A tailored course, built for your situation
Production-Grade AI Vendor Risk Assessment for Regulated Industries
Master compliance-aligned AI procurement with implementation-grade frameworks
$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 still rely on checklists, not production-grade validation
The situation this course is for
Organizations are adopting AI rapidly, but vendor risk processes haven't kept pace. Legacy due diligence fails to assess real-world model behavior, auditability, or compliance integration, leading to rework, delays, and regulatory scrutiny.
Who this is for
Compliance officers, risk managers, and technical leads in regulated industries overseeing AI procurement and deployment
Who this is not for
This course is not for individuals seeking introductory AI awareness or non-technical overviews of AI ethics.
What you walk away with
- Apply a structured framework for assessing AI vendor readiness in regulated environments
- Integrate compliance requirements into technical due diligence workflows
- Evaluate model documentation, audit trails, and update governance practices
- Prepare for third-party audits with standardized evidence collection
- Lead cross-functional AI procurement initiatives with confidence
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core principles and regulatory expectations for AI procurement
12 chapters in this module
- Defining regulated AI use cases
- Overview of compliance frameworks
- Key regulatory bodies and jurisdictions
- Risk tolerance by sector
- AI lifecycle stages and risk exposure
- Vendor ecosystem mapping
- Internal stakeholder alignment
- Procurement policy integration
- Risk categorization models
- Due diligence triggers
- Threshold-based assessment design
- Course navigation and tools
Module 2. Regulatory Boundary Setting and Compliance Mapping
Translate regulations into actionable vendor assessment criteria
12 chapters in this module
- Mapping GDPR to AI workflows
- HIPAA implications for model training
- SOX controls in AI decisioning
- SEC guidance on algorithmic transparency
- FFIEC expectations for model risk
- NERC-CIP and critical infrastructure
- Compliance-by-design principles
- Jurisdictional conflict resolution
- Regulatory change monitoring
- Internal policy drafting
- Control ownership models
- Audit trail requirements
Module 3. Technical Due Diligence for AI Systems
Assess model architecture, data provenance, and system reliability
12 chapters in this module
- Model documentation standards
- Training data lineage verification
- Bias detection protocols
- Explainability requirements
- Performance benchmarking
- Failure mode analysis
- Redundancy and fallback design
- Latency and scalability testing
- Model versioning practices
- Reproducibility validation
- Third-party dependency review
- Security-by-design integration
Module 4. Vendor Onboarding and Contractual Safeguards
Structure agreements to enforce ongoing compliance and performance
12 chapters in this module
- Risk-based vendor tiering
- Service level agreement design
- Data ownership clauses
- Audit rights negotiation
- Penalty frameworks for non-compliance
- Exit strategy requirements
- IP ownership and licensing
- Subcontractor oversight
- Change management protocols
- Incident response coordination
- Liability allocation models
- Renewal and termination triggers
Module 5. Model Validation and Testing Protocols
Implement repeatable validation processes for pre- and post-deployment
12 chapters in this module
- Validation vs verification distinction
- Test data set curation
- Ground truth establishment
- Drift detection thresholds
- Stress testing scenarios
- Edge case identification
- Cross-validation techniques
- Shadow model deployment
- A/B testing in regulated contexts
- Human-in-the-loop validation
- Third-party validation options
- Validation documentation standards
Module 6. Audit Readiness and Evidence Management
Prepare for internal and external audits with structured evidence collection
12 chapters in this module
- Audit scope definition
- Evidence taxonomy design
- Automated evidence pipelines
- Version-controlled documentation
- Access control for audit logs
- Regulator communication protocols
- Pre-audit self-assessment
- Corrective action planning
- Findings tracking systems
- Audit follow-up cadence
- Regulatory reporting alignment
- Continuous monitoring integration
Module 7. Change Management and Ongoing Oversight
Maintain compliance through model updates, retraining, and decommissioning
12 chapters in this module
- Model change approval workflows
- Retraining trigger criteria
- Version comparison protocols
- Decommissioning checklists
- Stakeholder notification plans
- Backward compatibility assessment
- Rollback capability design
- Change impact analysis
- Model registry integration
- Version sunset policies
- Legacy system interaction
- Change audit trail maintenance
Module 8. Incident Response and Model Monitoring
Detect, respond to, and document AI system anomalies
12 chapters in this module
- Anomaly detection thresholds
- Incident classification frameworks
- Response team activation
- Regulatory breach notification
- Model rollback procedures
- Post-incident review process
- Monitoring tool integration
- Real-time alerting design
- False positive reduction
- User feedback integration
- Model performance degradation
- Security incident coordination
Module 9. Cross-Functional Collaboration Models
Align legal, compliance, engineering, and business teams
12 chapters in this module
- RACI matrix design
- Cross-team communication protocols
- Joint risk assessment workshops
- Shared documentation platforms
- Conflict resolution frameworks
- Decision escalation paths
- Unified risk scoring
- Stakeholder training plans
- Governance committee structure
- Feedback loop integration
- Performance metric alignment
- Change coordination workflows
Module 10. Scaling AI Governance Across Portfolios
Extend vendor risk practices across multiple AI initiatives
12 chapters in this module
- Centralized governance models
- Tiered risk assessment
- Portfolio-level reporting
- Resource allocation strategies
- Tooling standardization
- Knowledge sharing systems
- Vendor consolidation opportunities
- Cross-program benchmarking
- Governance maturity models
- Automation of routine checks
- Third-party oversight scaling
- Global compliance alignment
Module 11. Emerging Regulatory Trends and Future-Proofing
Anticipate upcoming requirements and adapt vendor risk strategies
12 chapters in this module
- Global regulatory horizon scanning
- AI liability frameworks
- Explainability mandates
- Environmental impact assessment
- Workforce displacement considerations
- Human rights impact analysis
- AI insurance requirements
- International standard adoption
- Public disclosure expectations
- Stakeholder engagement trends
- Ethical review board models
- Future regulatory scenario planning
Module 12. Implementation Playbook and Continuous Improvement
Deploy and refine vendor risk assessment in real-world settings
12 chapters in this module
- Pilot program design
- Stakeholder onboarding
- Process documentation
- Tool integration roadmap
- Feedback collection mechanisms
- KPI definition and tracking
- Lessons learned frameworks
- Governance iteration cycles
- Benchmarking against peers
- Continuous training programs
- External validation strategies
- Maturity progression planning
How this maps to your situation
- AI procurement in financial services
- Healthcare AI vendor due diligence
- Regulatory audit preparation
- Cross-jurisdictional compliance alignment
Before vs. after
Before
Relying on generic checklists and ad-hoc reviews for AI vendor assessment
After
Leading structured, repeatable, and regulator-ready evaluations across the AI lifecycle
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 45, 60 hours of self-paced learning, designed for professionals balancing full-time roles.
If nothing changes
Continuing with outdated due diligence methods increases exposure to compliance failures, operational disruption, and reputational harm during audits or incidents.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade frameworks tailored to regulated industry requirements, with actionable templates and real-world validation techniques.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and technical leads in regulated industries overseeing AI procurement and deployment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed for professionals balancing full-time roles..
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