A tailored course, built for your situation
Strategic AI Vendor Risk Assessment for Compliance Officers
Master compliance-grade AI vendor evaluation with implementation-ready 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.
Falling behind on third-party AI oversight despite growing expectations
The situation this course is for
Compliance teams are expected to assess complex AI vendors without clear frameworks, consistent methodology, or practical tools, leading to inconsistent evaluations, audit findings, and reputational exposure.
Who this is for
Compliance officers, risk analysts, and governance leads in mid-to-large organizations adopting AI through third-party vendors.
Who this is not for
Individual contributors not involved in vendor assessment, developers building in-house AI, or teams without formal compliance mandates.
What you walk away with
- Apply a structured methodology to assess AI vendor risk across technical, legal, and operational domains
- Align vendor evaluations with evolving regulatory expectations including data privacy and algorithmic accountability
- Develop audit-ready documentation packages for internal and external review
- Negotiate stronger contract language using proven risk-mitigation clauses
- Lead cross-functional vendor reviews with confidence and clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Establish core concepts, market context, and compliance imperatives shaping AI vendor oversight.
12 chapters in this module
- Defining AI vendor risk in modern compliance
- Market trends driving increased scrutiny
- Regulatory drivers across jurisdictions
- Key differences from traditional IT vendor risk
- The compliance officer’s evolving role
- Stakeholder alignment across legal, security, and procurement
- Common misconceptions about AI risk
- Risk taxonomy for third-party AI systems
- Case study: Global hospitality brand AI rollout
- Vendor lifecycle overview
- Risk-based segmentation of AI vendors
- Setting expectations for internal stakeholders
Module 2. Regulatory Landscape Mapping
Navigate current compliance requirements impacting AI vendor selection and oversight.
12 chapters in this module
- GDPR and automated decision-making
- CCPA and AI-driven personalization
- EU AI Act vendor obligations
- Sector-specific rules in services and hospitality
- Cross-border data transfer implications
- Algorithmic transparency mandates
- Enforcement trends from recent rulings
- Future-looking standards in development
- Mapping controls to regulatory clauses
- Jurisdictional risk hotspots
- Compliance-by-design in vendor contracts
- Documentation for regulatory audits
Module 3. Technical Due Diligence Framework
Evaluate AI vendors using structured technical assessment criteria.
12 chapters in this module
- Understanding model inputs and training data
- Assessing data provenance and bias controls
- Model explainability expectations
- API security and access controls
- Infrastructure resilience and uptime
- Incident response capabilities
- Third-party dependencies and sub-vendors
- Software bill of materials (SBOM) review
- Penetration testing disclosures
- Model versioning and update protocols
- Monitoring for model drift
- Red teaming and adversarial testing
Module 4. Contractual Risk Mitigation
Negotiate enforceable vendor agreements with clear risk boundaries.
12 chapters in this module
- Critical clauses for AI vendor contracts
- Liability for incorrect or harmful outputs
- Indemnification for IP and regulatory violations
- Data ownership and usage rights
- Audit rights and transparency obligations
- Performance guarantees and SLAs
- Termination for ethical concerns
- Subprocessor approval workflows
- Insurance and financial backing requirements
- Dispute resolution mechanisms
- Change control for model updates
- Exit strategy and data portability
Module 5. Vendor Onboarding Process
Implement a standardized intake and assessment workflow.
12 chapters in this module
- Pre-engagement risk screening
- Initial information request design
- Response evaluation rubric
- Stakeholder review coordination
- Risk tiering based on impact
- Escalation paths for high-risk vendors
- Documentation standards
- Integration with procurement systems
- Compliance checkpoint design
- Pilot phase monitoring
- Go/no-go decision framework
- Lessons from failed onboarding
Module 6. Ongoing Monitoring & Reporting
Maintain continuous oversight of active AI vendors.
12 chapters in this module
- Establishing monitoring frequency
- Key risk indicators for AI systems
- Automated alerting and dashboards
- Quarterly compliance check-ins
- Incident reporting expectations
- Model performance tracking
- User feedback collection
- Regulatory change impact analysis
- Audit trail maintenance
- Vendor self-reporting requirements
- Escalation for non-compliance
- Reporting to executive leadership
Module 7. Ethical AI Governance
Embed ethical principles into vendor assessment and oversight.
12 chapters in this module
- Defining ethical AI for your organization
- Bias detection and mitigation expectations
- Fairness across customer segments
- Transparency with end users
- Human oversight requirements
- Use case appropriateness review
- Community impact considerations
- Ethics review board integration
- Whistleblower mechanisms
- Public accountability standards
- Handling controversial applications
- Ethics audit preparation
Module 8. Incident Response Planning
Prepare for and respond to AI-related incidents involving vendors.
12 chapters in this module
- Common AI failure modes
- Vendor incident notification timelines
- Root cause investigation protocols
- Customer impact assessment
- Regulatory reporting obligations
- Public relations coordination
- Internal communication plan
- Legal hold procedures
- Remediation tracking
- Vendor accountability enforcement
- Lessons learned documentation
- Update to future vendor assessments
Module 9. Cross-Functional Alignment
Lead collaboration across legal, security, procurement, and business units.
12 chapters in this module
- Stakeholder mapping and influence
- Building a vendor risk council
- Role clarity across functions
- Consensus-building techniques
- Conflict resolution strategies
- Shared documentation platforms
- Meeting cadence design
- Decision rights framework
- Escalation protocols
- Training for non-compliance teams
- Vendor performance scorecards
- Celebrating risk-aware culture
Module 10. Audit Readiness & Documentation
Prepare for internal and external audits with confidence.
12 chapters in this module
- Required documentation inventory
- Evidence collection workflow
- Version control for assessments
- Internal audit coordination
- External auditor expectations
- Regulatory examination prep
- Document retention policies
- Redaction and confidentiality
- Cross-border audit logistics
- Remediation tracking for findings
- Continuous improvement cycle
- Audit success metrics
Module 11. Scaling Vendor Risk Programs
Expand oversight from pilot vendors to enterprise-wide programs.
12 chapters in this module
- Centralized vs decentralized models
- Team structure and resourcing
- Technology platform selection
- Standardized assessment templates
- Automation opportunities
- Training for regional teams
- Global consistency with local adaptation
- Metrics for program maturity
- Budget justification and ROI
- Continuous improvement roadmap
- Benchmarking against peers
- Future-state vision
Module 12. Future-Proofing AI Vendor Strategy
Anticipate next-generation risks and opportunities in AI sourcing.
12 chapters in this module
- Emerging AI capabilities on horizon
- Regulatory trends in development
- New risk vectors from generative AI
- Supply chain complexity growth
- Open source AI vendor models
- Consolidation and vendor stability
- Geopolitical risk in AI sourcing
- Sustainability considerations
- Workforce impact forecasting
- Responsible innovation frameworks
- Strategic vendor partnerships
- Long-term compliance roadmap
How this maps to your situation
- You're evaluating your first AI vendor and need a structured approach
- You're scaling AI adoption and need consistent oversight
- You're preparing for regulatory scrutiny on third-party AI
- You're building a centralized vendor risk function
Before vs. after
Before
Uncertain, reactive, and inconsistent in AI vendor evaluations
After
Confident, systematic, and audit-ready in third-party AI risk oversight
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 hours per module, designed for flexible, self-paced learning with immediate applicability.
If nothing changes
Without a structured approach, organizations face inconsistent evaluations, regulatory findings, reputational damage, and operational disruptions from poorly managed AI vendors.
How this compares to the alternatives
Unlike generic compliance courses or academic AI ethics programs, this course delivers implementation-grade frameworks specifically for assessing third-party AI vendors, combining regulatory insight, technical depth, and practical tooling in one focused offering.
Frequently asked
Who is this course designed for?
Compliance officers, risk analysts, and governance professionals responsible for evaluating or overseeing third-party AI vendors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical?
It balances technical depth with compliance practicality, designed for non-engineers to confidently assess vendor systems without needing to code.
$199 one-time. Approximately 3 hours per module, designed for flexible, self-paced learning with immediate applicability..
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