A tailored course, built for your situation
Pragmatic AI Vendor Risk Assessment for Mid-Market Operations
A structured, implementation-grade framework for evaluating AI vendor risk in mid-market environments
$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 vendor risk practices remain ad hoc and reactive, creating friction, compliance gaps, and operational debt.
The situation this course is for
Mid-market organizations are adopting AI faster than their risk frameworks can evolve. Teams lack standardized methods to assess vendor trustworthiness, validate claims, or enforce accountability, leading to delayed deployments, compliance exposure, and strained cross-functional alignment.
Who this is for
Operations, compliance, and technology leaders in mid-market organizations overseeing AI procurement, deployment, or governance.
Who this is not for
Enterprise GRC teams with mature AI governance boards and dedicated legal resources; startups using only open-source or no-code AI tools without vendor contracts.
What you walk away with
- Apply a repeatable 5-phase framework to assess AI vendor risk
- Map vendor obligations to compliance requirements (HIPAA, SOC 2, GDPR)
- Evaluate technical claims using lightweight validation playbooks
- Negotiate contract terms that protect operational continuity
- Build internal alignment between legal, security, and operations teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Define core concepts, risk categories, and the role of operations in AI procurement.
12 chapters in this module
- Understanding AI vendor ecosystems
- Key risk dimensions: technical, legal, operational
- Differentiating AI from traditional software risk
- The mid-market context: resources, speed, and scale
- Risk ownership across functions
- Common misconceptions about AI safety
- How AI contracts differ from SaaS agreements
- The lifecycle of AI vendor engagement
- Internal stakeholder mapping
- Building a risk-aware culture
- Regulatory touchpoints in AI procurement
- Course navigation and tools overview
Module 2. Due Diligence Framework
Implement a structured approach to pre-contract vendor evaluation.
12 chapters in this module
- Designing a due diligence checklist
- Assessing vendor legitimacy and funding stability
- Evaluating team expertise and turnover risk
- Reviewing third-party audits and certifications
- Validating AI use case alignment
- Identifying red flags in marketing claims
- Benchmarking against peer vendors
- Documenting assumptions and gaps
- Engaging security teams early
- Creating a vendor shortlist
- Scoring systems for comparative analysis
- Due diligence reporting templates
Module 3. Compliance Mapping
Align vendor capabilities with regulatory and organizational requirements.
12 chapters in this module
- Mapping AI use to HIPAA, GDPR, and SOC 2
- Data residency and processing obligations
- Consent and transparency requirements
- Vendor roles: processor vs. controller
- Audit rights and access provisions
- Incident response coordination
- Documentation standards for compliance
- Handling data subject requests through vendors
- Cross-border data transfer mechanisms
- Compliance validation workflows
- Maintaining evidence trails
- Compliance playbook templates
Module 4. Technical Validation
Verify vendor claims with lightweight, repeatable technical checks.
12 chapters in this module
- Assessing model transparency and documentation
- Testing input/output behavior under load
- Evaluating bias and fairness claims
- Reviewing training data provenance
- API reliability and uptime verification
- Latency and scalability benchmarks
- Security testing: penetration and vulnerability scans
- Model drift detection methods
- Output consistency checks
- Integration testing with existing systems
- Failover and disaster recovery validation
- Technical validation report templates
Module 5. Contractual Safeguards
Identify and enforce key contractual terms that reduce risk.
12 chapters in this module
- Must-have clauses for AI vendor contracts
- Service Level Agreements for AI performance
- Data ownership and usage rights
- Model retraining and versioning terms
- Right to audit provisions
- Termination and exit clauses
- Liability caps and indemnification
- Insurance requirements for AI vendors
- Subprocessor disclosure obligations
- Change management and notification terms
- Dispute resolution mechanisms
- Contract review checklist
Module 6. Operational Monitoring
Establish ongoing oversight of AI vendor performance and compliance.
12 chapters in this module
- Designing operational KPIs for AI vendors
- Monthly performance review processes
- Automated alerting for service degradation
- Tracking model accuracy over time
- Monitoring for bias or drift
- Compliance status dashboards
- Incident reporting workflows
- Vendor communication protocols
- Quarterly business review templates
- Escalation paths for unresolved issues
- Renewal readiness assessments
- Monitoring playbook templates
Module 7. Incident Response Planning
Prepare for and respond to AI-related incidents involving vendors.
12 chapters in this module
- Defining AI incident types
- Roles and responsibilities during incidents
- Vendor notification timelines
- Data breach coordination protocols
- Model failure triage
- Reputation risk management
- Legal and regulatory reporting
- Internal communication plans
- Post-incident review processes
- Lessons learned documentation
- Vendor accountability tracking
- Incident response templates
Module 8. Stakeholder Alignment
Align legal, security, operations, and business teams on vendor risk.
12 chapters in this module
- Identifying key stakeholders
- Creating shared risk language
- Facilitating cross-functional workshops
- Documenting decision rationale
- Balancing speed and safety
- Escalation frameworks for disagreements
- Change approval workflows
- Vendor risk communication plans
- Training non-technical stakeholders
- Building trust across silos
- Governance committee structures
- Alignment playbook templates
Module 9. Risk Prioritization
Focus on the highest-impact risks with limited resources.
12 chapters in this module
- Risk scoring methodologies
- Likelihood vs. impact assessment
- Criticality of AI use cases
- Resource-constrained risk management
- Tiered vendor classification
- Time-bound risk acceptance
- Dynamic risk reassessment
- Risk register maintenance
- Reporting to leadership
- Risk appetite documentation
- Prioritization decision logs
- Prioritization templates
Module 10. Exit Strategy Design
Plan for vendor transitions before contracts begin.
12 chapters in this module
- Defining exit triggers
- Data portability requirements
- Model retraining considerations
- Knowledge transfer expectations
- Contractual exit rights
- Transition timeline planning
- Identifying replacement vendors
- Cost of exit estimation
- Minimizing operational disruption
- Exit readiness assessments
- Sunset planning for AI features
- Exit strategy templates
Module 11. Scaling Across Use Cases
Replicate risk assessment practices across multiple AI initiatives.
12 chapters in this module
- Creating reusable assessment templates
- Standardizing evaluation workflows
- Centralizing vendor information
- Building a vendor risk knowledge base
- Training new team members
- Automating risk assessments
- Integrating with procurement systems
- Versioning assessment frameworks
- Feedback loops for improvement
- Scaling governance without bureaucracy
- Cross-departmental adoption
- Scaling playbook templates
Module 12. Future-Proofing AI Risk
Anticipate emerging risks and adapt frameworks accordingly.
12 chapters in this module
- Tracking regulatory developments
- Monitoring AI research trends
- Assessing generative AI risks
- Evaluating open-weight models
- Adapting to new attack vectors
- Ethical AI considerations
- Reputation risk from AI misuse
- Long-term vendor sustainability
- AI insurance market trends
- Scenario planning for disruption
- Building adaptive risk frameworks
- Future-proofing checklist
How this maps to your situation
- Assessing a new AI vendor for clinical data processing
- Managing compliance for an AI-powered patient engagement tool
- Responding to a model performance degradation incident
- Planning exit from an underperforming AI analytics vendor
Before vs. after
Before
AI vendor decisions are made reactively, with inconsistent criteria, fragmented documentation, and limited cross-team alignment.
After
Your team applies a standardized, auditable framework to assess and manage AI vendor risk, accelerating deployment while reducing exposure.
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 asynchronous, self-paced learning with practical implementation milestones.
If nothing changes
Without a structured approach, organizations face delayed AI adoption, compliance gaps, operational friction, and increased exposure to vendor failure or misuse.
How this compares to the alternatives
Unlike generic AI ethics courses or enterprise-focused GRC programs, this course delivers mid-market-specific, operationally actionable methods, not theory. It fills the gap between high-level principles and vendor-specific playbooks.
Frequently asked
Who is this course for?
Operations, compliance, and technology leaders in mid-market organizations managing AI vendor procurement or governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and assessments.
$199 one-time. Approximately 3-4 hours per module, designed for asynchronous, self-paced learning with practical implementation milestones..
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