AI Governance Professional Training
Executive training for AI governance, risk, and regulatory implementation
Practical, operational AI governance
A practical training focused on operational AI governance for senior professionals navigating AI risk, regulation, and lifecycle responsibilities. Not a technical AI engineering course or certification exam preparation.
The focus is on understanding AI risk, regulatory expectations, lifecycle governance, supplier oversight, accountability, and how to translate AI governance into operational controls across organisations.
Who it is for
Data Protection Officers, CISOs, and GRC professionals
Public sector bodies, data protection authorities, and accreditation bodies
AI developers, software engineers, and technical teams
Startups and scale-ups building AI solutions
A practical foundation for governing AI in regulated environments
Cross-domain understanding of AI risk
Practical AI governance and risk management
Alignment with the EU AI Act and emerging regulation
Curated peer cohort, capped at ten participants
Two ways to engage
Half-Day Session
Executive Briefing
Half-day high-level session for leadership and decision-makers.
Three Half-Day Sessions
Professional Training
Three half-day sessions focused on operational implementation.
Private Organizational Delivery
Adapted to your context
Private delivery can be adapted to the organization's context, including:
Internal AI use cases
Governance maturity
Public sector or regulated environments
EU AI Act exposure
Supplier and procurement risks
Roles and responsibilities across teams
Participation
€1,800per participant
50% discount for startups
Limited seats per cohort
Private delivery available on request
Limited seats per cohort
Upcoming Cohort
August 24–26, 2026
Can also be delivered on-demand for organisations.
Program Schedule 2026
October 4–6
December 6–8
All sessions follow the same structure.
Seven modules covering the full scope of AI governance
Definitions of AI and common governance concepts
Different types of AI systems
Core concepts such as data, algorithms, models, and systems
AI system classification and context of use
Principles of responsible and trustworthy AI governance
AI harm taxonomy and how to identify AI risks
Individual risks such as bias, discrimination, and privacy harms
Organizational risks including reputational, operational, and legal exposure
Societal and systemic risks including misinformation and surveillance
Environmental and infrastructure-related AI impacts
EU AI Act risk-based classification
High-risk AI obligations and governance expectations
Interaction with GDPR and existing legal frameworks
Roles of providers, deployers, and users
Public sector and regulated environment considerations
Planning and design of AI systems
Data collection, quality, and preparation risks
Model development, testing, and validation
Deployment, monitoring, and incident handling
Decommissioning and lifecycle documentation
Governance roles and accountability structures
Risk identification across legal, technical, operational, and security domains
Integration with existing GRC and compliance frameworks
Use of standards such as ISO 42001, ISO 42005, ISO 27001, and NIST AI RMF
Moving from policy to operational controls
Embedding AI governance into projects and procurement
Supplier and third-party AI risk management
Monitoring, auditability, and evidence collection
Cross-functional collaboration between legal, risk, IT, security, and business teams
Practical implementation challenges and lessons learned
Generative AI and agentic AI risk
Overreliance on predictive systems
Misuse scenarios including profiling, manipulation, and surveillance
AI risk across critical infrastructure and public services
Scaling governance as AI adoption increases
Participants will leave with
Practical ability to implement AI governance and risk management so that AI systems can be deployed with control and accountability
Alignment with EU AI Act, ISO 42001, and ISO 42005 so that regulatory expectations are translated into real operations
Ability to connect AI governance with risk management, data protection, and procurement so that decisions are made across domains, not in silos
Tools to challenge suppliers and conduct AI due diligence so that external risk is controlled before it enters the organisation
Capability to identify and classify AI risks across systems, processes, and governance structures so that governance becomes proactive rather than reactive
Stronger ability to procure, assess, and govern AI solutions so that long-term legal, operational, and financial risks are reduced
Bring this training to your organization
Available as a curated cohort or as a private organizational session tailored to your internal context, AI use cases, and regulatory exposure.