A tailored course, built for your situation
Enterprise-Class AI Vendor Risk Assessment for Established Enterprises
Master governance, compliance, and due diligence for AI vendor integration at scale
$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 adoption is accelerating, but inconsistent vendor evaluations expose organizations to compliance gaps and operational risk.
The situation this course is for
Organizations are signing AI contracts faster than their risk frameworks can evolve. Without standardized assessment protocols, teams face misaligned expectations, regulatory exposure, and integration failures.
Who this is for
Risk officers, compliance leads, AI governance professionals, and technology executives in established enterprises overseeing third-party AI vendor adoption.
Who this is not for
Startups, individual developers, or teams evaluating open-source AI tools without formal vendor contracts.
What you walk away with
- Apply a standardized framework to assess AI vendor compliance with enterprise security and data policies
- Evaluate model transparency, explainability, and ethical alignment across vendor proposals
- Lead cross-functional risk assessments that align legal, IT, and business stakeholders
- Develop audit-ready documentation for AI procurement decisions
- Design escalation paths and SLA enforcement mechanisms for long-term vendor management
The 12 modules (with all 144 chapters)
Module 1. Foundations of Enterprise AI Risk
Establish core principles of AI risk in large organizations.
12 chapters in this module
- Defining enterprise-class AI risk
- Evolution of third-party AI adoption
- Key stakeholders in vendor assessment
- Regulatory landscape overview
- Internal policy alignment
- Risk taxonomy for AI systems
- Vendor lifecycle stages
- Governance maturity models
- Assessment scope definition
- Stakeholder communication plan
- Documenting assumptions and constraints
- Module integration roadmap
Module 2. Vendor Due Diligence Framework
Build a repeatable process for evaluating AI vendors.
12 chapters in this module
- Due diligence prerequisites
- Request for information design
- Security questionnaire structure
- Compliance checklist development
- Data handling expectations
- Jurisdictional considerations
- Certifications and attestations
- Third-party audit rights
- Subcontractor transparency
- Incident response obligations
- Right-to-audit clauses
- Documentation standards
Module 3. Model Transparency and Explainability
Assess technical transparency in vendor AI models.
12 chapters in this module
- Model card requirements
- Performance benchmarking
- Bias detection protocols
- Explainability methods
- Ground truth validation
- Drift monitoring commitments
- Confidence interval reporting
- Failure mode disclosure
- Human-in-the-loop design
- Red team testing expectations
- Model retraining frequency
- Version control transparency
Module 4. Data Governance and Privacy
Ensure vendor practices align with enterprise data policies.
12 chapters in this module
- Data ownership definitions
- Processing agreement terms
- Anonymization standards
- Cross-border transfer mechanisms
- Access control expectations
- Data retention policies
- Deletion and portability
- Logging and audit trails
- Consent management
- Breach notification timelines
- Data minimization compliance
- Vendor subprocessing rules
Module 5. Security and Resilience Assessment
Evaluate vendor security posture and system resilience.
12 chapters in this module
- Infrastructure security model
- Penetration testing access
- Vulnerability disclosure policy
- Encryption standards
- Authentication protocols
- Incident response plan
- Disaster recovery testing
- Availability SLAs
- Threat modeling access
- Patch management process
- Zero-day response protocol
- Security certification alignment
Module 6. Contractual and Legal Alignment
Align vendor agreements with enterprise legal standards.
12 chapters in this module
- Liability allocation
- Indemnification clauses
- IP ownership terms
- Warranty provisions
- Termination triggers
- Change control process
- Renewal and exit terms
- Dispute resolution
- Governing law selection
- Force majeure considerations
- Assignment and subcontracting
- Amendment process
Module 7. Ethical AI and Fairness Standards
Incorporate ethical principles into vendor evaluation.
12 chapters in this module
- Fairness metric selection
- Bias audit requirements
- Representation in training data
- Stakeholder impact assessment
- Redress mechanisms
- Monitoring for disparate impact
- Community engagement expectations
- Transparency in decision logic
- Human oversight requirements
- Ethics board access
- Public accountability commitments
- Remediation process design
Module 8. Integration and Interoperability
Assess technical compatibility with existing systems.
12 chapters in this module
- API design standards
- Data format expectations
- Authentication integration
- Logging and monitoring
- Error handling protocols
- Scalability testing
- Performance benchmarks
- Versioning strategy
- Backward compatibility
- Deprecation notice policy
- Support escalation paths
- Documentation completeness
Module 9. Performance Monitoring and SLAs
Define measurable service expectations and tracking.
12 chapters in this module
- SLA definition framework
- Uptime measurement method
- Latency thresholds
- Error rate tolerances
- Reporting frequency
- Penalty structures
- Remediation timelines
- Service credit process
- Escalation procedures
- Independent verification
- Third-party monitoring tools
- Continuous improvement commitments
Module 10. Change Management and Governance
Establish oversight for ongoing vendor management.
12 chapters in this module
- Change approval workflows
- Notification requirements
- Emergency change process
- Rollback expectations
- Stakeholder communication
- Documentation updates
- Training obligations
- User impact assessment
- Compliance revalidation
- Audit trail retention
- Version governance
- End-of-life planning
Module 11. Board and Executive Reporting
Translate technical assessments into strategic insights.
12 chapters in this module
- Risk appetite alignment
- Executive summary design
- Key risk indicators
- Vendor concentration risk
- Strategic alignment
- Budget implications
- Reputational exposure
- Regulatory horizon scanning
- Risk escalation protocols
- Portfolio-level view
- Vendor performance dashboard
- Success metrics reporting
Module 12. Continuous Improvement and Exit Planning
Plan for long-term vendor lifecycle management.
12 chapters in this module
- Performance review cycle
- Feedback integration
- Contract renewal assessment
- Exit trigger identification
- Data migration planning
- Knowledge transfer
- Lessons learned documentation
- Post-mortem process
- Vendor offboarding checklist
- Relationship closure
- Archival requirements
- Future procurement insights
How this maps to your situation
- Assessing a new AI vendor for enterprise deployment
- Renewing or renegotiating an existing AI vendor contract
- Responding to internal audit findings on vendor risk
- Building an internal AI governance framework
Before vs. after
Before
Overwhelmed by inconsistent vendor evaluations, unclear compliance obligations, and fragmented stakeholder input during AI procurement.
After
Equipped with a standardized, board-ready framework to assess, approve, and manage enterprise AI vendors with confidence and clarity.
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 with implementation-focused exercises.
If nothing changes
Without a structured approach, organizations face increased exposure to compliance gaps, operational disruptions, and reputational damage from poorly vetted AI vendors.
How this compares to the alternatives
Unlike generic AI ethics courses or compliance overviews, this program delivers implementation-grade frameworks specifically for evaluating and managing third-party AI vendors in complex enterprise environments.
Frequently asked
Who is this course designed for?
Risk officers, compliance leads, AI governance professionals, and technology executives in established enterprises managing AI vendor relationships.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course include practical tools?
Yes, every module includes downloadable templates, worked examples, and the full implementation playbook delivered at enrollment.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning with implementation-focused exercises..
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