A tailored course, built for your situation
Enterprise-Class AI Vendor Risk Assessment for Compliance Officers
Master the evaluation, governance, and compliance frameworks for AI vendors at scale
$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 are often reactive, inconsistent, or disconnected from compliance mandates, creating friction, rework, and oversight gaps.
The situation this course is for
Compliance officers are increasingly asked to assess AI vendors without structured frameworks or clear benchmarks. The lack of standardized evaluation practices leads to inconsistent outcomes, difficulty justifying decisions to stakeholders, and increased coordination overhead with legal and security teams.
Who this is for
Compliance, risk, and governance professionals in mid-to-senior roles who influence or lead third-party AI vendor assessments and need to apply rigorous, repeatable methods.
Who this is not for
This is not for individual contributors focused only on internal tooling, nor for technical auditors seeking code-level AI model validation.
What you walk away with
- Apply a standardized framework to assess AI vendors across risk domains
- Align vendor evaluations with enterprise compliance and regulatory expectations
- Produce auditable assessment reports with clear risk scoring and mitigation paths
- Lead cross-functional alignment between compliance, legal, security, and procurement
- Deploy a repeatable process that scales across vendor portfolios
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Establish core concepts, risk categories, and the evolving compliance landscape.
12 chapters in this module
- Defining AI vendor risk in enterprise contexts
- Key differences from traditional software risk
- Regulatory drivers shaping AI vendor oversight
- The role of compliance in AI procurement
- Emerging standards and frameworks
- Stakeholder mapping across legal, security, and procurement
- Risk taxonomy for AI systems
- Vendor lifecycle stages and risk touchpoints
- Global considerations in AI vendor assessment
- Ethical and reputational risk dimensions
- Data governance implications
- Baseline expectations for enterprise readiness
Module 2. AI Vendor Due Diligence Framework
Build a structured approach to pre-contract evaluation and scoping.
12 chapters in this module
- Designing a due diligence checklist
- Initial vendor screening criteria
- Assessing AI model transparency
- Evaluating training data provenance
- Vendor documentation requirements
- Third-party audit readiness
- Certifications and attestations to request
- Use case alignment and scope validation
- Identifying red flags in vendor claims
- Engaging technical teams for input
- Risk-based tiering of vendors
- Documenting preliminary findings
Module 3. Compliance Alignment and Regulatory Mapping
Map vendor assessments to relevant compliance obligations.
12 chapters in this module
- GDPR and data protection implications
- Sector-specific regulations (finance, healthcare, etc.)
- AI-specific guidance from regulatory bodies
- Mapping vendor practices to compliance controls
- Handling cross-border data flows
- Consent and lawful basis verification
- Algorithmic accountability requirements
- Recordkeeping and audit trail expectations
- Vendor obligations under privacy laws
- Regulatory reporting linkages
- Compliance exception management
- Maintaining oversight post-implementation
Module 4. Security and Data Protection Evaluation
Assess technical safeguards and data handling practices.
12 chapters in this module
- Data encryption in transit and at rest
- Access controls and identity management
- Model inversion and membership inference risks
- API security and integration risks
- Incident response and breach notification
- Penetration testing and vulnerability disclosure
- Secure development lifecycle practices
- Data minimization and retention policies
- Third-party subprocessing oversight
- Logging and monitoring capabilities
- Zero-trust alignment
- Security certification validation
Module 5. Model Risk and Performance Validation
Evaluate model reliability, fairness, and operational robustness.
12 chapters in this module
- Model accuracy and performance metrics
- Bias detection and fairness testing
- Model drift and retraining protocols
- Explainability and interpretability standards
- Stress testing under edge cases
- Human-in-the-loop requirements
- Fallback mechanisms and fail-safes
- Validation against ground truth data
- Model documentation (model cards, datasheets)
- Third-party model audit support
- Performance benchmarking
- Handling model degradation
Module 6. Contractual and Legal Risk Mitigation
Structure agreements to enforce risk controls and accountability.
12 chapters in this module
- Key clauses for AI vendor contracts
- Liability for model errors or bias
- Indemnification and insurance requirements
- IP ownership and usage rights
- Audit rights and access provisions
- Termination and exit strategies
- Data ownership and portability
- Subcontractor oversight clauses
- Service level agreements for AI systems
- Warranties for model performance
- Dispute resolution mechanisms
- Regulatory change clauses
Module 7. Vendor Governance and Oversight
Establish ongoing monitoring and governance mechanisms.
12 chapters in this module
- Ongoing risk monitoring frameworks
- Key risk indicators for AI vendors
- Regular review cycles and reporting
- Escalation pathways for issues
- Change management for model updates
- Vendor performance dashboards
- Handling model versioning and updates
- Incident response coordination
- Independent validation intervals
- Vendor relationship maturity models
- Centralized vendor inventory management
- Lessons learned and continuous improvement
Module 8. Cross-Functional Alignment and Communication
Coordinate effectively across teams and stakeholders.
12 chapters in this module
- Building a cross-functional assessment team
- Defining roles and responsibilities
- Communication protocols with vendors
- Reporting to executive leadership
- Engaging legal and security teams
- Managing procurement alignment
- Facilitating risk committee reviews
- Documenting decisions and rationale
- Handling conflicting stakeholder priorities
- Creating standardized briefing materials
- Presenting risk findings clearly
- Driving consensus on high-risk vendors
Module 9. Risk Scoring and Decision Frameworks
Develop consistent methods for evaluating and prioritizing risk.
12 chapters in this module
- Designing a risk scoring matrix
- Weighting risk dimensions (compliance, security, model, etc.)
- Thresholds for approval, mitigation, or rejection
- Calibrating scoring across assessors
- Handling edge cases and exceptions
- Visualizing risk exposure
- Scenario analysis for high-impact risks
- Benchmarking against peer organizations
- Revising scoring over time
- Documenting risk rationale
- Presenting scores to governance bodies
- Integrating scores into procurement workflows
Module 10. Implementation Playbook Development
Create organization-specific tools and workflows.
12 chapters in this module
- Customizing the assessment framework
- Building internal templates and checklists
- Integrating with existing GRC platforms
- Training internal assessors
- Setting up review workflows
- Automating data collection where possible
- Developing onboarding materials
- Piloting the process with select vendors
- Gathering feedback and iterating
- Scaling across business units
- Maintaining version control
- Documenting institutional knowledge
Module 11. Stakeholder Engagement and Influence
Lead with authority and build credibility in AI governance.
12 chapters in this module
- Positioning compliance as an enabler
- Communicating risk in business terms
- Building trust with technical teams
- Influencing without direct authority
- Educating stakeholders on AI risks
- Handling pushback on delays or denials
- Demonstrating value of rigorous assessment
- Sharing success stories and wins
- Creating feedback loops with business units
- Developing executive summaries
- Leading training sessions
- Advancing your role in AI governance
Module 12. Future-Proofing and Emerging Trends
Stay ahead of evolving risks and capabilities.
12 chapters in this module
- Anticipating next-generation AI risks
- Generative AI and large language model challenges
- Regulatory horizon scanning
- Advances in model evaluation tools
- AI assurance and certification trends
- Global regulatory divergence
- Supply chain transparency for AI
- Open-source model risks
- AI risk insurance emerging practices
- Board-level AI oversight expectations
- Sustainability and energy use in AI
- Preparing for audits and regulatory inquiries
How this maps to your situation
- Assessing a high-risk AI vendor for the first time
- Designing a standardized evaluation process across teams
- Responding to a regulatory inquiry about vendor practices
- Scaling AI adoption while maintaining compliance
Before vs. after
Before
Unstructured evaluations, inconsistent outcomes, and reactive compliance efforts that slow down innovation.
After
A repeatable, enterprise-grade assessment process that enables faster, safer AI adoption with confidence.
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 steady progress alongside professional responsibilities.
If nothing changes
Without a structured approach, organizations risk inconsistent evaluations, compliance gaps, and delayed AI adoption due to unresolved risk questions.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade detail tailored to the specific challenges of assessing third-party AI vendors in regulated environments.
Frequently asked
Who is this course designed for?
Compliance officers, risk managers, and governance professionals who assess or oversee third-party AI vendors and need a structured, repeatable methodology.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after finishing all modules and assessments.
$199 one-time. Approximately 3-4 hours per module, designed for steady progress alongside professional responsibilities..
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