A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Compliance Officers
A practitioner's implementation-grade path through emerging AI compliance frameworks and third-party risk governance
$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.
Navigating AI vendor risk without a structured assessment framework leads to inconsistent evaluations and reactive compliance.
The situation this course is for
Compliance officers are increasingly asked to evaluate AI vendors but lack standardized tools or clear benchmarks. This results in fragmented assessments, delayed approvals, and difficulty demonstrating due diligence to internal stakeholders.
Who this is for
Compliance and risk professionals in mid-to-large organizations adopting AI tools and managing third-party technology vendors.
Who this is not for
Those seeking high-level AI awareness content or general cybersecurity training without focus on compliance frameworks and vendor assessment mechanics.
What you walk away with
- Apply a structured framework to assess AI vendor risk across legal, ethical, and operational domains
- Evaluate model transparency, data governance, and compliance readiness using standardized checklists
- Navigate evolving regulations including EU AI Act, NIST AI RMF, and sector-specific guidance
- Build audit-ready documentation for AI vendor due diligence
- Integrate risk assessment workflows into existing vendor management processes
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduce core concepts in AI risk, compliance lifecycle, and third-party oversight distinctions.
12 chapters in this module
- Defining AI vendor risk in modern compliance
- Compliance officer roles in AI governance
- Third-party vs. proprietary AI systems
- Regulatory drivers shaping AI oversight
- Mapping AI risk to existing frameworks
- Stakeholder alignment in vendor assessment
- AI procurement workflow integration
- Risk categorization models
- Vendor classification tiers
- Assessment scoping techniques
- Baseline documentation standards
- Common pitfalls in early-stage evaluations
Module 2. AI Model Transparency and Explainability
Assess vendor claims about model behavior, interpretability, and decision logic.
12 chapters in this module
- Understanding model explainability standards
- Evaluating SHAP, LIME, and other XAI tools
- Vendor documentation expectations
- Model card analysis techniques
- Systemic bias detection methods
- Counterfactual reasoning in AI decisions
- Human-in-the-loop requirements
- Model confidence reporting
- Decision audit trail design
- Performance monitoring thresholds
- Drift detection protocols
- Transparency maturity models
Module 3. Data Governance and Lineage
Verify data provenance, usage rights, and pipeline integrity in AI systems.
12 chapters in this module
- Data sourcing compliance checks
- Training data provenance verification
- PII handling in AI pipelines
- Data versioning standards
- Data lineage documentation
- Consent and licensing validation
- Data quality assurance processes
- Synthetic data use cases
- Data retention policies
- Cross-border data flow compliance
- Data minimization alignment
- Audit readiness for data workflows
Module 4. Legal and Regulatory Alignment
Map vendor practices to current and emerging AI laws and industry standards.
12 chapters in this module
- EU AI Act compliance mapping
- NIST AI RMF integration
- Sector-specific regulations (finance, healthcare)
- Algorithmic accountability laws
- Vendor liability frameworks
- Enforcement trends and penalties
- Certification and audit requirements
- AI ethics board alignment
- Regulatory sandbox participation
- Incident reporting obligations
- Compliance maturity benchmarks
- Global regulatory coordination
Module 5. Security and Model Integrity
Evaluate AI system resilience, adversarial robustness, and deployment security.
12 chapters in this module
- Model poisoning risks
- Adversarial attack vectors
- Input manipulation detection
- Model inversion techniques
- Secure model deployment
- API security for AI services
- Model watermarking
- Model integrity verification
- Zero-day vulnerability response
- Penetration testing AI systems
- Secure update mechanisms
- Threat modeling for AI pipelines
Module 6. Ethical AI and Fairness
Assess fairness, bias mitigation, and ethical design in vendor offerings.
12 chapters in this module
- Bias detection across demographics
- Fairness metric selection
- Disparate impact analysis
- Ethical design principles
- Stakeholder fairness expectations
- Bias mitigation techniques
- Auditability of fairness controls
- Human oversight mechanisms
- Redress processes for harm
- Ethical review board alignment
- Public trust considerations
- Bias reporting transparency
Module 7. Vendor Due Diligence Workflows
Implement structured processes for AI vendor onboarding and ongoing monitoring.
12 chapters in this module
- Questionnaire design for AI vendors
- Evidence collection protocols
- Third-party audit coordination
- Onsite assessment planning
- Remote evaluation techniques
- Continuous monitoring design
- Key risk indicators for AI vendors
- Performance threshold tracking
- Remediation tracking systems
- Escalation pathways
- Offboarding AI vendor processes
- Lessons learned integration
Module 8. Contractual and SLA Alignment
Negotiate enforceable terms covering AI performance, liability, and compliance.
12 chapters in this module
- AI-specific SLA components
- Performance guarantee language
- Liability limitation clauses
- Indemnification for AI harm
- Audit rights negotiation
- Data ownership terms
- Model update commitments
- Service continuity planning
- Exit clause design
- Dispute resolution mechanisms
- Compliance certification terms
- Subcontractor oversight
Module 9. Incident Response and Audit Readiness
Prepare for AI-related incidents and regulatory audits.
12 chapters in this module
- AI incident classification
- Breach notification timelines
- Root cause analysis frameworks
- Regulatory reporting templates
- Internal investigation protocols
- External communications strategy
- Regulatory engagement planning
- Audit trail completeness
- Documentation retention
- Lessons learned implementation
- Recovery validation
- Post-mortem frameworks
Module 10. Cross-Functional Collaboration
Align compliance, legal, IT, and data science teams in AI vendor oversight.
12 chapters in this module
- Stakeholder role mapping
- Communication protocol design
- Joint assessment frameworks
- Conflict resolution pathways
- Decision authority clarity
- Feedback loop integration
- Training alignment
- Tooling interoperability
- Shared documentation systems
- Escalation clarity
- Governance meeting cadence
- Cross-team accountability
Module 11. AI Risk Reporting and Metrics
Develop dashboards and reports for leadership and board-level oversight.
12 chapters in this module
- Key risk indicator design
- AI risk appetite alignment
- Board reporting frameworks
- Executive summary templates
- Risk heat mapping
- Trend analysis techniques
- Benchmarking against peers
- Vendor risk aggregation
- Exposure threshold tracking
- Compliance gap reporting
- Remediation progress tracking
- Risk culture metrics
Module 12. Future-Proofing AI Compliance
Anticipate emerging risks and adapt assessment frameworks accordingly.
12 chapters in this module
- Generative AI risk trends
- Autonomous agent oversight
- AI supply chain complexity
- Open-source model risks
- AI model marketplace compliance
- Decentralized AI governance
- AI insurance considerations
- Regulatory horizon scanning
- Emerging standards integration
- Scenario planning for AI risk
- AI compliance innovation
- Lifelong learning in AI governance
How this maps to your situation
- Onboarding a new AI vendor with complex data usage
- Responding to regulatory inquiry about AI decisioning
- Evaluating a generative AI tool for customer service
- Auditing an existing AI system for compliance drift
Before vs. after
Before
Assessing AI vendors inconsistently, relying on ad-hoc checklists and fragmented stakeholder input.
After
Leading structured, repeatable AI vendor risk assessments with confidence, aligned to evolving standards and internal governance requirements.
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 self-paced learning with practical implementation milestones.
If nothing changes
Without a formalized approach, organizations risk delayed AI adoption, regulatory scrutiny, and reputational exposure from undetected vendor shortcomings.
How this compares to the alternatives
Unlike generic AI awareness courses, this program delivers granular, implementation-focused content tailored to compliance officers managing third-party AI risk , with templates and playbooks not available in open-source or certification-only training.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals responsible for assessing third-party AI vendors and ensuring regulatory alignment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, 30-day money-back guarantee if you find the content doesn't meet your expectations.
$199 one-time. Approximately 3, 4 hours per module, designed for self-paced learning with practical 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