A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Cross-Functional Programs
Master implementation-grade risk frameworks for AI procurement and cross-team execution
$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.
Organizations are adopting generative AI faster than risk frameworks can keep up, creating execution gaps between procurement, compliance, and delivery teams.
The situation this course is for
Cross-functional AI initiatives often stall due to misaligned risk criteria, inconsistent vendor evaluation practices, and lack of shared playbooks between legal, security, and technical stakeholders. This leads to delayed deployments, rework, and compliance friction.
Who this is for
Business and technology professionals leading or supporting AI vendor selection, risk assessment, and cross-team implementation in mid-market organizations.
Who this is not for
This is not for individual contributors focused solely on internal AI tools without vendor interaction, or executives seeking only high-level overviews without implementation detail.
What you walk away with
- Apply a structured, repeatable framework for assessing AI vendor risk across technical, legal, and operational domains
- Align cross-functional teams around shared evaluation criteria and documentation standards
- Integrate risk-weighted decision gates into procurement workflows
- Navigate model provenance, data licensing, and IP considerations with confidence
- Deploy vendor assessment playbooks that scale across programs
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduce core concepts, market landscape, and risk taxonomy specific to AI third-party engagement.
12 chapters in this module
- Defining AI vendor risk in modern procurement
- Evolution of third-party risk in the AI era
- Key stakeholders in cross-functional assessment
- Regulatory signals shaping vendor expectations
- Risk vs. innovation trade-offs in AI adoption
- Vendor lifecycle stages and risk touchpoints
- Common failure patterns in AI procurement
- Differentiating cloud, API, and model-based vendors
- Establishing risk tolerance thresholds
- Mapping organizational maturity to vendor complexity
- Case study: Early-stage AI integration
- Case study: Enterprise-scale AI rollout
Module 2. Governance Frameworks for AI Procurement
Explore policies, oversight models, and escalation paths for managing AI vendor relationships.
12 chapters in this module
- Designing AI-specific governance charters
- Board-level reporting expectations
- Cross-functional risk committee structures
- Policy development for AI acquisitions
- Vendor classification by risk tier
- Delegation of authority in procurement
- Audit readiness for AI vendor portfolios
- Incident response planning with vendors
- Third-party assurance requirements
- Maintaining policy agility amid AI shifts
- Integrating with existing GRC platforms
- Benchmarking governance maturity
Module 3. Technical Due Diligence for AI Vendors
Assess model architecture, data provenance, and system reliability in vendor offerings.
12 chapters in this module
- Evaluating model transparency and documentation
- Verifying training data sources and licenses
- Assessing bias detection and mitigation practices
- Model versioning and update protocols
- API security and rate-limiting controls
- Infrastructure resilience and uptime SLAs
- Model drift monitoring capabilities
- Explainability and interpretability features
- Red teaming and adversarial testing
- Penetration testing expectations
- Source code escrow considerations
- Technical exit strategies
Module 4. Legal and Contractual Risk Mitigation
Structure agreements that protect IP, limit liability, and enforce compliance obligations.
12 chapters in this module
- Defining ownership of outputs and models
- Negotiating IP indemnification clauses
- Warranties for model performance and fairness
- Liability caps and insurance requirements
- Data processing addendums for AI systems
- Subprocessor transparency obligations
- Right-to-audit provisions
- Termination and data portability terms
- Jurisdiction-specific AI compliance clauses
- Export control and sanctions screening
- Open-source license compliance
- Dispute resolution mechanisms
Module 5. Security and Data Protection Alignment
Ensure AI vendors meet organizational security standards and data handling expectations.
12 chapters in this module
- Mapping vendor controls to internal policies
- Data encryption in transit and at rest
- Access control and identity management
- Data retention and deletion protocols
- Security certifications and attestations
- Penetration test report validation
- Incident notification timelines
- Shared responsibility model clarity
- PII handling and anonymization practices
- Vendor SOC 2 and ISO 27001 alignment
- Cloud security posture assessment
- Zero-trust architecture integration
Module 6. Compliance and Regulatory Readiness
Prepare for audits and regulatory scrutiny in AI vendor engagements.
12 chapters in this module
- Aligning with NIST AI Risk Management Framework
- Preparing for EU AI Act compliance
- State-level AI regulations in the US
- Financial industry AI oversight expectations
- Healthcare AI compliance considerations
- Vendor certification and attestation requirements
- Recordkeeping for audit trails
- Regulatory change monitoring
- Ethical AI principles in practice
- Bias impact assessments
- Transparency reporting obligations
- Compliance automation tools
Module 7. Financial and Operational Risk Assessment
Evaluate vendor stability, pricing models, and long-term sustainability.
12 chapters in this module
- Vendor financial health indicators
- Burn rate and funding stage analysis
- Pricing model transparency
- Cost escalation triggers
- Business continuity planning
- Vendor lock-in risks
- Multi-cloud deployment flexibility
- Total cost of ownership modeling
- Service credit calculations
- Performance-based pricing structures
- Exit cost estimation
- Third-party dependency mapping
Module 8. Cross-Functional Team Alignment
Foster collaboration between legal, security, engineering, and business units.
12 chapters in this module
- Identifying core stakeholder needs
- Creating shared assessment scorecards
- Facilitating joint evaluation sessions
- Documenting consensus and dissent
- Escalation paths for unresolved risks
- Role-based access to assessment data
- Change management for new workflows
- Training cross-functional assessors
- Feedback loops between teams
- Conflict resolution in vendor decisions
- Executive communication strategies
- Metrics for team alignment
Module 9. Risk-Weighted Evaluation Workflows
Implement scalable processes for assessing vendors based on risk tier.
12 chapters in this module
- Designing tiered assessment checklists
- Automating initial screening questions
- Dynamic questionnaire routing
- Conditional evidence collection
- Risk scoring algorithms
- Threshold-based approval paths
- Fast-track pathways for low-risk vendors
- Escalation workflows for high-risk vendors
- Integration with procurement systems
- Continuous monitoring triggers
- Reassessment frequency planning
- Workflow auditability
Module 10. Implementation Playbook Development
Build organization-specific templates, checklists, and tooling.
12 chapters in this module
- Customizing assessment frameworks
- Creating vendor onboarding checklists
- Developing due diligence playbooks
- Template library curation
- Tool integration strategies
- Version control for assessment assets
- Knowledge transfer protocols
- Onboarding new team members
- Updating playbooks with market changes
- Lessons learned capture
- Benchmarking against peers
- Scaling playbook adoption
Module 11. Vendor Performance Monitoring
Track ongoing compliance, performance, and risk posture post-contract.
12 chapters in this module
- Establishing KPIs and SLAs
- Continuous monitoring tools
- Quarterly business reviews
- Incident response coordination
- Model performance drift detection
- Security posture revalidation
- Compliance change alerts
- Financial stability tracking
- Relationship health scoring
- Renewal readiness assessment
- Exit planning triggers
- Lessons learned documentation
Module 12. Scaling AI Risk Programs
Expand vendor risk capabilities across multiple teams and business units.
12 chapters in this module
- Centralized vs. federated governance models
- Shared services for risk assessment
- Training internal assessors
- Standardizing across business lines
- Technology platform selection
- Vendor risk data aggregation
- Executive reporting dashboards
- Budgeting for risk operations
- Hiring and resourcing plans
- Mergers and acquisitions integration
- Global program coordination
- Future-proofing for AI evolution
How this maps to your situation
- Assessing a new AI vendor for enterprise deployment
- Aligning legal, security, and engineering teams on risk criteria
- Responding to internal audit findings on third-party AI use
- Scaling a centralized AI risk function across business units
Before vs. after
Before
Unstructured evaluations, misaligned stakeholder expectations, and reactive risk responses slow down AI adoption and create compliance exposure.
After
A standardized, scalable approach to AI vendor assessment enables faster, safer adoption with clear accountability and cross-functional alignment.
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 24 hours of self-paced learning, with implementation activities extending value into daily practice.
If nothing changes
Organizations that lack structured AI vendor risk practices face increased exposure to compliance incidents, costly rework, and stalled innovation due to unresolved risk questions.
How this compares to the alternatives
Unlike generic third-party risk courses, this program focuses specifically on AI vendor complexities, model provenance, data licensing, bias mitigation, and dynamic compliance, offering implementation-grade detail not found in surface-level overviews or certification prep materials.
Frequently asked
Who is this course designed for?
It's for business and technology professionals involved in AI vendor selection, risk assessment, and cross-functional implementation, including risk officers, compliance leads, procurement specialists, and technical architects.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, upon finishing all modules, participants receive a certificate of completion from The Art of Service.
$199 one-time. Approximately 24 hours of self-paced learning, with implementation activities extending value into daily practice..
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