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Certificate in AI Governance

Frameworks, Policies, Ethics, and Governance Controls for Responsible and Shadow AI

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Why Choose Us

The Certificate in AI Governance training course equips governance, compliance, risk and technology professionals to design, implement and sustain responsible AI governance. It provides a structured framework covering ethical principles, international regulatory landscapes, AI risk management, bias assessment and Shadow AI governance. Participants also develop the strategic capability to create an enterprise-wide AI governance blueprint. This practical knowledge supports rigorous and credible oversight of AI across an organisation.

AI governance is now a board-level imperative for organisations using AI in hiring, lending, healthcare, security or operations. Increasing regulatory duties, ethical accountability and operational risks demand structured governance frameworks rather than policy statements alone. The course examines risk registers, bias detection, explainability frameworks, generative AI governance, NIST and ISO models, procurement controls and Shadow AI oversight. A capstone exercise enables delegates to design a complete AI governance blueprint tailored to their organisation.

This Certificate in AI Governance training course will highlight:

  • Applying responsible AI ethics principles within governance frameworks
  • Navigating global AI regulations and compliance obligations
  • Managing AI risks through structured registers and controls
  • Assessing bias and establishing explainability frameworks
  • Governing generative AI and overseeing Shadow AI
  • Developing an organisation-specific, enterprise-wide AI governance blueprint
Why Choose Certificate in AI Governance
Course Goals

What are the Goals?

The Certificate in AI Governance Course aims to build comprehensive capability in AI governance — beginning with fundamental ethical principles and regulatory compliance, and progressing through risk management, bias evaluation, Shadow AI governance, and the development of enterprise AI governance strategies.

Upon completing this course, participants will be able to:

  • Describe AI governance definitions, scope, primary drivers, and essential ethical principles, including fairness, accountability, transparency, and privacy
  • Assess global approaches to AI governance and establish the business case for responsible AI in their organisation
  • Interpret global AI regulations, including GDPR and regional data protection legislation, and perform regulatory impact assessments
  • Implement compliance frameworks for high-risk AI systems, including obligations relating to documentation, transparency, and reporting
  • Recognise and evaluate AI risks across technical, operational, ethical, and societal dimensions, and use strategies for detecting and mitigating bias
  • Use Explainable AI methods and tools, and oversee the AI model lifecycle, including testing, validation, and audit trails
  • Create AI governance structures, including committees, roles, accountability models, and oversight responsibilities
  • Manage Shadow AI — including its underlying causes, governance gaps, and the integration of oversight within existing governance frameworks
  • Use NIST, ISO, and organisational governance frameworks, and create AI policies, acceptable-use policies, and vendor procurement controls
  • Create an enterprise AI governance strategy, carry out maturity assessments, align governance with ESG objectives, and design a complete AI governance blueprint
Learning Approach

How will this Training Course be Presented?

The Certificate in AI Governance Course uses a structured learning methodology centred on governance, combining analysis of regulatory frameworks, workshops on risk and bias assessment, sessions addressing Shadow AI governance, policy creation exercises, and a comprehensive capstone project that progresses from core ethics and regulatory compliance to risk management, governance design, and the development of strategic maturity.

Real-world failures in AI governance, regulatory impact assessments, risk workshops, and a capstone exercise focused on producing an AI governance blueprint are embedded throughout, ensuring delegates link governance frameworks with the operational and strategic duties they will assume following certification.

Methods of delivery include:

  • Instructor-led teaching covering AI ethics principles, regulatory frameworks, risk categories, governance models, and maturity strategies
  • Case study evaluation of actual AI governance failures, including Amazon's recruiting AI, COMPAS, and other prominent incidents
  • Regulatory impact assessment workshops that apply GDPR, the AI Act, and regional regulatory requirements to organisational compliance scenarios
  • AI risk and bias assessment workshops using risk registers, bias detection, fairness assessment, and explainability frameworks
  • A capstone exercise to design a complete AI governance blueprint specific to an organisation, for submission as part of the certification assessment

The Course Content

Foundations of AI Governance & Responsible AI

  • Understanding AI governance: definitions, scope, and importance
  • Key drivers for AI governance in the public and private sectors
  • Overview of AI ethics principles: fairness, accountability, transparency, privacy
  • Types of AI systems and associated governance challenges
  • Case studies: governance failures (Amazon recruiting AI, COMPAS, etc.)
  • Introduction to global AI governance models and frameworks
  • Building the business case for responsible AI
  • Workshop: Mapping AI governance needs in your organisation

Regulatory Landscapes, Standards & Compliance Requirements

  • Overview of global regulations
  • AI classifications and compliance obligations
  • Data protection laws and AI (GDPR, regional regulations)
  • Governance requirements for high-risk AI systems
  • AI documentation, transparency, and reporting obligations
  • Building internal compliance frameworks
  • Workshop: Conducting a regulatory impact assessment

AI Risk Management, Bias, & Algorithmic Transparency

  • Understanding AI risks: technical, operational, ethical, and societal
  • Bias detection, fairness assessment, and mitigation strategies
  • Explainable AI (XAI) methods and tools
  • Governance for generative AI models and large language models
  • AI model lifecycle management and monitoring
  • Risk registers, AI control checkpoints, and audit trails
  • AI system testing and validation frameworks
  • Workshop: Conducting an AI risk assessment & bias analysis

Designing & Implementing AI Governance Frameworks (Including Shadow AI)

  • Governance structures: committees, roles, and oversight responsibilities
  • Accountability models for AI ownership and decision-making
  • Understanding AI Shadow: causes, organisational blind spots, and governance gaps
  • Why Shadow AI emerges despite existing IT and AI policies
  • Integrating Shadow AI oversight into governance structures
  • AI governance frameworks: NIST, ISO, and organisational models
  • Creating AI governance policies, acceptable-use policies, and standard operating procedures
  • Controlling employee use of public and generative AI tools
  • Procurement governance: evaluating and approving third-party AI vendors
  • Managing Shadow AI in SaaS platforms and embedded AI tools
  • Human-in-the-loop (HITL) and human-on-the-loop (HOTL) controls
  • Incident response and escalation procedures for Shadow AI misuse or failure
  • Building governance for generative AI & autonomous systems

Strategy, Maturity Models & Future Trends

  • Developing an enterprise AI governance strategy
  • AI maturity assessments and roadmap development
  • Aligning AI governance with organisational values and ESG goals
  • Integrating AI governance into digital transformation programs
  • Preparing for future trends: autonomous systems, AGI, and next-gen regulations
  • Capstone exercise: Designing a complete AI governance blueprint
  • Certificate examination / assessment
  • Closing session: Action plan for AI governance implementation
Recognition

Certificate

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.
Collaborations

In Partnership With

Anderson Copex Coventry

Frequently Asked Questions

The course covers AI governance frameworks, policies, ethics, and governance controls. It focuses on how these elements support responsible AI. It also addresses the governance of shadow AI.
The course examines policies as a core part of AI governance. It considers how policies work alongside frameworks, ethical principles, and governance controls. The emphasis is on supporting responsible AI and addressing shadow AI.
The course explores governance controls for responsible and shadow AI. These controls are considered alongside broader frameworks, policies, and ethics. The provided course information does not specify individual control types.
Yes, shadow AI is explicitly included in the course focus. The course addresses it through governance frameworks, policies, ethical considerations, and controls. This makes shadow AI part of the wider responsible AI governance discussion.
You will learn about frameworks used to structure AI governance. The course connects these frameworks with policies, ethics, and governance controls. Its focus is on applying a coordinated approach to responsible and shadow AI.
Yes, AI ethics is one of the course’s stated areas of focus. It is covered in relation to governance frameworks, policies, and controls rather than as a separate topic. This links ethical considerations to the governance of responsible and shadow AI.
The course supports responsible AI by focusing on the frameworks, policies, ethics, and governance controls used to govern it. These elements provide a structured basis for considering how AI should be managed. The course also places responsible AI in the context of shadow AI governance.
The course treats responsible AI and shadow AI as related governance concerns. Both are examined through the shared lenses of frameworks, policies, ethics, and governance controls. This provides a unified governance focus without treating either topic in isolation.

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