4D Training & Consultancy

Governance, Risk & Compliance

Third-Party AI and Algorithm Risk Management

This in-depth course develops directly applicable capability in Third-Party AI and Algorithm Risk Management. It connects Third-Party AI Inventory and Tiering, Due Diligence and Evidence, and Contract and Control Requirements to the decisions, controls, and activities participants need to perform in their workplace.

Duration confirmed during proposalIn-house, online, or customized deliveryCorporate teams and professional groups

Overview

Practical learning for workplace transfer.

This in-depth course develops directly applicable capability in Third-Party AI and Algorithm Risk Management. It connects Third-Party AI Inventory and Tiering, Due Diligence and Evidence, and Contract and Control Requirements to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward Vendor AI Risk Review, using evidence, scenarios, and work products appropriate to the subject.

Objectives

  • Analyze third-party ai inventory and tiering, including embedded, standalone, api, model, agent, and subcontracted ai.
  • Configure or structure due diligence and evidence, including intended use, training data, evaluation, security, privacy, resilience, and governance.
  • Evaluate contract and control requirements, including use restrictions, data rights, model change, audit, incident, and exit terms.
  • Manage monitoring and exit, including track versions, performance, drift, incidents, complaints, and provider changes.
  • Apply vendor ai risk review, including tier a proposed ai service.

Target audience

  • Professionals responsible for this subject area
  • Managers, supervisors, and team leaders
  • Analysts, specialists, engineers, or coordinators working with the relevant processes
  • Project, implementation, assurance, or improvement team members
  • Professionals preparing for broader responsibilities in this field

Program outline

A clear structure for the learning journey.

Program outline

Outline points are grouped in one designed block instead of being treated as separate module cards.

Module 1: Third-Party AI Inventory and Tiering

Embedded, standalone, API, model, agent, and subcontracted AI

Classify by data, autonomy, criticality, and affected parties

Identify fourth-party and model-provider dependencies

Module 2: Due Diligence and Evidence

Intended use, training data, evaluation, security, privacy, resilience, and governance

Test reports, model cards, certifications, incidents, and limitations

Validate claims rather than relying on questionnaires alone

Module 3: Contract and Control Requirements

Use restrictions, data rights, model change, audit, incident, and exit terms

Performance, bias, explainability, human oversight, and record access

Subprocessor and cross-border controls

Module 4: Monitoring and Exit

Track versions, performance, drift, incidents, complaints, and provider changes

Reassessment triggers and corrective action

Data return, model replacement, continuity, and lock-in reduction

Module 5: Vendor AI Risk Review

Tier a proposed AI service

Evaluate evidence and contract gaps

Set approval conditions and monitoring requirements

Materials provided

  • ○ Course-specific presentation slides
  • ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
  • ○ Course-specific worksheets, checklists, or calculation templates
  • ○ Applied workplace case materials
  • ○ 4D Certificate of Completion issued by 4D Training & Consultancy
  • ○ Post-course support for implementation questions

Training Options

Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.

Why choose 4D

4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.

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