A tailored course, built for your situation
Operationally-Sound AI Vendor Risk Assessment for Regulated Industries
A structured, implementation-grade path 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 adoption in regulated environments is accelerating, but vendor risk practices haven't kept pace, creating friction, rework, and compliance exposure.
The situation this course is for
Teams are expected to move quickly on AI initiatives, yet lack standardized, defensible methods for assessing third-party AI vendors. This leads to inconsistent evaluations, delayed deployments, and last-minute compliance scrambles. The absence of clear frameworks forces professionals to reinvent the wheel for every procurement, increasing risk and reducing strategic impact.
Who this is for
Business and technology professionals in regulated industries, compliance officers, risk managers, procurement leads, IT architects, data governance leads, and product owners, who are accountable for safe, compliant AI adoption through third-party vendors.
Who this is not for
This course is not for executives seeking high-level overviews, vendors marketing AI tools, or technical researchers focused on model development. It is for practitioners who must implement and defend AI vendor risk decisions.
What you walk away with
- Apply a repeatable framework for assessing AI vendor risk across regulatory domains
- Map vendor capabilities to compliance obligations in real time
- Structure due diligence workflows that reduce review cycles by 40-60%
- Negotiate contracts with clear AI-specific risk controls and exit clauses
- Build audit-ready documentation packages for AI vendor engagements
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core definitions, regulatory touchpoints, and the business case for operational rigor.
12 chapters in this module
- Defining AI vendor risk in context
- Regulatory drivers across sectors
- The cost of ad hoc assessment
- Key stakeholders and their concerns
- Risk taxonomy for AI vendors
- Maturity model for vendor oversight
- Common failure patterns
- Balancing innovation and control
- Emerging expectations from boards
- Linking risk to business outcomes
- Scope definition for assessments
- Course roadmap and tools
Module 2. Regulatory Landscape and Compliance Mapping
Navigate overlapping requirements from GDPR, HIPAA, SOX, NYDFS, and sector-specific mandates.
12 chapters in this module
- GDPR and automated decision-making
- HIPAA considerations for AI in health
- SOX implications for AI-driven finance
- NYDFS and AI in financial services
- Sector-specific regulatory updates
- Cross-jurisdictional challenges
- Mapping controls to obligations
- Using NIST AI RMF in practice
- ISO standards for AI trustworthiness
- Preparing for upcoming AI Acts
- Compliance as a procurement lever
- Maintaining up-to-date mappings
Module 3. Vendor Due Diligence: Technical and Operational Readiness
Evaluate AI vendors on model provenance, data handling, scalability, and resilience.
12 chapters in this module
- Assessing model development lifecycle
- Data sourcing and lineage verification
- Model documentation standards
- Bias detection and mitigation plans
- Performance benchmarking expectations
- Scalability under load
- Disaster recovery and uptime SLAs
- Incident response preparedness
- Third-party dependency risks
- API security and integration safety
- Patch management and updates
- Vendor financial and operational stability
Module 4. Contractual Risk Controls and Negotiation Levers
Structure agreements with enforceable terms for audit rights, IP, liability, and exit.
12 chapters in this module
- Defining AI-specific contract clauses
- Audit rights and transparency obligations
- Intellectual property ownership
- Liability for model errors or drift
- Data ownership and portability
- Right to inspect model behavior
- Exit strategies and data handback
- Penalties for non-compliance
- Subcontractor oversight requirements
- Change control and version tracking
- Service level agreements for AI
- Negotiation playbook and templates
Module 5. Data Governance and Privacy by Design
Ensure AI vendors embed privacy and governance into architecture and process.
12 chapters in this module
- Privacy impact assessment integration
- Data minimization in AI systems
- Anonymization and de-identification
- Cross-border data flow compliance
- Consent management alignment
- Purpose limitation enforcement
- Data retention and deletion
- Access control and role separation
- Logging and monitoring requirements
- Vendor access to customer data
- Encryption in transit and at rest
- Privacy by design maturity checklist
Module 6. Model Risk Management Integration
Align AI vendor assessments with existing model risk frameworks.
12 chapters in this module
- MRM lifecycle alignment
- Pre-deployment validation expectations
- Ongoing performance monitoring
- Model drift detection protocols
- Version control and rollback
- Independent validation requirements
- Documentation for model audits
- Stress testing AI models
- Scenario analysis for edge cases
- Model inventory integration
- Change approval workflows
- MRM reporting and escalation
Module 7. Security and Resilience Assessment
Evaluate AI vendors on cyber readiness, attack surface, and response capability.
12 chapters in this module
- Penetration testing results review
- Vulnerability disclosure practices
- Secure development lifecycle
- Threat modeling for AI systems
- Adversarial attack resistance
- Model inversion and extraction risks
- Supply chain security for AI
- Zero-trust architecture alignment
- Incident response coordination
- Breach notification timelines
- Security certifications and attestations
- Continuous monitoring integration
Module 8. Ethics, Fairness, and Societal Impact
Assess vendor approaches to ethical AI, fairness, and public accountability.
12 chapters in this module
- Ethical AI principles alignment
- Fairness metrics and reporting
- Stakeholder impact assessments
- Transparency and explainability
- Human oversight mechanisms
- Redress processes for harm
- Community engagement practices
- Algorithmic accountability
- Bias audit requirements
- Public reporting and disclosure
- Ethics board or review process
- Handling controversial use cases
Module 9. Ongoing Monitoring and Performance Validation
Implement continuous oversight for AI vendor performance and compliance.
12 chapters in this module
- Key risk indicators for AI vendors
- Automated monitoring tools
- Quarterly review cadence
- Performance benchmarking
- Compliance check-in protocols
- Customer feedback integration
- Regulatory change alerts
- Vendor self-reporting validation
- Escalation pathways
- Corrective action tracking
- Renewal risk assessment
- Sunset planning
Module 10. Audit Readiness and Documentation
Build defensible, organized records for internal and external audits.
12 chapters in this module
- Audit trail requirements
- Document retention policies
- Evidence collection workflows
- Internal audit coordination
- External auditor expectations
- Regulatory examination prep
- Gap remediation tracking
- Management attestation
- Third-party attestation use
- Version-controlled documentation
- Secure storage and access
- Audit simulation exercises
Module 11. Cross-Functional Collaboration and Governance
Orchestrate alignment between legal, risk, IT, procurement, and business units.
12 chapters in this module
- RACI matrix for AI vendor risk
- Governance committee structure
- Escalation protocols
- Decision rights and approvals
- Communication templates
- Conflict resolution mechanisms
- Training for stakeholders
- Change management for new processes
- Feedback loops across teams
- Metrics for governance effectiveness
- Board reporting cadence
- Continuous improvement cycle
Module 12. Implementation Roadmap and Continuous Improvement
Deploy the framework and evolve it with changing technology and regulation.
12 chapters in this module
- Assessment maturity baseline
- Pilot program design
- Tooling and automation options
- Integration with procurement systems
- Training rollout plan
- KPIs for program success
- Feedback collection and iteration
- Regulatory horizon scanning
- Benchmarking against peers
- Scaling across business units
- Annual review and update
- Sustaining executive support
How this maps to your situation
- Evaluating a new AI vendor for a core business function
- Responding to increased regulatory scrutiny on third-party AI use
- Building internal capability to assess AI vendors consistently
- Preparing for an audit of existing AI vendor arrangements
Before vs. after
Before
Unstructured evaluations, reactive compliance, fragmented documentation, and delayed deployments.
After
Standardized, defensible assessments, proactive compliance, audit-ready records, and faster, safer AI 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 flexible, asynchronous learning with implementation milestones.
If nothing changes
Without a structured approach, organizations face inconsistent risk decisions, compliance gaps, and operational disruptions, especially as AI vendor scrutiny intensifies across regulated sectors.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk overviews, this program delivers specific, actionable methods for assessing AI vendors in regulated environments, with templates, checklists, and a playbook built for real-world use.
Frequently asked
Who is this course designed for?
Business and technology professionals in regulated industries who are responsible for assessing, approving, or overseeing AI vendors, especially in compliance, risk, procurement, IT, and data governance roles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is available after finishing all modules and passing the final assessment.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, asynchronous learning with 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