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Understanding and Managing AI Risk & Shadow AI in Organizations

Identifying, Assessing, and Controlling AI Risks for Responsible and Secure AI Adoption

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Why Choose Us

The Understanding and Managing AI Risk & Shadow AI in Organizations Course gives risk, governance, compliance, and technology professionals a comprehensive, structured framework for identifying, assessing, managing, and governing AI risk with particular focus on the growing challenge of Shadow AI and the organisational, ethical, legal, and reputational exposures it creates.

AI risk is no longer a future consideration it is a present operational reality. From biased decision-making and data privacy breaches to unmanaged generative AI tools used by employees without oversight, the risks created by AI in organisations are expanding faster than most governance frameworks are equipped to handle. Shadow AI — the informal, unapproved use of AI tools across business units is one of the most significant and least understood risk vectors organisations face today.

This course addresses that challenge directly covering the full spectrum of AI risk categories, Shadow AI identification and assessment methodologies, acceptable-use policies, human-in-the-loop controls, third-party vendor risk, incident response, governance frameworks, and a capstone action planning exercise. Every module is grounded in real-world risk scenarios and practical governance application.

The Understanding and Managing AI Risk & Shadow AI in Organizations Course is built for professionals who are accountable for managing AI risk responsibly — and who want the knowledge, tools, and governance frameworks to do it effectively before incidents occur.

 

Why Choose Understanding and Managing AI Risk & Shadow AI in Organizations
Course Goals

What are the Goals?

The Understanding and Managing AI Risk & Shadow AI in Organizations Course is designed to develop comprehensive AI risk management and Shadow AI governance capability — from risk foundations and category assessment through identification, control design, and enterprise risk integration.

By the end of this course, participants will be able to:

  • Explain the definitions, scope, and drivers of AI risk and distinguish between IT risk, digital risk, and AI risk
  • Explain Shadow AI — its causes, examples, differences from Shadow IT, and why it emerges in organisations
  • Evaluate the full range of AI risk categories including strategic, operational, data privacy, cybersecurity, ethical, legal, and reputational risks
  • Assess how Shadow AI amplifies organisational AI risk exposure and apply lessons from real AI risk incidents
  • Map AI use across business units, identify informal and unapproved AI usage, and classify AI risk using structured assessment methodologies
  • Conduct AI and Shadow AI risk assessments including impact and likelihood analysis, risk registers, and generative AI tool evaluation
  • Develop AI acceptable-use policies, data governance controls, HITL and HOTL frameworks, monitoring protocols, and vendor AI risk management approaches
  • Develop an AI risk and Shadow AI management roadmap and a complete action plan for implementation within their own organisation

Who is this Training Course for?

The Understanding and Managing AI Risk & Shadow AI in Organizations Course is designed for risk, governance, compliance, technology, and HR professionals who are responsible for identifying, assessing, and managing AI-related risks including the increasingly significant challenge of Shadow AI across their organisations.

This course is suitable for:

  • Risk managers and enterprise risk professionals integrating AI risk into ERM frameworks and governance structures
  • Compliance and legal professionals managing regulatory, data privacy, and intellectual property risks from AI usage
  • IT and cybersecurity professionals managing AI-related security, access control, and data governance risks
  • AI and digital governance professionals developing acceptable-use policies, oversight frameworks, and accountability structures
  • HR and organisational development leaders managing employee AI usage, cultural AI risk, and workforce conduct guidelines
  • Internal auditors assessing AI risk exposure and Shadow AI governance maturity across business units
  • Senior leaders and executives seeking to understand their accountability for AI risk and Shadow AI governance
  • Graduate risk, compliance, and technology professionals building a structured foundation in AI risk management
Learning Approach

How will this Training Course be Presented?

The Understanding and Managing AI Risk & Shadow AI in Organizations Course is delivered through a structured, practically intensive learning approach that moves from AI risk foundations and Shadow AI concepts through risk category analysis, assessment methodology, control design, and governance framework development culminating in a capstone exercise where delegates create a complete AI risk and Shadow AI management action plan.

Case studies of real AI risk incidents, risk assessment workshops, policy design exercises, and governance mapping sessions are integrated throughout every day — ensuring delegates connect frameworks to the genuine risk management challenges they face in their own organisational contexts.

Delivery methods include:

  • Instructor-led sessions covering AI risk definitions, Shadow AI concepts, risk category frameworks, and governance principles
  • AI risk category analysis sessions examining strategic, operational, data privacy, cybersecurity, ethical, legal, and reputational risk types
  • Shadow AI identification and mapping workshops applying indicators, red flags, and risk classification to real AI usage scenarios
  • AI risk assessment workshops conducting impact and likelihood analysis, building risk registers, and evaluating generative AI tool risks
  • Capstone action planning exercise developing a complete, organisation-specific AI risk and Shadow AI management action plan

The Course Content

Foundations of AI Risk and Shadow AI

  • Overview of Artificial Intelligence in modern organizations
  • How AI is used across operations, services, and decision-making
  • Understanding AI risk: definitions, scope, and drivers
  • Differences between IT risk, digital risk, and AI risk
  • Introduction to Shadow AI: concepts, causes, and examples
  • Shadow AI versus Shadow IT
  • Why Shadow AI emerges in organizations
  • Introduction to AI governance and accountability
  • Discussion: Identifying AI and Shadow AI use within participants’ organizations

AI Risk Categories and Organizational Impact

  • Strategic and decision-making risks
  • Operational and performance risks
  • Data privacy and confidentiality risks
  • Cybersecurity and intellectual property risks
  • Ethical, bias, and fairness risks
  • Legal and regulatory compliance risks
  • Reputational and trust-related risks
  • How Shadow AI amplifies AI risk exposure
  • Case Study: Lessons learned from AI risk incidents

Identifying and Assessing AI & Shadow AI Risks

  • Mapping AI use across business units
  • Identifying informal and unapproved AI usage
  • Indicators and red flags of Shadow AI
  • Risk classification of AI use cases
  • AI risk assessment methodologies
  • Impact and likelihood analysis
  • Risk registers and documentation requirements
  • Assessing risk in generative AI tools
  • Workshop: Conducting an AI and Shadow AI risk assessment

Managing and Controlling AI Risk

  • Principles of risk-based AI governance
  • AI acceptable-use policies and employee guidelines
  • Managing employee use of generative AI
  • Data governance and access controls
  • Human-in-the-loop (HITL) and human-on-the-loop (HOTL) controls
  • Monitoring, logging, and auditability
  • Managing third-party and vendor AI risks
  • Incident response and escalation for AI misuse
  • Workshop: Designing AI risk controls and mitigation actions

Governing Shadow AI and Embedding AI Risk Management

  • Shadow AI as a governance and cultural challenge
  • Bringing Shadow AI into controlled environments
  • Approved AI tools, platforms, and innovation sandboxes
  • Roles, responsibilities, and accountability for AI risk
  • Integrating AI risk into enterprise risk management (ERM)
  • Aligning AI risk management with ESG and organizational values
  • Measuring AI and Shadow AI risk maturity
  • Developing an AI risk and Shadow AI roadmap
  • Capstone Exercise: Creating an AI risk and Shadow AI management action plan
  • Course review and implementation next steps
Recognition

Certificate

  • • AZTech Certificate of Completion for delegates who attend and complete the training course.
Collaborations

In Partnership With

Anderson Copex Coventry

Frequently Asked Questions

Shadow AI refers to the informal, unapproved use of AI tools by employees and business units without organisational oversight or governance including the use of consumer generative AI platforms, browser-based AI tools, and AI-enabled applications that bypass IT approval and data governance controls. Shadow AI is addressed throughout this course because it represents one of the fastest-growing and most underestimated AI risk vectors organisations face — creating data privacy, intellectual property, compliance, and reputational exposures that many organisations are not yet equipped to manage.  

Day 3 covers AI and Shadow AI identification in full including how to map AI usage across business units, identify indicators and red flags of informal and unapproved AI usage, and classify AI use cases by risk profile. Delegates complete a hands-on risk assessment workshop that applies these methodologies to realistic organisational scenarios — leaving with a practical identification and mapping process they can deploy immediately within their own organisations.  

Managing employee generative AI usage is addressed directly within Day 4 covering how to develop acceptable-use guidelines for generative AI tools, how to balance the productivity benefits of generative AI with data confidentiality and intellectual property risks, and how to build the monitoring and logging frameworks needed to maintain visibility of how generative AI is being used across the workforce. Delegates leave with a practical governance approach that manages generative AI risk without creating cultural resistance to AI adoption.  

Day 2 covers the full spectrum of AI risk categories — including strategic and decision-making risks, operational and performance risks, data privacy and confidentiality risks, cybersecurity and intellectual property risks, ethical, bias, and fairness risks, legal and regulatory compliance risks, and reputational and trust-related risks. Delegates develop the ability to evaluate each risk category in the context of their own organisation's AI usage — and to understand how Shadow AI amplifies exposure across every one of these dimensions.  

Day 4 covers AI risk control design in full including acceptable-use policy development, data governance and access controls, human-in-the-loop and human-on-the-loop oversight frameworks, monitoring and auditability requirements, and vendor and third-party AI risk management. Delegates complete a control design workshop — leaving with a practical, structured approach to building the policies and controls that reduce AI risk exposure without preventing the productive use of AI across the organisation.  

Incident response and escalation for AI misuse are addressed within Day 4 — covering how to recognise when AI misuse has occurred or is at risk of occurring, what the immediate response priorities are, how to escalate within governance structures, and how to investigate and document AI incidents in a way that supports regulatory reporting and organisational learning. Delegates develop both the procedural knowledge and the leadership readiness to manage AI misuse incidents effectively when they arise.  

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