Operationalizing AI Governance: From Framework to Practice
Regulatory frameworks set the destination, but few organizations know how to build the road to get there. This two-day workshop gives compliance, risk, and technology leaders the operational blueprint to translate AI governance principles, including ISO/IEC 42001 and the EU AI Act, into enforceable policy and audit-ready practice.
> 65,000
students trained since 2001
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Experienced consultants
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Category
Artificial Intelligence
Training Type
Workshop
Level
Professional
Closing the gap between AI policy and AI practice
Most organizations already have an AI policy document. Far fewer have an AI governance system that functions inside daily operations. As artificial intelligence adoption accelerates, the space between written commitments and operational enforcement is where accountability gaps, compliance failures, and reputational risk take hold.
Operationalizing AI Governance: From Framework to Practice is a two-day, hands-on program built for organizations that have moved past the question of why to govern AI and now need to answer how. Grounded in global standards including ISO/IEC 42001 and the EU AI Act, the workshop gives participants a structured method for converting abstract principles into enforceable policy, defined accountability, and auditable controls.
What “Operationalizing AI Governance” actually means
Operationalizing AI governance means turning a policy document into a working system: a named committee with veto authority, a risk classification method every team applies consistently, and a documented process for assessing AI systems before they reach production. This workshop builds all three, using the ISO/IEC 42001 and EU AI Act frameworks as the reference structure.
What this workshop covers
Day one is about building the foundation. You’ll map the EU AI Act’s four-tiered risk model, apply ISO/IEC 42001 Clause 4 to your own organization’s context, and turn principles like fairness and accountability into commitments your teams can be held to. Then you’ll structure an AI Ethics Committee and apply the Three Lines of Defense model, so accountability sits exactly where it belongs.
Day two is where it gets tested. You’ll classify AI use cases by risk tier, run a full AI Impact Assessment before a model goes live, and pick risk treatments that will satisfy your executive sign-off process. Then comes the part most programs skip: shadow AI, unassessed vendor models, and training data nobody has checked for bias. You’ll leave with a plan for all of it.
Who Should Attend
This workshop is designed for cross-functional leaders responsible for the safe adoption and oversight of enterprise AI:
- Compliance, Risk and Legal Managers, including Chief Risk Officers, General Counsel, and Compliance Officers responsible for enterprise risk alignment
- IT Leaders and Technical Owners, including CTOs, CIOs, data scientists, and developers who build, acquire, or deploy AI systems
- Corporate Boards and Audit Leaders, including directors and internal auditors serving as the third line of defense
- Business Unit Leaders driving digital transformation or procuring third-party AI tools
A working understanding of corporate risk management, IT governance, or data privacy principles is recommended before attending.
What You Will Learn
By the end of this workshop, you will be able to:
- Translate Frameworks into Practice: Convert global standards (e.g., ISO/IEC 42001, EU AI Act) and local guidelines into clear, enforceable enterprise policies and responsibilities.
- Establish Structural Governance: Implement the Three Lines of Defense (3LOD) model and build an authoritative, cross-functional internal AI Ethics Committee.
- Execute Risk Management: Classify AI use cases by risk tier and confidently run comprehensive AI Impact Assessments (AIIA) prior to deployment.
- Enforce Operational Controls: Apply technical safeguards to manage data privacy, mitigate the risks of “shadow AI,” and govern third-party vendor models effectively.
Why it’s worth two days out of your calendar
Because governance done badly costs you twice: once in fines and failed audits, and once in the AI projects that stall out because nobody trusts the process. Done well, clear approval paths and defined accountability let your teams move fast, because the guardrails are already built.
You’ll leave with audit-ready documentation mapped to ISO/IEC 42001 and the EU AI Act. You’ll leave with a sharper read on your real AI risk exposure. And you’ll leave with a finished accountability matrix and risk mitigation plan, built for your organization and ready to use the moment you’re back at your desk.
Two interactive drills, a Governance Blueprint Mapping exercise and a Red Team Simulation, mean you walk away with working documents, not just notes. Available in classroom or virtual instructor-led format. Malaysia-registered employers can claim this course under HRDC.
Check the Training Schedules for the next available date, or Contact Us to bring this workshop to your team.
Duration
2-days
Course Delivery
Classroom or Virtual Classroom
Prerequisites
- A strategic or operational role involving the planning, development, compliance, or deployment of technology/AI within an organization.
- A basic understanding of organizational risk management or corporate governance. (Optional but recommended)
- Familiarity with fundamental data privacy principles, such as Malaysia’s Personal Data Protection Act 2010 (PDPA)
HRDC Claimable Training: Invest in Your Future!
Did you know? All our trainings are eligible for HRDC claiming.
What our clients say
Concepts Covered
Day 1: Demystifying Frameworks & Setting Up the Guardrails
Day 2: Risk Management & Operational Compliance
Demystifying Frameworks & Setting Up the Guardrails
- The Global Regulatory Shift: Navigating the EU AI Act’s four-tiered risk model (Unacceptable, High-Risk, Limited Risk, Minimal Risk)
- ISO/IEC 42001 Fundamentals: Using Clause 4 to analyze the organizational and legal context for an AI Management System
- Translating Standards: Mapping high-level principles (Fairness, Transparency, Accountability) into baseline corporate commitments
Structural Governance & Cross-Functional Roles
- The AI Ethics Committee: Structuring an oversight body with the authority to review and veto AI deployments
- The Three Lines of Defense (3LOD): Assigning accountability across developers (1st line), compliance (2nd line), and internal audit (3rd line)
- Leadership & AI Policy: Crafting documented, auditable AI acceptable-use policies
- Interactive Drill 1: Governance Blueprint Mapping
AI Risk Classification & Mitigation
- Identifying AI Risk Typologies: Understanding security, enterprise, legal, scalability, and “black box” risks
- AI Impact Assessments (AIIA): Executing mandatory stakeholder and privacy impact assessments before moving high-risk models to production
- Risk Treatment Planning: Selecting mitigation controls and gaining executive sign-off for residual risks
Technical & Operational Controls
- Data Privacy & Provenance: Establishing safeguards for training data and evaluating dataset bias
- Managing Shadow AI: Creating a centralized AI inventory to uncover and control unauthorized employee AI usage
- Vendor Management: Navigating the shared-responsibility matrix for third-party and vendor-supplied AI models
- Interactive Drill 2: Red Team Simulation/Case Study
Training Schedules
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