A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Risk-Adverse Boards
A practical, board-ready framework for assessing AI vendor risk with confidence and clarity
$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.
Translating complex AI vendor risks into clear, board-appropriate insights remains a persistent challenge, even for seasoned professionals.
The situation this course is for
AI adoption is accelerating, but board members demand clarity, not technical jargon. Risk assessments often fail to align with governance expectations, leading to delayed decisions, escalated concerns, or rejected proposals. Without a standardized, credible methodology, professionals struggle to present findings that balance innovation with prudence.
Who this is for
Business and technology professionals responsible for AI governance, vendor due diligence, compliance, risk management, or technology strategy who engage with executive or board-level stakeholders.
Who this is not for
This course is not for engineers seeking hands-on coding labs or entry-level learners new to risk concepts. It assumes foundational knowledge of AI systems and risk frameworks.
What you walk away with
- Apply a structured, repeatable framework to assess AI vendor risk across 12 critical domains
- Translate technical findings into clear, board-appropriate narratives
- Leverage proven templates for scoping, scoring, and reporting vendor risk
- Anticipate board-level concerns and prepare evidence-based responses
- Build credibility as a strategic advisor in AI procurement and governance
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Establish core principles, terminology, and the evolving expectations of board-level governance.
12 chapters in this module
- Defining AI vendor risk in modern procurement
- The shift from IT risk to strategic governance
- Board expectations vs. technical reality
- Key regulatory drivers shaping vendor assessments
- Common failure points in AI vendor due diligence
- Risk appetite alignment across stakeholders
- The role of ethics in vendor evaluation
- Mapping vendor risk to business impact
- Emerging standards in AI governance
- Stakeholder communication models
- Creating a risk taxonomy for AI vendors
- Baseline assessment checklist
Module 2. Legal and Contractual Risk Domains
Assess liability, intellectual property, data rights, and enforceability in AI vendor agreements.
12 chapters in this module
- IP ownership in AI-generated outputs
- Liability for algorithmic harm
- Data licensing and reuse rights
- Jurisdiction and dispute resolution
- Subprocessor transparency requirements
- Warranties and indemnification clauses
- Exit rights and data portability
- Audit rights and access limitations
- Regulatory compliance obligations
- Insurance requirements for AI vendors
- Force majeure and AI performance failure
- Contractual risk scoring model
Module 3. Data Privacy and Protection
Evaluate how vendors handle sensitive data across jurisdictions and use cases.
12 chapters in this module
- Data classification and vendor handling policies
- Cross-border data transfer mechanisms
- Purpose limitation in AI training
- Consent and lawful basis verification
- Anonymization and re-identification risk
- Data retention and deletion obligations
- Third-party data sourcing transparency
- Data subject rights fulfillment
- Privacy-by-design in vendor platforms
- Breach notification timelines
- Data protection impact assessments
- Privacy risk scoring template
Module 4. Model Transparency and Explainability
Assess the interpretability of AI models and the vendor’s ability to justify decisions.
12 chapters in this module
- Levels of model explainability
- Documentation requirements for AI systems
- Feature importance and decision tracing
- Human-in-the-loop design patterns
- Bias detection and mitigation reporting
- Model validation methodologies
- Ground truth data provenance
- Performance metrics beyond accuracy
- Stakeholder communication of model behavior
- Explainability for non-technical audiences
- Vendor transparency scorecard
- Scenario-based explainability drills
Module 5. Security and Infrastructure Resilience
Evaluate the robustness of vendor systems against threats and outages.
12 chapters in this module
- AI-specific attack vectors
- Model poisoning and evasion defenses
- Secure development lifecycle
- Infrastructure redundancy and failover
- Penetration testing and red teaming
- API security for AI services
- Access controls and role-based permissions
- Incident response planning
- Threat intelligence integration
- Security certification validation
- Zero trust architecture alignment
- Security resilience checklist
Module 6. Ethical and Societal Impact
Assess fairness, accountability, and broader societal implications of AI deployments.
12 chapters in this module
- Defining ethical AI in vendor contexts
- Fairness metrics across demographic groups
- Stakeholder impact assessments
- Community engagement practices
- AI use case red lines
- Whistleblower and escalation channels
- Environmental impact of AI models
- Labor displacement considerations
- Ethical review board requirements
- Public trust and reputational risk
- Ethical risk rating framework
- Case studies in ethical failure
Module 7. Operational and Performance Risk
Measure reliability, scalability, and real-world performance of AI systems.
12 chapters in this module
- Service level objectives for AI systems
- Latency and throughput expectations
- Drift detection and model decay
- Monitoring and alerting infrastructure
- Fail-safe and fallback mechanisms
- User feedback integration
- Scalability under load
- Versioning and rollback capability
- Performance benchmarking
- Incident root cause analysis
- Operational risk dashboard
- Vendor uptime validation
Module 8. Vendor Viability and Business Continuity
Assess the long-term stability and resilience of the vendor organization.
12 chapters in this module
- Financial health indicators
- Customer concentration risk
- Leadership team stability
- Funding runway and burn rate
- Business continuity planning
- Disaster recovery capabilities
- Key person dependencies
- Mergers and acquisition exposure
- Insurance and liability coverage
- Vendor lock-in mitigation
- Exit strategy feasibility
- Vendor viability score
Module 9. Integration and Interoperability
Evaluate how AI systems connect with existing infrastructure and workflows.
12 chapters in this module
- API design and documentation quality
- Data format compatibility
- Authentication and identity management
- Event-driven integration patterns
- Legacy system compatibility
- Customization vs. configuration
- Change management processes
- Upgrade and patching frequency
- Interoperability testing protocols
- Integration effort estimation
- Dependency mapping
- Integration risk matrix
Module 10. Regulatory and Compliance Alignment
Ensure vendor practices align with current and emerging regulatory expectations.
12 chapters in this module
- GDPR and AI-specific provisions
- U.S. state privacy law alignment
- Sector-specific regulations (health, finance, etc.)
- Algorithmic accountability laws
- Export controls on AI models
- Accessibility requirements
- Recordkeeping and audit trails
- Regulatory change monitoring
- Compliance certification validity
- Self-regulation vs. mandated standards
- Compliance gap analysis
- Regulatory risk heatmap
Module 11. Board Communication and Reporting
Shape assessments into concise, actionable insights for executive and board audiences.
12 chapters in this module
- Translating technical risk to business terms
- Risk appetite alignment in reporting
- Visualizing risk exposure
- Scenario planning for board discussion
- Pre-empting board questions
- Balancing innovation and caution
- Executive summary frameworks
- Risk escalation protocols
- Board-level risk dashboard design
- Narrative structuring for impact
- Communication rehearsal drills
- Board feedback integration
Module 12. Implementation and Continuous Improvement
Deploy the framework across your organization and refine it over time.
12 chapters in this module
- Pilot program design
- Stakeholder onboarding plan
- Training materials for assessors
- Tooling and automation options
- Feedback loop integration
- Version control for assessment templates
- Benchmarking against peers
- Lessons learned documentation
- Quarterly review cadence
- Scaling across business units
- Continuous improvement playbook
- Final implementation checklist
How this maps to your situation
- Preparing for first AI vendor assessment
- Responding to board-level risk inquiries
- Standardizing assessment processes across teams
- Improving credibility in cross-functional leadership
Before vs. after
Before
Uncertainty in how to structure AI vendor risk assessments that satisfy both technical and governance requirements.
After
Confidence in delivering structured, repeatable, and board-ready evaluations that balance innovation with prudence.
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 around professional commitments.
If nothing changes
Without a formalized approach, organizations risk delayed AI adoption, increased scrutiny from boards, or unintended exposure due to inconsistent assessments.
How this compares to the alternatives
Unlike generic risk courses, this program focuses exclusively on AI vendor risk with board-level communication strategies. It goes beyond theory with implementation-grade tools, templates, and a tailored playbook not found in academic or certification programs.
Frequently asked
Who is this course designed for?
It's for business and technology professionals involved in AI governance, vendor due diligence, compliance, or risk management who engage with executive or board-level stakeholders.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is awarded after finishing all modules and passing the final assessment.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning around professional commitments..
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