A tailored course, built for your situation
Cross-Functional AI Vendor Risk Assessment for Risk-Adverse Boards
Master board-ready risk assessment frameworks for AI vendor 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 evaluations are no longer just an IT checklist , they’re strategic, cross-functional decisions with board-level implications.
The situation this course is for
Traditional vendor risk assessments fail to address the complexity of AI systems. Legal teams lack technical insight, engineering teams miss compliance requirements, and security assessments overlook ethical drift. Without a unified framework, organizations face misalignment, delayed deployments, and governance gaps , especially under pressure from risk-adverse boards.
Who this is for
A business or technology professional responsible for vendor governance, risk, compliance, or AI procurement , working across legal, security, engineering, or strategy to ensure safe, auditable AI adoption.
Who this is not for
This course is not for individual contributors focused solely on technical AI development or for executives seeking high-level overviews without implementation detail.
What you walk away with
- Lead cross-functional AI vendor risk assessments with confidence
- Align legal, security, and engineering teams around a unified evaluation framework
- Produce board-ready risk dossiers that anticipate governance concerns
- Apply structured due diligence to AI-specific risks like model drift, data provenance, and third-party dependency
- Deploy a repeatable assessment process with measurable risk thresholds
The 12 modules (with all 144 chapters)
Module 1. The Evolving Landscape of AI Vendor Risk
Understand the forces driving new scrutiny of AI vendors and the shift toward board-level governance.
12 chapters in this module
- Rise of AI in enterprise procurement
- Board expectations for technology risk
- Third-party risk in the AI era
- From checklist to strategic assessment
- Regulatory tailwinds shaping due diligence
- Global variation in AI governance standards
- Case study: AI vendor failure post-mortem
- Lessons from past technology overruns
- Emergence of cross-functional teams
- Role clarity across legal, security, engineering
- Stakeholder mapping for AI assessments
- Building credibility with executive leadership
Module 2. Defining Risk in AI Vendor Contexts
Establish a shared language for risk across disciplines and define what 'risk-adverse' means in practice.
12 chapters in this module
- Technical vs. reputational risk
- Operational continuity risks
- Model transparency and explainability
- Data privacy and provenance
- Bias, fairness, and ethical drift
- Contractual liability gaps
- Intellectual property exposure
- Supply chain dependencies
- Incident response preparedness
- Long-term model maintenance
- Vendor lock-in and exit costs
- Scenario planning for model failure
Module 3. Cross-Functional Team Design
Structure effective assessment teams with clear roles, inputs, and decision rights.
12 chapters in this module
- Core functions involved in AI risk
- Legal team inputs and expectations
- Security team threat modeling
- Engineering team integration review
- Compliance team regulatory mapping
- Finance team cost-risk analysis
- HR and workforce impact
- Establishing escalation paths
- Decision rights and thresholds
- Conflict resolution frameworks
- Documentation standards
- Cross-functional communication protocols
Module 4. Board Communication Frameworks
Translate technical findings into governance-grade insights for executive audiences.
12 chapters in this module
- Understanding board priorities
- Risk appetite vs. risk tolerance
- Tailoring reports for non-technical leaders
- Visualizing risk exposure clearly
- Avoiding jargon without losing precision
- Scenario-based risk reporting
- Linking vendor risk to strategic goals
- Time horizons for risk disclosure
- Preparing for board questions
- Documenting assumptions and gaps
- Versioning risk assessments
- Audit readiness for vendor files
Module 5. Due Diligence Questionnaire Design
Build and refine AI-specific questionnaires that extract actionable insights from vendors.
12 chapters in this module
- Structure of a layered questionnaire
- Technical architecture questions
- Model training data provenance
- Third-party component disclosure
- Model monitoring and logging
- Incident response SLAs
- Right-to-audit clauses
- Data handling certifications
- Model update frequency
- Bias testing methodology
- Explainability capabilities
- Exit strategy and data portability
Module 6. Technical Risk Assessment
Evaluate AI systems for robustness, transparency, and operational resilience.
12 chapters in this module
- Model performance under stress
- Input validation and adversarial testing
- Model drift detection
- Version control and reproducibility
- Logging and observability
- API security and access control
- Model explainability techniques
- Testing for edge cases
- Scalability under load
- Fail-safe and fallback mechanisms
- Model decommissioning protocols
- Third-party dependency mapping
Module 7. Legal and Compliance Alignment
Map vendor practices to regulatory expectations and contractual obligations.
12 chapters in this module
- GDPR and AI data rights
- CCPA and consumer data use
- Sector-specific regulations (finance, health)
- AI liability frameworks
- Intellectual property ownership
- Model licensing terms
- Export control considerations
- Jurisdiction and dispute resolution
- Right-to-repair and audit access
- Subprocessor transparency
- Certifications (SOC 2, ISO, etc.)
- Compliance gap analysis
Module 8. Ethical and Social Impact Evaluation
Assess AI systems for fairness, inclusivity, and societal impact.
12 chapters in this module
- Bias in training data
- Demographic performance gaps
- Fairness metrics selection
- Community impact assessment
- Stakeholder consultation methods
- Transparency to end users
- Consent and notice design
- Right to contest automated decisions
- Human-in-the-loop requirements
- Cultural appropriateness
- Long-term societal effects
- Ethical review board engagement
Module 9. Operational Resilience and Continuity
Ensure AI systems remain reliable and supportable over time.
12 chapters in this module
- Uptime and availability SLAs
- Disaster recovery planning
- Vendor business continuity
- Model retraining schedules
- Dependency on key personnel
- Source code escrow options
- Support response timelines
- Change management process
- Incident escalation paths
- Third-party dependency risks
- Single points of failure
- Contingency planning
Module 10. Risk Scoring and Threshold Design
Build objective scoring systems to guide go/no-go decisions.
12 chapters in this module
- Defining risk dimensions
- Weighting criteria by impact
- Scoring consistency across teams
- Calibrating to risk appetite
- Red/Amber/Green thresholds
- Risk aggregation methods
- Scenario-based scoring
- Time-bound risk reassessment
- Vendor improvement tracking
- Benchmarking against peers
- Adjusting thresholds by use case
- Documenting scoring rationale
Module 11. Implementation Playbook Development
Turn frameworks into action with templates, workflows, and governance integration.
12 chapters in this module
- Vendor intake workflow
- Cross-functional review calendar
- Documentation repository setup
- Approval workflow design
- Integration with procurement
- Risk register maintenance
- Training for assessors
- Audit trail generation
- Dashboard for leadership
- Continuous improvement loop
- Lessons learned capture
- Scaling across business units
Module 12. Real-World Application and Case Studies
Apply the full framework to realistic scenarios and learn from peer experiences.
12 chapters in this module
- Healthcare AI vendor assessment
- Financial services model validation
- Retail personalization risk review
- HR tech fairness audit
- Manufacturing predictive maintenance
- Public sector transparency case
- Start-up vendor due diligence
- Incident response simulation
- Board presentation rehearsal
- Post-mortem analysis
- Lessons from failed deployments
- Scaling successful practices
How this maps to your situation
- AI vendor due diligence lagging behind adoption
- Cross-functional misalignment in risk evaluation
- Board requests for more structured AI governance
- Need for repeatable, auditable assessment processes
Before vs. after
Before
AI vendor assessments are fragmented, inconsistent, and fail to meet board expectations for rigor.
After
You lead unified, cross-functional evaluations that produce clear, board-ready risk assessments with actionable next steps.
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 busy professionals. Total investment: 50, 70 hours, self-paced.
If nothing changes
Without a structured approach, organizations risk delayed AI adoption, governance gaps, and loss of stakeholder trust , especially when facing scrutiny from risk-adverse boards.
How this compares to the alternatives
Unlike generic risk frameworks or high-level AI ethics courses, this program delivers implementation-grade tools tailored to cross-functional teams and board-level accountability , with no reliance on live sessions or video content.
Frequently asked
Who is this course designed for?
Business and technology professionals responsible for AI vendor governance, risk, compliance, or procurement across legal, security, engineering, or strategy functions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there live support or instructor access?
No. The course is self-paced, text-based, and includes downloadable templates and a hand-built implementation playbook for immediate application.
$199 one-time. Approximately 4-6 hours per module, designed for busy professionals. Total investment: 50, 70 hours, self-paced..
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