AI Governance for Leaders
AI Regulations, Controls & Operating Discipline
When AI does the work, who answers for it?
A practical, two-hour live masterclass on governing AI through clear accountability, effective controls and evidence that stands up to scrutiny, under the EU AI Act and Asia’s regulators.
Why this masterclass matters
AI is no longer confined to technology teams or experimental projects. It is embedded in financial analysis, recruitment, customer interactions, procurement, risk assessment and everyday business decisions, and AI agents are beginning to execute tasks, access enterprise systems and act with varying degrees of autonomy.
So who is accountable when an AI system produces an incorrect decision, uses sensitive information inappropriately or acts beyond its intended authority? That is not simply a technical problem. It is a question of corporate governance, operational risk and management accountability.
Regulators are moving too. The EU AI Act sets obligations on risk, transparency, human oversight and accountability. Singapore’s AI governance frameworks and the MAS AI Risk Management Guidelines give practical direction, particularly for financial institutions. Across the Gulf, heavy AI investment is raising the bar for operational safeguards, data governance and vendor accountability.
The approaches differ, but organisations face the same management questions:
- What AI systems are we using, and for what purposes?
- Which applications carry material financial, operational, legal or reputational risk?
- Who is authorised to approve AI deployment and accept its risks?
- What limits should apply to AI agents and automated decisions?
- How do we establish effective human oversight?
- What evidence shows that governance controls operate as intended?
The challenge is to translate governance principles and regulatory expectations into controls that function in real business processes.
What makes this masterclass different
Many AI governance programmes explain frameworks and responsible-AI principles. This one focuses on the decisions boards, senior management and operational leaders must make when AI is deployed, connecting four dimensions:
Regulatory expectations
Understanding the relevant obligations and how they differ across jurisdictions.
Governance and accountability
Establishing decision rights, risk ownership and management oversight.
Operational controls
Defining what AI systems and agents may do, how they are monitored and when intervention is required.
Evidence and assurance
Showing that governance arrangements are documented, implemented and capable of review.
What we will cover
AI governance and the regulatory landscape
- EU AI Act: key obligations, implementation milestones and implications for organisations outside Europe.
- Singapore: IMDA governance frameworks and MAS AI risk management expectations.
- Relevant governance developments across the Gulf and wider Asia.
- How ISO/IEC 42001 and the NIST AI RMF relate to regulatory requirements.
- Why regulatory compliance and effective risk management are related but not identical.
Know your AI: inventory and risk classification
- Identifying AI systems, models, agents and third-party applications.
- Provider, deployer and business-owner responsibilities.
- Assessing AI risk by purpose, autonomy, impact and context.
- Prioritising the applications that need enhanced oversight.
Who decides? Governance and accountability
- Responsibilities of boards, executive management and business owners.
- The roles of technology, risk, compliance, legal and internal audit.
- Approval authorities and risk acceptance.
- Human oversight, escalation and intervention arrangements.
- Connecting AI governance with existing enterprise risk management.
Generative AI, autonomous agents and third-party risks
- Governing AI that generates content, recommends decisions or executes actions.
- Defining permitted activities and limits on agent autonomy.
- Managing risks from third-party models and vendors.
- Why outsourcing technology does not remove organisational accountability.
- Practical case discussion.
Controls, evidence and operating discipline
- Translating governance policies into actionable controls.
- Documentation, testing, monitoring and review processes.
- Maintaining evidence for internal audit, management oversight and regulatory scrutiny.
- Identifying control gaps and setting implementation priorities.
What you will take away
AI regulatory overview
Key governance developments and obligations across Europe and selected Asian jurisdictions.
AI inventory template
A structured way to identify systems, business purposes and accountable owners.
Risk classification worksheet
Practical criteria for assessing AI use cases and setting oversight requirements.
Governance decision-rights matrix
Who develops, approves, deploys, monitors and reviews AI systems.
Agentic AI and vendor oversight checklist
Key safeguards for autonomous systems and third-party providers.
AI governance action plan
A practical starting point for prioritising controls and management responsibilities.
These resources support organisational implementation and do not, by themselves, establish legal compliance or control effectiveness.
Who should attend
For leaders and professionals responsible for AI-related decisions, risks, governance or oversight. No coding or advanced technical knowledge is required.
Board members and senior executives
Your responsibilities for AI governance and organisational accountability.
Chief risk, compliance and legal officers
Overseeing regulatory obligations, enterprise risks, policies and internal controls.
CIOs, CTOs, chief data officers and AI leaders
Responsible for AI adoption, technology investment and deployment.
Internal audit and assurance professionals
Evaluating AI governance arrangements and the evidence behind effective controls.
Business and operational leaders
Implementing AI in finance, HR, procurement, customer operations and other functions.
Consultants and professional advisers
Supporting AI governance, technology risk and regulatory implementation.
Relevant across banking, insurance, financial services, professional services, technology, healthcare, manufacturing, infrastructure and the public sector.
Designed for an international audience
Europe
Organisations addressing EU AI Act obligations, including those procuring, developing or deploying AI.
GCC
Enterprises, financial institutions and public sector organisations strengthening AI governance as adoption expands.
Singapore and Southeast Asia
Organisations turning responsible-AI guidance and supervisory expectations into operational controls.
International businesses
Multinationals seeking one consistent internal approach while respecting local differences.
Your facilitator
Dr Nisha Kohli, PhD, CA, CFA
Founder & CEO, CorpStage
Dr Kohli brings more than two decades of experience in corporate governance, finance, risk management and executive capability development. Her work spans enterprise governance, sustainability-related financial risks, organisational controls and practical governance approaches for emerging technologies.
Through CorpStage and its AIVARA initiative, she helps organisations translate AI governance expectations into accountable decision-making, operational controls and evidence-based oversight. She developed CorpStage’s AI governance professional certification pathways and contributes to executive and professional education.
Thursday 19 November 2026: times by region
Register your place
The question is not only whether your organisation is adopting AI. It is whether you can demonstrate that AI is being governed effectively.
Tickets are sold through Eventbrite (USD 175 per participant). Refunds up to 7 days before the event. Joining instructions are shared with registered participants before the event.
Register on Eventbrite →Questions? Email iris@corpstage.com.