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Risk-Managed AI Vendor Risk Assessment for Audit Teams

$199.00
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A tailored course, built for your situation

Risk-Managed AI Vendor Risk Assessment for Audit Teams

Implementable frameworks for audit-ready AI governance

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Audit teams are being asked to assess AI vendors without clear standards or playbooks.

The situation this course is for

Traditional vendor risk frameworks don’t account for AI-specific risks like model drift, data provenance, or opaque decision logic. Audit teams are stepping up, but often lack structured methods to evaluate AI vendors consistently, confidently, and in alignment with evolving compliance expectations.

Who this is for

Senior audit, compliance, and governance professionals in technology, financial services, healthcare, or regulated industries who are responsible for third-party risk and AI oversight.

Who this is not for

This is not for software developers building AI models, nor for executives seeking high-level overviews. It’s for practitioners who need actionable methods to assess AI vendors systematically.

What you walk away with

  • Apply a standardized risk-scoring model to AI vendor proposals
  • Identify critical control gaps in AI vendor documentation and architecture
  • Design audit trails that capture model behavior and data lineage
  • Evaluate AI vendor contracts for enforceable risk provisions
  • Produce board-ready assessment summaries with clear risk ratings

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Define AI-specific risk factors and map them to audit responsibilities.
12 chapters in this module
  1. Defining AI vendor risk in audit contexts
  2. Distinguishing AI from traditional software vendors
  3. Regulatory drivers shaping AI oversight
  4. Core components of an AI system architecture
  5. Roles and responsibilities in AI procurement
  6. Audit scope boundaries for AI vendor assessments
  7. Key differences between cloud SaaS and AI-as-a-Service
  8. Understanding model lifecycle stages
  9. Vendor transparency expectations
  10. Baseline compliance frameworks applicable to AI
  11. Mapping AI risks to control domains
  12. Common pitfalls in early-stage AI audits
Module 2. Vendor Due Diligence Frameworks
Structure initial assessments to filter high-risk AI vendors efficiently.
12 chapters in this module
  1. Designing AI-specific RFP checklists
  2. Evaluating vendor documentation completeness
  3. Assessing model development methodology
  4. Reviewing data sourcing and labeling practices
  5. Vendor claims vs. verifiable evidence
  6. Third-party audit reports and SOC for AI
  7. Certifications relevant to AI vendors
  8. Evaluating model validation processes
  9. Assessing model update and retraining policies
  10. Incident response planning for AI systems
  11. Right-to-audit clauses in AI contracts
  12. Establishing vendor risk thresholds
Module 3. Model Risk and Performance Validation
Audit model behavior, accuracy, and operational integrity.
12 chapters in this module
  1. Understanding model performance metrics
  2. Detecting bias in training and inference
  3. Evaluating fairness across demographic groups
  4. Model drift detection mechanisms
  5. Monitoring for concept drift
  6. Verifying model stability over time
  7. Assessing confidence intervals and uncertainty
  8. Evaluating model explainability outputs
  9. Techniques for black-box model auditing
  10. Validating model inputs and feature pipelines
  11. Testing for adversarial robustness
  12. Reviewing model rollback and versioning
Module 4. Data Governance and Provenance
Trace data lineage and assess data quality risks in AI systems.
12 chapters in this module
  1. Mapping data flow from source to model
  2. Assessing data labeling quality
  3. Verifying data consent and licensing
  4. Evaluating data anonymization techniques
  5. Detecting data leakage risks
  6. Assessing data drift impact
  7. Data retention and deletion policies
  8. Cross-border data transfer compliance
  9. Vendor access to training data
  10. Data integrity verification methods
  11. Audit trails for data transformations
  12. Evaluating synthetic data usage
Module 5. Control Environment Assessment
Evaluate the strength and auditability of vendor controls.
12 chapters in this module
  1. Mapping AI risks to control objectives
  2. Evaluating access controls for model systems
  3. Authentication and authorization design
  4. Change management for AI models
  5. Model deployment approval workflows
  6. Segregation of duties in AI operations
  7. Logging and monitoring coverage
  8. Incident detection and response
  9. Backup and recovery for AI components
  10. Third-party dependency management
  11. Vendor business continuity planning
  12. Control testing methodologies for AI
Module 6. Compliance and Regulatory Alignment
Align AI vendor assessments with GDPR, CCPA, AI Act, and other frameworks.
12 chapters in this module
  1. Mapping AI use cases to data privacy laws
  2. AI Act compliance requirements
  3. NYDFS and other financial regulations
  4. Sector-specific AI guidelines
  5. Algorithmic accountability standards
  6. Human-in-the-loop requirements
  7. Right to explanation obligations
  8. AI registry and inventory needs
  9. Audit rights under regulatory frameworks
  10. Reporting obligations for AI incidents
  11. Vendor cooperation with regulators
  12. Preparing for AI-specific audits
Module 7. Third-Party Risk Integration
Embed AI vendor risk into enterprise-wide third-party risk programs.
12 chapters in this module
  1. Integrating AI risk into vendor risk tiers
  2. Risk scoring models for AI vendors
  3. Ongoing monitoring strategies
  4. Key risk indicators for AI systems
  5. Vendor performance scorecards
  6. Contractual risk transfer mechanisms
  7. Insurance requirements for AI vendors
  8. Subcontractor and supply chain risks
  9. Vendor offboarding and model decommissioning
  10. AI-specific exit clauses
  11. Knowledge transfer requirements
  12. Post-engagement audits
Module 8. Audit Trail Design and Evidence Collection
Build defensible, repeatable audit trails for AI vendor assessments.
12 chapters in this module
  1. Defining audit evidence requirements
  2. Documenting assessment methodology
  3. Version control for assessment artifacts
  4. Storing vendor responses and clarifications
  5. Creating risk-rating worksheets
  6. Maintaining independence and objectivity
  7. Cross-referencing evidence to controls
  8. Preparing workpapers for peer review
  9. Board-level reporting formats
  10. Archiving assessment records
  11. Legal hold considerations
  12. Audit trail automation tools
Module 9. Contractual Risk Mitigation
Identify and enforce key risk provisions in AI vendor contracts.
12 chapters in this module
  1. Defining acceptable use policies
  2. Model performance guarantees
  3. Service level agreements for AI systems
  4. Penalties for non-compliance
  5. Right to audit enforcement
  6. Data ownership and usage rights
  7. IP rights and model ownership
  8. Liability caps and indemnification
  9. Breach notification timelines
  10. Termination for cause clauses
  11. Dispute resolution mechanisms
  12. Renewal and exit terms
Module 10. Stakeholder Communication and Reporting
Translate technical findings into executive insights.
12 chapters in this module
  1. Tailoring messages to technical teams
  2. Summarizing risk for legal teams
  3. Communicating with compliance officers
  4. Presenting to audit committees
  5. Board-level risk dashboards
  6. Managing vendor communication
  7. Documenting follow-up actions
  8. Escalation protocols for critical risks
  9. Creating standard assessment summaries
  10. Vendor feedback loops
  11. Change communication plans
  12. Maintaining assessment confidentiality
Module 11. Implementation Playbook Development
Build a customized, reusable assessment process.
12 chapters in this module
  1. Assessing organizational readiness
  2. Identifying internal stakeholders
  3. Defining assessment ownership
  4. Creating standard operating procedures
  5. Developing training materials
  6. Piloting the assessment process
  7. Gathering feedback from early users
  8. Iterating on assessment design
  9. Scaling across business units
  10. Integrating with procurement
  11. Automating evidence collection
  12. Maintaining playbook version control
Module 12. Future-Proofing AI Governance
Anticipate emerging risks and adapt assessment frameworks.
12 chapters in this module
  1. Tracking emerging AI regulations
  2. Monitoring advances in AI capabilities
  3. Adapting to new model types
  4. Evaluating generative AI vendors
  5. Assessing multimodal AI systems
  6. Preparing for autonomous AI agents
  7. AI risk horizon scanning
  8. Building cross-functional AI governance teams
  9. Leveraging AI audit networks
  10. Sharing best practices across peers
  11. Continuous improvement of assessment methods
  12. Developing AI governance maturity models

How this maps to your situation

  • An audit team evaluating their first AI vendor
  • A compliance officer updating third-party risk frameworks
  • A governance lead preparing for AI Act readiness
  • A risk manager assessing AI model performance claims

Before vs. after

Before
Uncertain how to assess AI vendors beyond surface-level checklists, lacking structured methods to evaluate model risk, data provenance, and compliance alignment.
After
Equipped with a repeatable, audit-ready framework to assess AI vendors systematically, produce defensible risk ratings, and lead AI governance initiatives confidently.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 4-6 hours per module, designed for asynchronous, self-paced learning with practical exercises.

If nothing changes
Without a structured approach, audit teams risk inconsistent assessments, overlooked risks, and diminished influence in AI governance decisions , reducing their ability to provide assurance as AI adoption accelerates.

How this compares to the alternatives

Unlike generic AI ethics guides or high-level compliance overviews, this course delivers implementation-grade tools, templates, and decision frameworks tailored specifically for audit teams conducting AI vendor risk assessments.

Frequently asked

Who is this course designed for?
Senior audit, compliance, and governance professionals responsible for assessing AI vendors and managing third-party risk in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 4-6 hours per module, designed for asynchronous, self-paced learning with practical exercises..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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