A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Senior Leaders
A 12-module implementation-grade program for leading AI governance with confidence
$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 outpacing governance frameworks, creating execution risk even in mature organizations.
The situation this course is for
Senior leaders are expected to approve and oversee AI integrations without standardized assessment tools. The lack of structured vendor evaluation leads to misaligned expectations, compliance exposure, and integration delays. Teams default to technical checklists that miss strategic risk levers.
Who this is for
Business and technology executives responsible for AI adoption, vendor oversight, compliance, or enterprise risk management
Who this is not for
Individual contributors without decision authority, technical implementers focused only on integration, or teams seeking coding-level AI guidance
What you walk away with
- Apply a structured framework to assess AI vendor risk across legal, operational, and technical domains
- Lead vendor negotiations with clarity on data rights, model transparency, and exit clauses
- Align AI procurement with existing governance, compliance, and risk management standards
- Anticipate and mitigate third-party model drift, bias, and performance degradation
- Deploy a repeatable assessment workflow that scales across business units
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Establish the core principles of AI-specific vendor risk in enterprise contexts.
12 chapters in this module
- Defining AI vendor risk in modern organizations
- Key differences between traditional and AI vendor assessment
- The role of senior leadership in governance
- Mapping AI risk to business outcomes
- Regulatory landscape overview
- Emerging standards and frameworks
- Stakeholder alignment across legal, IT, and business units
- Vendor lifecycle stages and risk touchpoints
- Common failure patterns in AI procurement
- Building a cross-functional assessment team
- Risk tolerance thresholds for AI systems
- Integrating AI risk into enterprise risk management
Module 2. AI Vendor Landscape Analysis
Navigate the evolving ecosystem of AI vendors and service models.
12 chapters in this module
- Classifying AI vendors by capability and scope
- Understanding model-as-a-service offerings
- API-based AI integration risks
- Cloud provider AI services vs. third-party vendors
- Open-source model dependencies in vendor offerings
- Vendor consolidation trends and implications
- Assessing vendor financial and operational stability
- Evaluating vendor track record and client references
- Geopolitical considerations in AI sourcing
- Supply chain transparency for AI systems
- Benchmarking vendor performance claims
- Identifying red flags in vendor marketing materials
Module 3. Legal and Contractual Risk Frameworks
Structure contracts that protect organizational interests in AI engagements.
12 chapters in this module
- Data ownership and usage rights in AI contracts
- Model ownership and intellectual property clauses
- Liability for AI-generated outputs
- Indemnification strategies for AI failures
- Warranties and service level agreements
- Audit rights and transparency requirements
- Exit strategies and data portability
- Subcontractor and third-party dependencies
- Jurisdiction and dispute resolution
- Compliance with data protection regulations
- Handling model updates and version control
- Contractual enforcement of ethical AI principles
Module 4. Data Governance and Privacy Integration
Ensure AI vendor practices align with organizational data policies.
12 chapters in this module
- Data lineage and provenance in vendor systems
- Training data provenance and bias considerations
- Data minimization in AI processing
- Anonymization and pseudonymization effectiveness
- Cross-border data transfer compliance
- Consent management for AI training
- Data retention and deletion policies
- Access controls and authentication
- Data breach notification requirements
- Vendor data security certifications
- Monitoring data usage post-deployment
- Third-party data sourcing transparency
Module 5. Model Transparency and Explainability
Evaluate vendor claims about model behavior and decision-making.
12 chapters in this module
- Understanding model explainability techniques
- Assessing vendor-provided model documentation
- Evaluating interpretability for high-stakes decisions
- Model card and datasheet analysis
- Testing for algorithmic bias and fairness
- Performance metrics beyond accuracy
- Model uncertainty and confidence scoring
- Human-in-the-loop requirements
- Adversarial testing readiness
- Model drift detection capabilities
- Vendor response protocols for model anomalies
- Third-party model validation options
Module 6. Operational Resilience and Monitoring
Ensure AI systems perform reliably in production environments.
12 chapters in this module
- Uptime and availability expectations
- Disaster recovery and failover planning
- Performance monitoring and alerting
- Incident response for AI system failures
- Vendor support response times
- Change management processes
- Capacity planning for AI workloads
- Resource consumption transparency
- Dependency management for AI services
- Integration with existing monitoring tools
- Performance degradation detection
- Vendor escalation pathways
Module 7. Security and Threat Modeling
Assess AI vendor security posture against emerging threats.
12 chapters in this module
- Threat modeling for AI systems
- Adversarial attack surface analysis
- Model inversion and membership inference risks
- Prompt injection and manipulation defenses
- Secure API design and authentication
- Infrastructure security certifications
- Penetration testing policies
- Security patching timelines
- Vulnerability disclosure programs
- Zero-trust architecture alignment
- Supply chain security for AI components
- Incident history and response maturity
Module 8. Compliance and Regulatory Alignment
Map vendor practices to current and emerging regulatory requirements.
12 chapters in this module
- GDPR and AI processing requirements
- Sector-specific regulations (finance, healthcare, etc.)
- Algorithmic accountability frameworks
- Bias and discrimination compliance
- Recordkeeping and audit trail requirements
- Regulatory reporting obligations
- Pre-market assessment expectations
- Ongoing compliance monitoring
- Vendor regulatory engagement history
- Certification and attestation processes
- Cross-jurisdictional compliance challenges
- Preparing for regulatory audits
Module 9. Ethical AI and Social Impact
Evaluate vendor alignment with ethical AI principles and societal impact.
12 chapters in this module
- Defining organizational AI ethics principles
- Vendor alignment with ethical frameworks
- Human rights impact considerations
- Environmental impact of AI systems
- Labor displacement and augmentation effects
- Community and stakeholder engagement
- Transparency in AI decision-making
- Accountability mechanisms
- Redress processes for affected parties
- Diversity in AI development teams
- Bias mitigation strategies
- Long-term societal implications
Module 10. Financial and Performance Due Diligence
Assess the economic viability and performance claims of AI vendors.
12 chapters in this module
- Total cost of ownership analysis
- Pricing model transparency
- Hidden costs in AI vendor contracts
- Performance benchmarking and validation
- ROI measurement frameworks
- Scalability cost implications
- Vendor financial health indicators
- Funding history and sustainability
- Customer retention and churn rates
- Reference site validation
- Independent performance audits
- Cost-benefit analysis templates
Module 11. Integration and Change Management
Plan for successful adoption of AI vendor solutions across the organization.
12 chapters in this module
- Technical integration complexity assessment
- API compatibility and documentation quality
- Data format and schema alignment
- Legacy system integration challenges
- Change management for AI adoption
- User training and support needs
- Organizational readiness evaluation
- Stakeholder communication plans
- Pilot program design
- Success criteria definition
- Feedback loop implementation
- Scaling from pilot to production
Module 12. Ongoing Vendor Oversight and Exit Planning
Establish continuous monitoring and contingency plans for AI vendor relationships.
12 chapters in this module
- Ongoing performance monitoring
- Regular risk reassessment cycles
- Contract renewal and renegotiation
- Vendor performance scorecards
- Independent audit rights
- Exit strategy development
- Data migration and portability planning
- Knowledge transfer requirements
- Sunset process for AI systems
- Contingency planning for vendor failure
- Alternative vendor identification
- Lessons learned documentation
How this maps to your situation
- Evaluating a new AI vendor for a critical business function
- Renewing or renegotiating an existing AI vendor contract
- Responding to regulatory inquiries about AI usage
- Building internal AI governance capacity
Before vs. after
Before
Uncertainty in AI vendor evaluations, reliance on technical teams, reactive risk management, inconsistent standards across projects
After
Structured, repeatable assessment process, confident decision-making, proactive risk mitigation, alignment across legal, compliance, and business units
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 executive pacing with just-in-time application.
If nothing changes
Without a formal assessment framework, organizations risk compliance gaps, financial exposure, operational disruption, and reputational damage from poorly governed AI vendor relationships.
How this compares to the alternatives
Unlike generic AI ethics courses or technical security guides, this program focuses exclusively on the vendor assessment lifecycle with implementation-grade tools for senior leaders.
Frequently asked
Who is this course designed for?
Senior leaders in business and technology roles responsible for AI procurement, risk oversight, compliance, or strategic adoption.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical?
It addresses technical concepts at a governance level, not a coding or engineering level. Designed for decision-makers, not implementers.
$199 one-time. Approximately 3-4 hours per module, designed for executive pacing with just-in-time application..
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