Responsible AI for Corporate Governance, Risk & Compliance

Name: Responsible AI for Corporate Governance, Risk & Compliance
Brand: Generative AI
SKU: CL-RAIGRC
Price: 3200 MYR
Availability: InStock

RM 3,200.00

Responsible AI for Corporate Governance, Risk & Compliance is a strategic, instructor-led programme designed to help GRC professionals manage the opportunities and risks introduced by artificial intelligence. As AI systems become embedded in enterprise decision-making—from predictive analytics to generative tools and autonomous agents—organizations must strengthen governance structures to ensure accountability, transparency, and regulatory alignment.

AI introduces new categories of risk that traditional control frameworks may not fully address. These include algorithmic bias, explainability limitations, hallucinations in generative models, cybersecurity vulnerabilities, and systemic supply chain exposure. This course provides a structured approach to understanding these risks and integrating AI governance into existing enterprise risk management and compliance frameworks.

Over two days, participants explore three AI paradigms—Predictive, Generative, and Agentic—and examine how each impacts governance, risk oversight, and compliance obligations. The course incorporates emerging regulatory and policy frameworks, including NIST AI RMF, the EU AI Act, Malaysia’s AIGE guidelines, and PDPA requirements. Participants learn how to interpret these frameworks and translate them into practical control mechanisms and governance structures.

Training Duration: 2 Days

Certificate Of Completion Available
Group Private Class
VILT Class Available
HRD Corp SBL-Khas Claimable

Artificial Intelligence (AI) is transforming how corporate and enterprise functions approach governance, risk, and compliance (GRC). From automated decision engines and generative content tools to regulatory modelling and vendor risk management, AI introduces both unprecedented capabilities and complex oversight challenges.

This 2-day course equips professionals with a structured understanding of AI technologies—Predictive, Generative, and Agentic—and their operational impact on GRC. Participants will explore emerging regulatory frameworks such as the NIST AI RMF, EU AI Act, and Malaysia’s AIGE guidelines, alongside practical issues including hallucination, bias, data leakage, and supply chain exposure.

Through hands-on modules and real-world case studies, the course provides actionable frameworks for AI policy design, governance ownership, risk mitigation, and responsible tool adoption. By the end of the course, participants will be able to evaluate AI risks, implement control measures, and support enterprise-wide governance models that balance innovation with accountability.
Upon completion of this course, participants will be able to:

Analyze AI paradigms and their enterprise use cases in GRC functions by differentiating Predictive, Generative, and Agentic AI and mapping their lifecycle stages to appropriate governance and risk oversight requirements with accurate classification and alignment to business context.

Evaluate AI regulatory and policy frameworks by interpreting NIST AI RMF, EU AI Act, Malaysia AIGE, and PDPA requirements to determine appropriate control design and hosting strategies with clear alignment to organizational compliance obligations.

Assess AI-related risk exposures by examining bias, explainability, hallucination, cybersecurity, and sustainability factors to determine their potential impact on compliance decision-making with structured risk categorization and justification.

Design AI control strategies and mitigation mechanisms by integrating human-in-the-loop governance, vendor vetting

criteria, and risk-based model selection principles into existing risk registers and assurance practices with documented alignment to organizational risk tolerance.

Analyze the global AI supply chain by mapping systemic dependencies, third-party exposures, and geopolitical risks to enterprise risk management frameworks with clear categorization of upstream and downstream risk impacts.

Design an enterprise AI governance model by structuring ownership roles, RACI matrices, and governance KPIs across the AI lifecycle with clear accountability, performance indicators, and alignment to organizational impact tiers.

Develop a responsible AI control framework by drafting policies, audit checklists, and GenAI usage guidelines integrated into GRC operating models with embedded control checkpoints and documented compliance considerations.

Evaluate strategic implications of AI on the future of GRC by assessing regulatory trends, assurance standards, and workforce shifts to formulate a future-ready roadmap with defined priorities, capability requirements, and measurable governance milestones.
Risk, Compliance, and Audit Professionals

IT Governance Managers

GRC Analysts and Officers

Executives involved in digital transformation
Module 1: AI Foundations and Paradigms for GRC

Overview of how AI technologies are applied in GRC functions across industries

Differentiating Predictive AI, Generative AI, and Agentic AI with enterprise examples

Fundamentals of predictive AI: supervised, unsupervised, and reinforcement learning

GenAI models in the market: architecture, trade-offs, and GRC-relevant use cases

Introduction to Agentic AI: autonomous systems, workflow integration, risk oversight

Lifecycle of AI development: training pipelines, evaluation, and deployment

Module 2: Regulatory and Policy Frameworks for AI

Key takeaways from NIST AI Risk Management Framework for control design

Implications of the EU AI Act for enterprise GRC adoption strategies

Malaysia National Guidelines on AI Governance and Ethics (AIGE) in practice

PDPA, privacy obligations, and training data risks for model development

Prompting safety guidelines and data leakage considerations in GenAI usage

Trade-offs across AI hosting models (cloud, on-prem, hybrid): risk vs cost vs complexity

Module 3: Double-Edged Risks of AI

Algorithmic bias and fairness risks in compliance decision-making

Explainability limitations in high-impact AI decisions

Hallucinations and factual reliability in GenAI-generated content

Emerging threats: deepfakes, misinformation, and AI-enabled scams

Adversarial attacks and cybersecurity vulnerabilities: model inversion & prompt injection

Environmental sustainability of large-scale AI: energy, compute, and carbon exposure

Module 4: Control Strategies and Mitigation Mechanisms

Human-in-the-loop governance and layered risk management architectures

Model selection principles: aligning the right AI tool to business risk tolerance

Detection tools for AI-generated content and their current limitations

Risk-based tool vetting: evaluating AI vendors and GenAI tools for GRC compatibility

Adapting corporate risk registers and assurance practices for AI-driven processes

Module 5: The Global AI Supply Chain and Macroeconomic Risks

Mapping the 8-layer global AI supply chain and systemic dependencies

Managing upstream risks from AI chipmakers, data vendors, and foundation model providers

Aligning third-party AI risks to existing vendor management frameworks

Regulatory and geopolitical exposure in the global AI infrastructure ecosystem

Framing AI as a supply chain risk category within enterprise risk management

Module 6: AI Governance Models, Ownership & Leadership

Common governance challenges in adopting AI across business units

Governance ownership models: risk, compliance, IT, legal, cross-functional committees

Emerging GRC roles

Structuring governance matrices: RACI roles across lifecycle and AI impact tiers

Enterprise case studies: structured governance in AI adoption

KPIs for governance maturity: model reliability, safety, efficiency, and compliance

Module 7: Building a Responsible AI Control Framework

Generative AI in practice: drafting policies, audit checklists, and risk frameworks

Integrating GenAI into GRC operating models with embedded control checkpoints

Designing and documenting GenAI usage guidelines tailored to regulated industries

Incorporating GenAI into vendor assessment and internal audit workflows

Hands-on activity: prompting for GRC outcomes (policy drafting, gap assessments)

Module 8: The Future of GRC in an AI-Driven Landscape

Strategic foresight: how AI transforms roles across GRC, audit, and compliance

ISO and regulatory certifications shaping the AI governance future (e.g., ISO 42001)

Talent shifts: growing demand for AI-aware GRC professionals and hybrid roles

AI assurance trends: model audits, traceability standards, and third-party validations

Building a future-ready GRC roadmap: upskilling, tooling, and cross-functional readiness

Frequently Asked Questions

What is Responsible AI in a GRC context?

Responsible AI refers to implementing artificial intelligence systems with governance controls, risk management processes, and compliance measures to ensure transparency, fairness, and accountability.

Who should attend a Responsible AI for GRC course?

This course is suitable for risk, compliance, audit, IT governance professionals, and executives responsible for overseeing AI adoption within organizations.

What regulatory frameworks are covered in this course?

The course covers frameworks such as the NIST AI Risk Management Framework, EU AI Act, Malaysia’s AIGE guidelines, and PDPA-related considerations.

What AI risks are addressed in this program?

The course addresses bias, explainability limitations, hallucinations, cybersecurity vulnerabilities, supply chain exposure, and sustainability risks.

How does this course help with AI governance design?

Participants learn how to structure AI governance models, assign ownership roles, define RACI matrices, and integrate AI controls into enterprise risk management frameworks.

Is this course HRDC Claimable?

Yes. This course is HRDC claimable, subject to HRDC approval and company eligibility requirements.

Can this course be customized for our organization?

Yes. The course can be tailored to reflect your organization’s industry regulations, risk appetite, governance structures, and digital transformation initiatives.

Does this course include practical policy drafting exercises?

Yes. Participants will draft policies, audit checklists, and GenAI usage guidelines aligned with governance and compliance requirements.

Responsible AI for Corporate Governance, Risk & Compliance

OVERVIEW

OBJECTIVES

AUDIENCE

COURSE MODULE

Frequently Asked Questions

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