Lead AI Risk Manager - 21/10 - 22/10

Artificial Intelligence is creating unprecedented opportunities, but it also introduces new categories of risks that extend beyond traditional IT and cybersecurity. Bias, explainability, security vulnerabilities, regulatory compliance and ethical concerns require organisations to adopt a structured approach to AI risk management.

The Lead AI Risk Manager is an intensive two-day certification training that equips professionals with the knowledge and practical skills to identify, assess, evaluate and mitigate AI-related risks throughout the AI lifecycle. Based on internationally recognised frameworks such as the NIST AI Risk Management Framework, ISO/IEC 23894, ISO/IEC 42001 and the EU AI Act, this course enables participants to establish effective AI risk governance and build trustworthy AI systems. Upon completion of the training, participants are fully prepared for the official PECB Lead AI Risk Manager certification examination.

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

[ Build trusted AI systems: Learn how to proactively identify and manage AI risks before they impact operations or compliance. ]

[ Work with recognised frameworks: Apply internationally accepted frameworks including the NIST AI RMF, ISO/IEC 23894, ISO/IEC 42001 and the EU AI Act. ]

[ Design and implement AI risk governance, monitoring and continuous improvement processes across the complete AI lifecycle. ]

[ Develop practical AI risk treatment plans that support responsible AI adoption while balancing innovation, compliance and business objectives. ]

Full programme information

The Lead AI Risk Manager is delivered as an intensive two-day training programme, structured around five integrated competency domains. Throughout the training, participants work with practical examples, real-world scenarios and recognised AI risk management frameworks to prepare for both operational implementation and the official PECB certification exam.

Module 1 – AI Risk Principles, Concepts & Regulations

Fundamentals of AI risk management
AI lifecycle and emerging AI risks
AI harms, impacts and trustworthiness
International AI standards and frameworks
EU AI Act and global AI regulations
ISO/IEC 23894, ISO/IEC 42001 and NIST AI RMF
Ethical and responsible AI principles

Outcome: Participants gain a solid understanding of the AI risk landscape, international standards and regulatory obligations that form the foundation of effective AI risk management.

Module 2 – AI Risk Governance & Risk Management Programme

Establishing AI risk governance
Roles and responsibilities
AI risk management processes
Governance structures and accountability
AI policies and organisational controls
Risk culture and management commitment
Integration with enterprise risk management

Outcome: Participants learn how to build and govern an AI risk management programme that aligns with organisational objectives, governance frameworks and regulatory expectations.

Module 3 – AI Risk Identification & Analysis

AI risk identification techniques
Risk sources across the AI lifecycle
Bias and fairness risks
Security and adversarial threats
Privacy and data governance risks
Transparency and explainability challenges
Risk analysis methodologies

Outcome: Participants develop the ability to systematically identify, categorise and analyse AI-related risks before they impact business operations or regulatory compliance.

Module 4 – AI Risk Evaluation, Treatment & Monitoring

AI risk evaluation methodologies
Risk prioritisation
Selecting appropriate controls
Designing AI risk treatment plans
Continuous monitoring and reporting
Measuring control effectiveness
Continuous improvement and post-deployment monitoring

Outcome: Participants learn how to evaluate AI risks, implement effective mitigation strategies and continuously monitor AI systems throughout their operational lifecycle.

Module 5 – Organisational Learning & Performance Improvement

Incident response for AI-related events
Lessons learned and continuous improvement
AI risk reporting
Performance metrics and KPIs
Organisational learning
Maintaining long-term AI resilience

Outcome: Participants understand how organisations can continuously improve AI governance through structured monitoring, performance measurement and organisational learning.

Certification Exam

Following the training, participants can take the official PECB Lead AI Risk Manager examination, covering:

AI risk principles, concepts and regulations
AI risk governance and management programmes
AI risk identification and analysis
AI risk evaluation, treatment and monitoring
Organisational learning and continual improvement

Outcome: Successful participants demonstrate the competence required to lead AI risk management initiatives and earn the internationally recognised PECB Lead AI Risk Manager credential.

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We offer this training course in a hybrid format. This allows us to tailor the course to your needs as much as possible. The price shown is for attending the course in person.

Would you prefer to attend the training course online? Please feel free to contact us for a bespoke quote.

ADDED VALUE

Build trusted AI systems: Learn how to proactively identify and manage AI risks before they impact operations or compliance.

Work with recognised frameworks: Apply internationally accepted frameworks including the NIST AI RMF, ISO/IEC 23894, ISO/IEC 42001 and the EU AI Act.

Strengthen AI governance: Develop governance structures, policies and controls that enable responsible AI adoption across the organisation.

Prepare for evolving regulation: Gain practical knowledge to navigate rapidly changing AI legislation and regulatory expectations.

Earn an internationally recognised certification: Demonstrate your expertise with the PECB Lead AI Risk Manager certification and position yourself as a specialist in responsible AI governance and risk management.