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Modern AI Vendor Risk Assessment for Risk-Adverse Boards

$199.00
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A tailored course, built for your situation

Modern AI Vendor Risk Assessment for Risk-Adverse Boards

Implementation-grade mastery for technology and business leaders guiding 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.
AI vendor decisions are moving fast, but risk committees need confidence, clarity, and control.

The situation this course is for

Organizations are adopting AI-powered solutions from third parties at speed, yet governance teams lack standardized, scalable methods to assess model risk, data provenance, and long-term liability. This creates friction between innovation and oversight, especially when boards demand accountability without slowing progress.

Who this is for

Business and technology professionals in risk, compliance, governance, IT, security, or product leadership roles who influence or own AI vendor due diligence.

Who this is not for

This course is not for software developers focused on building AI models, nor for executives seeking only high-level overviews without implementation detail.

What you walk away with

  • Apply a structured framework to evaluate AI vendor risk across technical, legal, and operational domains
  • Translate complex model behaviors into clear, board-ready risk narratives
  • Leverage standardized templates to accelerate due diligence cycles
  • Align AI procurement with existing compliance regimes (e.g., data privacy, financial controls, sector regulations)
  • Build defensible audit trails for vendor selection and monitoring

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Establish core concepts and governance models for third-party AI.
12 chapters in this module
  1. Defining AI vendor risk in enterprise contexts
  2. Key differences between traditional and AI-driven vendor risk
  3. Roles and responsibilities across teams
  4. Regulatory drivers shaping vendor oversight
  5. Mapping AI use cases to risk profiles
  6. Board expectations for AI procurement
  7. Common failure modes in vendor selection
  8. Case study: Overestimating model readiness
  9. Case study: Underestimating integration complexity
  10. Principles of risk proportionality
  11. Vendor lifecycle stages and risk touchpoints
  12. Building a cross-functional intake process
Module 2. Technical Due Diligence Frameworks
Evaluate AI vendor technical claims with precision.
12 chapters in this module
  1. Assessing model architecture documentation
  2. Understanding training data provenance and bias safeguards
  3. Evaluating model performance metrics beyond accuracy
  4. Interpreting fairness, explainability, and drift detection claims
  5. Reviewing API security and access controls
  6. Validating model versioning and rollback capabilities
  7. Auditing vendor development lifecycle practices
  8. Assessing computational resource commitments
  9. Reviewing third-party dependencies and open-source components
  10. Evaluating model monitoring and alerting
  11. Understanding model retirement and data deletion
  12. Creating a technical scorecard for vendor comparison
Module 3. Compliance and Regulatory Alignment
Map AI vendor practices to compliance obligations.
12 chapters in this module
  1. Aligning with GDPR and data privacy laws
  2. Mapping to financial services regulations (e.g., SR 11-7, IOSCO)
  3. Adapting to sector-specific AI guidance
  4. Vendor obligations under data processing agreements
  5. Handling cross-border data flows
  6. Documenting compliance for audit readiness
  7. Managing consent and data subject rights
  8. Assessing vendor adherence to AI ethics frameworks
  9. Integrating with internal policy requirements
  10. Preparing for regulatory examinations
  11. Responding to compliance findings
  12. Maintaining up-to-date compliance mappings
Module 4. Operational Risk and Resilience
Assess vendor reliability and business continuity.
12 chapters in this module
  1. Evaluating uptime SLAs and reporting transparency
  2. Reviewing disaster recovery and failover plans
  3. Assessing vendor financial health and stability
  4. Monitoring for service degradation
  5. Evaluating support response times and escalation paths
  6. Testing incident response coordination
  7. Reviewing change management practices
  8. Assessing documentation completeness
  9. Validating integration support commitments
  10. Planning for vendor lock-in and exit strategies
  11. Assessing workforce continuity and expertise depth
  12. Benchmarking operational maturity
Module 5. Model Transparency and Explainability
Ensure AI decisions can be understood and defended.
12 chapters in this module
  1. Defining explainability requirements by use case
  2. Assessing SHAP, LIME, and other explanation methods
  3. Evaluating model documentation quality
  4. Validating feature importance claims
  5. Testing for edge case behavior
  6. Assessing model confidence and uncertainty reporting
  7. Reviewing model decision logs
  8. Evaluating human-in-the-loop capabilities
  9. Ensuring consistency across model versions
  10. Creating model cards for internal stakeholders
  11. Building decision traceability frameworks
  12. Documenting model limitations and assumptions
Module 6. Data Governance and Provenance
Trace data lineage and ensure integrity in AI systems.
12 chapters in this module
  1. Mapping data sources and collection methods
  2. Assessing data labeling quality and oversight
  3. Validating data licensing and reuse rights
  4. Evaluating data preprocessing pipelines
  5. Ensuring data versioning and reproducibility
  6. Assessing data drift detection mechanisms
  7. Reviewing synthetic data usage
  8. Evaluating data retention and deletion policies
  9. Mapping data flows across systems
  10. Assessing data quality monitoring
  11. Documenting data governance controls
  12. Creating data lineage diagrams
Module 7. Legal and Contractual Risk
Structure agreements that protect organizational interests.
12 chapters in this module
  1. Defining liability for AI-driven errors
  2. Negotiating indemnification clauses
  3. Setting performance guarantees and benchmarks
  4. Ensuring audit rights and access
  5. Managing intellectual property rights
  6. Addressing model ownership and reuse
  7. Including model change notification requirements
  8. Establishing data ownership clarity
  9. Enforcing compliance with contractual terms
  10. Planning for contract termination and data exit
  11. Reviewing insurance and cyber liability coverage
  12. Documenting dispute resolution mechanisms
Module 8. Ethical and Reputational Risk
Anticipate and mitigate societal and brand impacts.
12 chapters in this module
  1. Assessing potential for bias and discrimination
  2. Evaluating fairness across demographic groups
  3. Reviewing model impact on vulnerable populations
  4. Assessing environmental and energy costs
  5. Monitoring for misuse potential
  6. Evaluating vendor diversity and inclusion practices
  7. Assessing public perception risks
  8. Reviewing community engagement and redress mechanisms
  9. Ensuring alignment with organizational values
  10. Creating ethical escalation pathways
  11. Documenting ethical review decisions
  12. Building reputation risk dashboards
Module 9. Board Communication and Reporting
Translate technical risk into strategic insights.
12 chapters in this module
  1. Defining board-level risk thresholds
  2. Creating concise risk summaries
  3. Visualizing risk exposure trends
  4. Balancing innovation and caution
  5. Explaining model uncertainty to non-technical leaders
  6. Highlighting key control points
  7. Reporting on compliance status
  8. Communicating incident response readiness
  9. Presenting vendor comparison outcomes
  10. Updating on model performance over time
  11. Documenting oversight decisions
  12. Building board-level risk dashboards
Module 10. Vendor Integration and Change Management
Ensure smooth, secure onboarding and evolution.
12 chapters in this module
  1. Assessing integration complexity
  2. Evaluating API documentation and support
  3. Planning for data migration and validation
  4. Testing in staging environments
  5. Managing user training and adoption
  6. Establishing monitoring baselines
  7. Coordinating with internal IT teams
  8. Handling model updates and versioning
  9. Managing configuration drift
  10. Ensuring secure credential management
  11. Reviewing access logging and auditing
  12. Planning for decommissioning
Module 11. Continuous Monitoring and Auditing
Maintain oversight throughout the vendor lifecycle.
12 chapters in this module
  1. Setting up model performance dashboards
  2. Monitoring for concept and data drift
  3. Reviewing audit logs and access patterns
  4. Scheduling periodic risk reassessments
  5. Tracking compliance changes
  6. Evaluating vendor updates and patches
  7. Assessing third-party audit reports
  8. Conducting internal control testing
  9. Updating risk profiles dynamically
  10. Reporting on control effectiveness
  11. Planning for external audits
  12. Maintaining documentation for regulators
Module 12. Implementation and Scaling
Deploy the framework across your organization.
12 chapters in this module
  1. Adapting the framework to different use cases
  2. Scaling across multiple vendors
  3. Integrating with procurement workflows
  4. Training risk teams on assessment methods
  5. Building internal review boards
  6. Creating standardized templates
  7. Automating data collection
  8. Integrating with GRC platforms
  9. Measuring program maturity
  10. Sharing best practices across units
  11. Optimizing for speed and rigor
  12. Planning for future AI governance evolution

How this maps to your situation

  • Evaluating a new AI vendor for a high-impact business function
  • Responding to board questions about existing AI vendor risk
  • Designing a repeatable due diligence process for AI procurement
  • Preparing for regulatory scrutiny of AI vendor practices

Before vs. after

Before
Uncertain how to systematically assess AI vendors while balancing innovation and oversight.
After
Equipped with a repeatable, board-justifiable framework to evaluate, monitor, and report on AI vendor risk.

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 3-4 hours per module, designed for flexible, self-paced learning.

If nothing changes
Without a structured approach, organizations may face delayed AI adoption, regulatory findings, or reputational damage from poorly governed vendor relationships.

How this compares to the alternatives

Unlike generic risk management courses, this program delivers AI-specific, implementation-grade frameworks tailored to board-level concerns. It avoids high-level theory and focuses on actionable templates, real-world scenarios, and compliance-ready documentation.

Frequently asked

Who is this course designed for?
Business and technology professionals in risk, compliance, governance, IT, security, or product leadership roles who influence or own AI vendor due diligence.
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 3-4 hours per module, designed for flexible, self-paced learning..

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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