About This Event
“Govern the models before the models start governing your decisions.”
A blended certificate programme of 100 to 130 study hours, completed with a PAC assessment, on generative AI, AI risk factors and responsible AI governance. You will learn how AI and machine-learning models fail, how to validate and monitor them, and how to build governance that your board and supervisors can rely on.
What You'll Explore
A PAC Risk & AI Governance Certificate, confirmed by assessment
An AI risk register template with scoring for key risk factors
A draft AI governance policy with roles and approval gates
A model-tiering and validation approach adapted for AI models
A monitoring checklist for model drift and generative AI outputs
Who Should Attend
Open to all qualifying staff, particularly: Model Risk Managers, Chief Risk Officers, Data Science and IT Leads, Internal Auditors, Governance and Compliance Staff.
Why This Course Matters
Understand the Risk
Generative AI and machine learning introduce risks that classic model validation was not built for, from bias to hallucination. Knowing these failure modes lets you set controls before a model reaches production.
Governance That Holds
Responsible AI frameworks define who approves, monitors and retires models. Clear accountability means an AI failure is caught by your controls, not by the public or a supervisor.
Assessed Credential
The PAC assessment confirms you can apply the material, not just follow it. Your institution gains staff with evidenced competence in a field where most organisations are still catching up.
Programme
Module 1
AI and generative AI foundations for risk professionals
You will build a working understanding of machine learning, large language models and generative AI, focused on how they are used in central banking and where their outputs can go wrong.
Module 2
AI risk factors: bias, explainability & data
You will analyse the key AI risk factors, including bias, explainability, data quality, privacy and security, and learn to score them in a risk register your committees can act on.
Module 3
Model risk management, validation & monitoring
You will adapt model risk management to AI, covering inventory, tiering, independent validation, performance monitoring and drift detection across the full model life cycle.
Module 4
Responsible AI governance frameworks & policy
You will compare the leading responsible AI frameworks and draft an AI governance policy for your institution, setting out roles, approval gates and escalation routes for high-risk use cases.
Module 5
Applied case study & PAC assessment
You will apply the full framework to a realistic AI use case, from risk assessment to governance sign-off, and complete the PAC assessment that confirms your certificate.
Standards & Faculty Benchmark
NIST AI Risk Management Framework
A voluntary framework to govern, map, measure and manage risks across the AI life cycle.
ISO/IEC 42001 AI Management Systems
The international management-system standard for organisations developing or using AI.
OECD AI Principles
Intergovernmental principles for trustworthy AI, covering transparency, robustness and accountability.
EU AI Act
A risk-based legal framework for AI, influential for firms and regulators beyond the EU.
Supervisors, auditors and technology vendors are converging on these frameworks as the common reference for AI risk. Building your governance on them means your controls will be recognised by others, and you avoid designing a bespoke approach that later needs rebuilding.
Is This Right for You?
- ☑You manage, validate or audit models used in your institution
- ☑Your institution is adopting or piloting AI and generative AI tools
- ☑You want an assessed credential, not just awareness training
Good to Know
Pitched at risk, audit and governance professionals; no coding is required, though comfort with basic statistics helps. Plan for 100 to 130 study hours across the blended format. You leave with the certificate, an AI risk register template and a draft AI governance policy.
The Bottom Line
Earn a credential and bring back an AI governance policy your board can adopt before the first serious model failure.
Recommended For
Open to all qualifying staff, particularly: Model Risk Managers, Chief Risk Officers, Data Science and IT Leads, Internal Auditors, Governance and Compliance Staff.