holistically across their lifecycle by integrating governance, quality, risk, data, security, and regulatory considerations using internationally recognised standards.
2. Evaluate AI use cases, systems, and providers by applying structured risk profiling, quality assessment, testing, and cybersecurity frameworks to determine fitness for purpose and compliance readiness.
3. Design an AI Quality Management and Data Governance approach that aligns organisational objectives, risk appetite, and lifecycle controls with relevant International Organization for Standardization (ISO) / International Electrotechnical Commission (IEC) and Institute of Electrical and Electronics Engineers (IEEE) standards.
4. Translate AI risk assessments into actionable requirements, controls, and assurance plans that address safety, security, legal, ethical, performance, and sustainability risks across stakeholder roles.
5. Develop an organisation-specific roadmap for responsible AI adoption that integrates regulatory awareness, organisational maturity, and continuous improvement mechanisms.
What You'll Learn
Entry Requirements
Basic IT knowledge