3-day Virtual Seminar

AI Third-Party Risk Management

Overview

This session approaches AI from the buyer and user side of the relationship: what should an organisation consider when an external provider supplies the AI capability, data, model, platform or service?

Attendees will be taken through the lifecycle of third-party AI risk, beginning with identifying the proposed use and understanding the information involved, then moving through vendor assessment, contractual and governance considerations, ongoing monitoring and response planning. The webinar also considers the human side of third-party risk - including shadow AI, employee use of unapproved tools and the danger of assuming that a recognised vendor automatically makes every use case appropriate.

The focus is on practical decision-making and risk awareness. Examples and assessment questions are explained within the presentation, without requiring live access to external AI or vendor platforms.

Why you should Attend

An AI tool can be approved in minutes and embedded into a workflow just as quickly, while the risks created by that decision may be much harder to unwind. Organisations that treat an AI provider like an ordinary software purchase may overlook data exposure, unclear accountability, hidden dependencies, changing model behaviour or contractual gaps.

This webinar helps professionals ask better questions before trusting a third-party AI solution. It provides a clear way to think about due diligence, risk ownership, monitoring and human oversight so that innovation does not move faster than the organisation’s ability to control it.

Areas Covered in the Session

• Understand how third-party AI risk differs from traditional vendor risk and why the intended use of the tool matters
  
• Identify common risk areas including privacy, information security, intellectual property, inaccurate outputs, data use and external dependencies
  
• Explore the questions organisations should consider when assessing an AI vendor, product or embedded AI capability
  
• Consider how data sensitivity and business impact can help determine the level of due diligence and oversight required
  
• Examine governance and contractual considerations, including responsibilities, documentation, change management and incident expectations
  
• Recognise shadow AI and unapproved third-party tools as part of the organisation’s wider vendor and AI risk landscape
  
• Understand why third-party AI risk continues after procurement through monitoring, reassessment and changes to services or use cases
  
• Consider contingency planning for provider failures, incidents or AI services that move outside the organisation’s risk tolerance
  
• Build a proportionate approach that balances AI innovation with accountability, human oversight and compliance requirements
  

Who Will Benefit

• Compliance and Ethics Professionals
  
• Enterprise Risk and Operational Risk Professionals
  
• Third-Party and Vendor Risk Managers
  
• Procurement and Vendor Management Teams
  
• Information Security and Data Privacy Professionals
  
• Legal Operations and Governance Professionals
  
• IT, Digital Transformation and AI Governance Leaders
  
• Business Managers responsible for selecting or approving AI tools