A tailored course, built for your situation
Enterprise-Class AI Vendor Risk Assessment for Regulated Industries
Master implementation-grade risk governance for AI in highly regulated 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.
Deploying AI without a formal vendor risk framework creates compliance exposure and operational lag
The situation this course is for
Teams in regulated industries face increasing pressure to adopt AI while maintaining audit readiness, data sovereignty, and control over third-party dependencies. Generic risk checklists fail under scrutiny. Without a tailored, enterprise-class methodology, organizations delay deployment or accept unacceptable liability.
Who this is for
Compliance officers, risk leads, AI governance leads, and technology executives in finance, healthcare, energy, and government sectors who are accountable for third-party AI vendor oversight
Who this is not for
This is not for developers seeking coding tutorials, students exploring AI concepts, or marketers looking for AI tools. It’s designed for professionals responsible for binding organizational risk decisions.
What you walk away with
- Apply a proven framework to assess AI vendor risk across 12 critical domains
- Align vendor evaluations with regulatory expectations in GDPR, HIPAA, SOX, and similar frameworks
- Deploy a repeatable due diligence process that scales across procurement cycles
- Leverage templates and checklists used in Fortune 500 vendor assessments
- Lead cross-functional AI risk reviews with confidence and clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Regulated Environments
Establish core principles of AI risk, regulatory scope, and third-party accountability.
12 chapters in this module
- Defining enterprise-class AI risk
- Regulatory drivers shaping vendor oversight
- Key differences: AI vs traditional software risk
- Stakeholder mapping in risk assessment
- Governance models for AI procurement
- Risk tolerance frameworks by sector
- Vendor lifecycle overview
- Due diligence triggers and thresholds
- Legal foundations of AI contracts
- Data handling obligations
- Audit readiness requirements
- Common failure patterns in early-stage assessments
Module 2. Regulatory Landscape and Compliance Alignment
Navigate global compliance expectations impacting AI vendor selection.
12 chapters in this module
- Mapping AI risk to GDPR and data privacy laws
- HIPAA implications for health-tech vendors
- SOX and financial reporting controls
- Sector-specific regulatory bodies and mandates
- Cross-border data transfer risks
- AI and algorithmic transparency laws
- Emerging standards from NIST and ISO
- Compliance by design in vendor contracts
- Documentation requirements for audits
- Handling regulatory change cycles
- Jurisdictional risk scoring
- Vendor compliance self-assessment pitfalls
Module 3. Technical Due Diligence for AI Systems
Evaluate AI vendor architecture, model provenance, and system integrity.
12 chapters in this module
- AI model lifecycle overview
- Model documentation standards
- Bias detection and mitigation approaches
- Explainability requirements by use case
- Model validation techniques
- Red teaming AI systems
- Infrastructure security posture
- API security and access controls
- Model drift and monitoring protocols
- Third-party dependency risks
- Software bill of materials (SBOM) for AI
- Incident response readiness
Module 4. Data Governance and Privacy Risk
Assess how vendors handle sensitive data across jurisdictions.
12 chapters in this module
- Data classification frameworks
- Consent management practices
- De-identification and anonymization standards
- Data retention and deletion policies
- Cross-border data flow mapping
- Subprocessor transparency
- Data subject rights fulfillment
- Privacy impact assessment integration
- Vendor data breach response timelines
- Data ownership clauses
- Encryption in transit and at rest
- Audit logging and access trails
Module 5. Contractual Risk Allocation and SLAs
Structure agreements that enforce accountability and performance.
12 chapters in this module
- Key AI-specific contract clauses
- Liability for model errors and harm
- Performance guarantees and benchmarks
- Service level agreements for AI uptime
- Model retraining obligations
- Penalties for non-compliance
- Termination rights and exit planning
- IP ownership and licensing terms
- Right to audit provisions
- Change control processes
- Force majeure and AI-specific disruptions
- Dispute resolution mechanisms
Module 6. Security Posture and Cyber Resilience
Evaluate vendor cybersecurity maturity and threat response.
12 chapters in this module
- Security certification benchmarks
- Penetration testing evidence review
- Vulnerability disclosure policies
- Zero-trust architecture alignment
- Identity and access management
- Threat modeling for AI systems
- Incident response playbooks
- Ransomware resilience
- Supply chain attack vectors
- Security audit frequency
- SOC 2 and ISO 27001 alignment
- Red team exercise reporting
Module 7. Operational Resilience and Business Continuity
Ensure AI vendors support uninterrupted operations.
12 chapters in this module
- Disaster recovery planning
- Failover and redundancy design
- Uptime tracking and reporting
- Capacity planning for AI workloads
- Vendor change management
- Criticality tiering for AI services
- Dependency mapping
- Crisis communication protocols
- Third-party escalation paths
- Geographic redundancy
- Load testing evidence
- Recovery time objectives
Module 8. Ethical AI and Bias Mitigation Frameworks
Incorporate fairness, accountability, and transparency into vendor assessment.
12 chapters in this module
- Ethical AI principles by sector
- Bias detection in training data
- Fairness metrics and thresholds
- Human-in-the-loop requirements
- Transparency reporting
- Stakeholder feedback loops
- Model card and datasheet review
- Ethics board oversight
- Bias remediation timelines
- Impact assessment for high-risk use cases
- Community engagement practices
- Ethical audit trails
Module 9. Vendor Financial and Operational Stability
Assess long-term viability of AI vendors.
12 chapters in this module
- Financial health indicators
- Funding stage implications
- Customer concentration risk
- Leadership team stability
- Go-to-market sustainability
- Revenue diversification
- Burn rate and runway
- Mergers and acquisition exposure
- Insurance coverage review
- Third-party dependency risks
- Exit strategy implications
- Vendor lock-in mitigation
Module 10. Implementation Playbook: Assessment Workflows
Deploy standardized workflows for consistent vendor evaluation.
12 chapters in this module
- Assessment intake process
- Stakeholder alignment templates
- Risk scoring rubrics
- Evidence collection checklists
- Cross-functional review meetings
- Risk tiering by impact
- Escalation protocols
- Approval workflows
- Documentation standards
- Version control for assessments
- Audit trail maintenance
- Continuous monitoring setup
Module 11. Implementation Playbook: Integration and Monitoring
Operationalize vendor risk oversight post-contract.
12 chapters in this module
- Onboarding risk controls
- Key performance indicator tracking
- Model performance monitoring
- Compliance recertification cycles
- Change notification requirements
- Incident reporting timelines
- Quarterly business reviews
- Risk register updates
- Remediation tracking
- Exit planning triggers
- Vendor offboarding checklist
- Lessons learned integration
Module 12. Scaling AI Risk Governance Across the Enterprise
Build organization-wide capability for AI vendor oversight.
12 chapters in this module
- Center of excellence models
- Training programs for assessors
- Automation of risk workflows
- Integration with GRC platforms
- Executive reporting dashboards
- Policy standardization
- Cross-departmental alignment
- Vendor risk maturity model
- Benchmarking against peers
- Continuous improvement cycle
- Regulatory horizon scanning
- Future-proofing for AI evolution
How this maps to your situation
- Assessing a high-risk AI vendor for a healthcare deployment
- Onboarding a financial forecasting AI in a SOX-regulated environment
- Evaluating an AI-powered claims processor in insurance
- Scaling AI risk governance across a multi-vendor portfolio
Before vs. after
Before
Uncertain about how to structure AI vendor assessments or align them with compliance mandates
After
Equipped with a full implementation-grade framework to lead AI vendor due diligence confidently
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 complete at their own pace over 6-8 weeks.
If nothing changes
Proceeding without a structured assessment increases exposure to compliance penalties, operational disruption, and reputational harm during audits or incidents.
How this compares to the alternatives
Unlike generic AI ethics courses or compliance overviews, this program delivers implementation-grade workflows, templates, and sector-specific risk criteria used in actual enterprise deployments.
Frequently asked
Who is this course designed for?
Compliance leads, risk officers, AI governance professionals, and technology executives in regulated industries who are accountable for third-party AI vendor oversight.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, upon finishing all modules, you’ll receive a digital credential verifying mastery of enterprise AI vendor risk assessment.
$199 one-time. Approximately 3-4 hours per module, designed for professionals to complete at their own pace over 6-8 weeks..
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