A tailored course, built for your situation
Practical AI Vendor Risk Assessment for Compliance Officers
A 12-module implementation-grade course for compliance and risk professionals navigating AI vendor ecosystems
$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 contracts are moving faster than compliance frameworks can track
The situation this course is for
Compliance officers face increasing pressure to assess AI vendors without clear frameworks, consistent terminology, or proven due diligence workflows. The gap between procurement speed and governance rigor creates implementation delays and inconsistent risk posture decisions.
Who this is for
Compliance officers, risk assessors, and governance leads in mid-to-large organizations adopting AI-powered vendor solutions
Who this is not for
Individuals seeking introductory AI literacy or general cybersecurity training without a compliance lens
What you walk away with
- Apply a structured framework to assess AI vendor risk across technical, legal, and operational domains
- Map vendor AI capabilities to compliance control requirements
- Conduct due diligence using tailored checklists and scoring models
- Draft contract language that enforces accountability and transparency
- Lead cross-functional AI vendor assessments with confidence
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduces core concepts of AI vendor ecosystems and their compliance implications
12 chapters in this module
- Defining AI vendors and service categories
- Compliance landscape for third-party AI
- Regulatory drivers shaping vendor risk
- Governance convergence with procurement
- Risk taxonomy for AI services
- Organizational accountability models
- Vendor lifecycle stages
- Due diligence triggers
- Compliance ownership models
- Risk tolerance frameworks
- Audit trail requirements
- Baseline assessment design
Module 2. AI Risk Classification Frameworks
Build classification models to categorize AI vendor risk levels
12 chapters in this module
- High-risk vs. limited-risk AI systems
- Sector-specific risk benchmarks
- Model transparency requirements
- Data dependency mapping
- Explainability thresholds
- Bias and fairness considerations
- Human oversight requirements
- Impact scoring models
- Risk tiering methodology
- Dynamic reclassification workflows
- Third-party validation needs
- Risk register integration
Module 3. Due Diligence Workflows
Design and execute vendor assessment processes
12 chapters in this module
- Pre-assessment scoping
- Request for information design
- Vendor self-reporting validation
- Document verification protocols
- Interview frameworks for technical teams
- Compliance evidence collection
- Gap analysis techniques
- Risk escalation paths
- Stakeholder alignment strategies
- Assessment timeline planning
- Resource allocation models
- Post-assessment reporting
Module 4. Control Mapping and Alignment
Align vendor offerings with internal compliance controls
12 chapters in this module
- Internal control inventory
- Control gap identification
- AI-specific control requirements
- Mapping vendor capabilities to controls
- Compensating control design
- Control effectiveness testing
- Audit readiness preparation
- Third-party attestation review
- SOC 2 and ISO alignment
- Continuous monitoring integration
- Control ownership assignment
- Evidence collection workflows
Module 5. Contractual Risk Mitigation
Draft enforceable agreements with AI vendors
12 chapters in this module
- Risk-based contract clauses
- Data handling requirements
- Audit rights and access
- Liability and indemnity terms
- Performance guarantees
- Transparency obligations
- Model update notifications
- Incident response expectations
- Termination triggers
- Subcontractor oversight
- Jurisdictional compliance
- Renewal and exit planning
Module 6. Data Governance and Privacy
Ensure AI vendors meet data protection standards
12 chapters in this module
- Data classification levels
- Processing agreement requirements
- Cross-border data flows
- Anonymization and pseudonymization
- Consent management integration
- Data minimization enforcement
- Retention and deletion protocols
- Breach notification timelines
- Data subject rights support
- Processor vs. controller roles
- Data protection impact assessments
- Vendor data incident response
Module 7. Model Transparency and Explainability
Evaluate AI model behavior and decision logic
12 chapters in this module
- Model documentation standards
- Input and output transparency
- Explainability techniques
- Feature importance analysis
- Model drift detection
- Confidence threshold reporting
- Decision audit trails
- Human-in-the-loop design
- Bias testing requirements
- Model validation frequency
- Third-party model audits
- Model card integration
Module 8. Security and Resilience Assessment
Evaluate technical safeguards and system reliability
12 chapters in this module
- Infrastructure security posture
- API security design
- Penetration testing expectations
- Incident response planning
- Disaster recovery readiness
- Model integrity checks
- Adversarial attack resistance
- Authentication and access controls
- Encryption standards
- Monitoring and alerting
- Threat modeling integration
- Vendor security certification review
Module 9. Ethical AI and Fairness
Assess vendor alignment with ethical AI principles
12 chapters in this module
- Fairness metrics definition
- Bias detection methodologies
- Diversity in training data
- Stakeholder impact assessment
- Ethics board engagement
- Redress mechanisms
- Community impact considerations
- Transparency in AI use
- Human dignity safeguards
- Equity in outcomes
- Ethical AI certifications
- Ongoing monitoring frameworks
Module 10. Cross-Functional Collaboration
Lead assessments with legal, IT, and business teams
12 chapters in this module
- Stakeholder identification
- Governance committee design
- Role clarity in assessments
- Communication protocols
- Conflict resolution strategies
- Decision escalation paths
- Shared documentation platforms
- Alignment with procurement
- Legal review coordination
- IT security collaboration
- Business unit engagement
- Post-implementation review
Module 11. Audit and Regulatory Readiness
Prepare for internal and external audits
12 chapters in this module
- Audit trail completeness
- Regulatory reporting alignment
- Evidence packaging
- Internal audit coordination
- External auditor expectations
- Findings response planning
- Compliance dashboard design
- Regulatory change monitoring
- Remediation tracking
- Audit frequency planning
- Stakeholder reporting
- Continuous compliance workflows
Module 12. Ongoing Monitoring and Review
Maintain compliance across vendor lifecycle
12 chapters in this module
- Performance monitoring design
- Risk re-assessment frequency
- Change notification requirements
- Model update impact
- Contractual compliance tracking
- Key risk indicators
- Automated alert systems
- Vendor performance scoring
- Renewal assessment criteria
- Exit strategy readiness
- Lessons learned integration
- Continuous improvement planning
How this maps to your situation
- When onboarding a new AI vendor
- When renewing an existing AI contract
- When responding to regulatory inquiry
- When scaling AI adoption across departments
Before vs. after
Before
Uncertain about how to systematically assess AI vendor risk or align due diligence with compliance frameworks
After
Confidently lead AI vendor assessments using structured workflows, control mapping, and enforceable contractual terms
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, self-paced learning over 8-12 weeks
If nothing changes
Without structured assessment practices, organizations may face inconsistent risk decisions, audit findings, or reputational exposure when AI vendor issues arise
How this compares to the alternatives
Unlike generic compliance training or high-level AI overviews, this course delivers implementation-grade workflows, real-world templates, and actionable frameworks tailored specifically to AI vendor risk assessment for compliance professionals
Frequently asked
Who is this course designed for?
Compliance officers, risk assessors, and governance leads responsible for evaluating AI-powered vendor solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or legal in focus?
It bridges both domains, offering practical frameworks for assessing technical, legal, and operational risk in AI vendor relationships.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning over 8-12 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