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Overview
This course equips professionals with the knowledge and tools to identify, assess, and mitigate AI-related risks within modern organisations. It explores the principles of AI governance, compliance, and ethics through globally recognised frameworks such as the NIST AI Risk Management Framework and the EU AI Act.
Learners will gain hands-on experience applying these frameworks to real-world scenarios involving bias, security vulnerabilities, and transparency challenges. By the end of the course, participants will understand how to embed AI risk management within organisational strategy to ensure responsible, compliant, and ethical AI adoption.
The course covers key international standards and frameworks that guide the development, deployment, and governance of AI systems. It covers standards, such as ISO 42001, ISO 42005, ISO 22989, and ISO 23894, ISO 38507, ISO 24028, ISO 23053 as well as frameworks like the NIST AI Risk Management Framework, and the MIT AI Risk Repository.
Prerequisites
Participants should have:
- A foundational understanding of artificial intelligence concepts and data governance principles
- Basic knowledge of organisational risk management or information security practices
- Familiarity with compliance and governance structures within a business environment
Target audience
This course is designed for:
- Risk, compliance, and governance professionals managing AI-related initiatives
- IT and security specialists responsible for evaluating AI systems and controls
- Data scientists, AI developers, and engineers integrating responsible AI practices
- Consultants advising on AI risk management and mitigation strategies
- Legal, ethical, and compliance advisors specialising in AI regulations
Learning Objectives
By the end of this course, learners will be able to:
- Explain the key concepts and principles of AI risk management and governance
- Apply frameworks such as the NIST AI Risk Management Framework and the EU AI Act to evaluate compliance and ethical considerations
- Identify and assess AI risks, including bias, data security, transparency, and accountability concerns
- Develop and implement AI risk mitigation and incident response strategies
- Integrate AI risk management into wider business and compliance frameworks
- Analyse real-world case studies to identify lessons learned and best practices for AI risk control
- Promote responsible AI use across the organisation through governance and continual improvement
Course Outline
Introduction to AI risk management
- Understanding AI’s opportunities and challenges in modern organisations
- Key terms, definitions, and risk management concepts
- Overview of global AI risk management standards and regulations
Learning outcomes, participants will be able to:
- Recognise the role of international AI standards and frameworks in guiding the governance, development, and deployment of AI systems.
- Explain the global landscape of AI regulations and their alignment across regions, including the EU, UK, U.S., and beyond.
AI risk identification, assessment, and measurement
- Techniques for identifying and classifying AI-related risks
- Quantitative and qualitative methods for risk assessment
- Assessing AI model reliability, data integrity, and ethical exposure
Learning outcomes, participants will be able to:
- Identify the fundamental components of artificial intelligence and how risks, impacts, and harms emerge from its use.
- Evaluate key sources of AI risk and the benefits of applying structured AI risk management practices.
- Outline the stages of the AI life cycle and assess how risks evolve throughout its phases.
AI risk mitigation, governance, and incident response
- Designing mitigation strategies aligned with compliance frameworks
- Developing governance structures for ethical AI deployment
- Planning incident response and escalation procedures for AI failures or bias events
Learning outcomes, participants will be able to:
- Explain the structure and purpose of an AI risk management program, including the role of essential controls and frameworks in minimising risks.
- Establish governance structures for AI risk, define AI system scope and boundaries, and perform a gap analysis to support effective oversight.
- Apply AI risk criteria and context establishment methods to align risk governance with organisational objectives.
- Identify AI-related risks, sources, events, and outcomes, while mapping risks across systems and processes.
- Assign AI risk ownership to ensure accountability, responsibility, and traceability in risk mitigation efforts.
AI risk monitoring and continual improvement
- Establishing metrics and KPIs for AI risk performance
- Continuous evaluation of AI systems through audits and impact assessments
- Integrating lessons learned into organisational governance frameworks
Learning outcomes, participants will be able to:
- Apply AI risk analysis approaches, using qualitative and quantitative methods to assess the probability, impact, and overall level of AI risks.
- Evaluate AI-related risks against established criteria (including EU AI Act categories) and prioritise them for effective treatment.
- Develop structured AI risk treatment plans that address documented risks through appropriate strategies and post-deployment activities.
- Determine suitable controls for implementing AI risk treatment strategies and ensure their proper integration within organisational processes.
- Review and continuously evaluate the effectiveness of applied controls to ensure ongoing AI risk mitigation and resilience.
AI risk management in business strategy
- Linking AI risk management with strategic planning and enterprise risk frameworks
- Ensuring accountability and ethical oversight in AI operations
- Building a culture of responsible and transparent AI innovation
Learning outcomes, participants will be able to:
- Implement effective AI risk monitoring and reporting practices, including performance metrics, recordkeeping, and lessons learned, to ensure transparency and accountability.
- Establish competence and awareness programs that address workforce skill gaps, support professional development, and sustain responsible AI operations.
- Evaluate the effectiveness of the AI risk management process through monitoring, internal audits, and performance reviews.
- Apply continual improvement practices to optimize AI risk management and strengthen organisational resilience.
Exams and assessments
Participants will complete a formal certification exam administered by PECB, post course. Certification fees and the exam voucher are included in the course price. Candidates who do not pass on the first attempt may retake the exam once for free within 12 months of the initial attempt. Knowledge checks, exercises, and quizzes are provided throughout the course to reinforce learning and readiness for the certification exam.
Hands-on learning
This course includes:
- Practical exercises based on real-world AI risk scenarios
- Exercise 1: AI life cycle
- Exercise 2: AI risk management program
- Exercise 3: AI risk treatment
- Exercise 4: AI risk monitoring and reporting
- Exercise 5: System context and purpose
- Exercise 6: AI risk governance and AI actors
- Exercise 7: Risk identification
- Exercise 8: Risk analysis and evaluation
- Exercise 9: Risk treatment
- Interactive group discussions and case-based simulations
- Workshops on developing and applying AI risk management frameworks
- Guidance from experienced instructors and access to over 450 pages of supporting materials
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Certificates of Achievement are issued at the end the course, either as a hard copy or via email. Read more here.
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