A tailored course, built for your situation
Practical AI Vendor Risk Assessment for Regulated Industries
A 12-module implementation-grade course for business and technology professionals navigating AI procurement in high-compliance 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 vendor evaluations are increasingly complex, but most teams lack a structured, repeatable method to assess risk across legal, technical, and operational domains.
The situation this course is for
Without a consistent framework, organizations face delayed deployments, compliance gaps, and misalignment between teams. Regulators expect robust oversight, yet practitioners often rely on ad-hoc checklists or inherited processes not built for modern AI systems.
Who this is for
Business and technology professionals in regulated industries, compliance leads, risk officers, procurement specialists, legal advisors, data stewards, and engineering managers, responsible for evaluating or approving third-party AI solutions.
Who this is not for
This course is not for developers building in-house AI models or researchers focused on algorithmic fairness. It is designed for those assessing externally sourced AI systems, not creating them.
What you walk away with
- Apply a standardized risk assessment framework to any AI vendor engagement
- Identify compliance-critical controls across data governance, model transparency, and system reliability
- Construct vendor evaluation scorecards aligned with regulatory expectations
- Negotiate contract terms that protect organizational risk posture
- Lead cross-functional AI procurement reviews with confidence
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Environments
Establish core principles, regulatory touchpoints, and risk categories unique to third-party AI systems.
12 chapters in this module
- Defining AI vendor risk in context
- Key regulatory drivers across sectors
- Common failure points in AI procurement
- Risk vs. innovation trade-offs
- Stakeholder mapping for AI reviews
- Overview of compliance frameworks
- Jurisdictional considerations
- Vendor lifecycle stages
- Risk categorization models
- Case study: Banking sector onboarding
- Case study: Healthcare AI integration
- Module integration exercise
Module 2. Regulatory Alignment and Compliance Thresholds
Map AI vendor activities to current compliance requirements across data protection, fairness, and accountability regimes.
12 chapters in this module
- GDPR and AI processing implications
- Sector-specific rules: finance, health, education
- Algorithmic accountability standards
- Fair lending and anti-bias expectations
- Recordkeeping and audit trail mandates
- Cross-border data flow constraints
- Regulatory reporting obligations
- Emerging guidelines from standards bodies
- Interpreting 'reasonable assurance'
- Compliance threshold scoring
- Benchmarking against peer practices
- Module integration exercise
Module 3. Vendor Due Diligence: Legal and Contractual Frameworks
Build legally sound evaluation criteria and contract provisions that enforce risk controls.
12 chapters in this module
- Key clauses in AI vendor agreements
- IP ownership and model usage rights
- Liability allocation and indemnification
- Subprocessor transparency requirements
- Right-to-audit provisions
- Termination and exit rights
- Warranties on model performance
- Data processing addendums
- Insurance and financial safeguards
- Contract scoring rubric
- Negotiation playbook
- Module integration exercise
Module 4. Technical Risk Assessment for Non-Engineers
Evaluate technical documentation, architecture, and testing practices without needing to code.
12 chapters in this module
- Reading AI system design documents
- Understanding model inputs and outputs
- Data provenance and lineage tracking
- Testing for drift and degradation
- Model versioning and update policies
- Explainability techniques overview
- Security controls in AI pipelines
- API and integration risks
- Infrastructure resilience checks
- Third-party dependency mapping
- Technical red flag checklist
- Module integration exercise
Module 5. Data Governance and Privacy by Design
Ensure vendor practices align with organizational data policies and privacy obligations.
12 chapters in this module
- Data minimization in AI systems
- Anonymization and pseudonymization effectiveness
- Consent management integration
- Purpose limitation enforcement
- Data retention and deletion workflows
- Privacy impact assessment alignment
- On-premise vs. cloud processing trade-offs
- Data access logging and monitoring
- Cross-system data flow mapping
- Vendor data governance scoring
- Privacy-by-design checklist
- Module integration exercise
Module 6. Model Performance and Reliability Standards
Define and verify acceptable performance thresholds under real-world conditions.
12 chapters in this module
- Accuracy vs. precision in context
- Bias detection across demographic groups
- Stress testing under edge cases
- Performance monitoring in production
- Fallback mechanisms and human oversight
- Latency and uptime SLAs
- Error rate tolerance by use case
- Benchmarking against industry baselines
- Model card interpretation
- Performance reporting requirements
- Reliability scoring template
- Module integration exercise
Module 7. Operational Risk and Change Management
Assess how vendors manage updates, incidents, and system changes over time.
12 chapters in this module
- Change control processes
- Incident response planning
- Patch management timelines
- Rollback capabilities
- System availability commitments
- Monitoring and alerting practices
- Disaster recovery preparedness
- Vendor business continuity planning
- Communication protocols during outages
- Operational transparency metrics
- Change management scoring
- Module integration exercise
Module 8. Audit Readiness and Documentation Practices
Evaluate whether vendor documentation supports internal and external audit requirements.
12 chapters in this module
- Required artifacts for AI audits
- Model development lifecycle records
- Testing and validation logs
- Governance committee minutes
- Risk assessment documentation
- Compliance certification review
- Third-party audit reports (SOC 2, ISO)
- Internal control evidence collection
- Documentation completeness scoring
- Audit trail preservation
- Pre-audit vendor preparation checklist
- Module integration exercise
Module 9. Cross-Functional Coordination and Governance
Align legal, risk, IT, and business teams around a unified vendor review process.
12 chapters in this module
- Establishing AI governance committees
- RACI matrix for vendor reviews
- Intake and triage workflows
- Escalation paths for high-risk vendors
- Consensus-building techniques
- Decision logging and traceability
- Stakeholder communication plans
- Governance meeting cadences
- Cross-team playbook integration
- Governance maturity assessment
- Coordination efficiency metrics
- Module integration exercise
Module 10. Risk Scoring and Tiered Review Processes
Implement a scalable risk-based approach to vendor evaluation intensity.
12 chapters in this module
- Risk scoring model design
- Low, medium, high-risk categorization
- Use case criticality assessment
- Automated triage tools
- Expedited review pathways
- Full review triggers
- Scoring calibration sessions
- Risk threshold documentation
- Scorecard validation techniques
- Tiered approval workflows
- Scalable review dashboard
- Module integration exercise
Module 11. Continuous Monitoring and Post-Implementation Review
Maintain oversight after contract signing through structured monitoring and review cycles.
12 chapters in this module
- Ongoing performance tracking
- Compliance reassessment schedules
- Key risk indicator dashboards
- Periodic control testing
- Contract renewal risk review
- User feedback collection
- Incident trend analysis
- Vendor maturity progression
- Exit readiness assessment
- Monitoring report templates
- Continuous improvement loop
- Module integration exercise
Module 12. Implementing Your AI Vendor Risk Program
Assemble all components into a tailored, organization-specific risk assessment program.
12 chapters in this module
- Gap analysis of current state
- Roadmap for program rollout
- Policy drafting guidance
- Template customization
- Training plan development
- Stakeholder onboarding
- Pilot program design
- Success metric definition
- Executive reporting framework
- Program audit preparation
- Sustaining long-term adoption
- Final integration project
How this maps to your situation
- Onboarding a new AI vendor in a regulated function
- Responding to internal audit findings on AI procurement
- Designing a centralized AI governance process
- Scaling AI adoption while maintaining compliance
Before vs. after
Before
AI vendor assessments are inconsistent, reactive, and siloed, leading to delays, compliance exposure, and team misalignment.
After
Your team applies a structured, repeatable, and regulator-ready framework to every AI vendor engagement, enabling faster, safer adoption.
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 professionals to progress at their own pace while applying concepts to real work.
If nothing changes
Without a formal approach, organizations risk delayed deployments, regulatory scrutiny, and operational disruptions from poorly vetted AI vendors.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers actionable, implementation-grade guidance specific to third-party AI risk in regulated environments, structured for immediate application, not theoretical discussion.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, legal advisors, procurement leads, and technology leaders in regulated industries evaluating third-party AI solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical expertise required?
No. The course is designed for professionals who need to assess AI vendors without being data scientists or engineers.
$199 one-time. Approximately 3-4 hours per module, designed for professionals to progress at their own pace while applying concepts to real work..
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