A tailored course, built for your situation
Production-Grade AI Vendor Risk Assessment for Compliance Officers
Master vendor risk in the age of enterprise AI with implementation-grade 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.
Compliance teams are expected to govern AI vendor relationships but lack standardized, field-tested assessment frameworks.
The situation this course is for
AI adoption is outpacing compliance readiness. Vendor risk processes built for legacy software don’t translate to AI’s dynamic, data-driven, and often opaque systems. Compliance officers need new tools to verify model integrity, assess data provenance, and enforce contractual accountability, all while operating under increased scrutiny.
Who this is for
Compliance Officers, Risk Managers, and Governance Leads in mid-to-large organizations adopting third-party AI solutions.
Who this is not for
Individuals seeking introductory AI awareness or general cybersecurity hygiene. This is not for technical data scientists building models in-house.
What you walk away with
- Deploy a standardized AI vendor assessment framework aligned with compliance mandates
- Evaluate model risk across accuracy, bias, data lineage, and regulatory alignment
- Negotiate AI vendor contracts with enforceable SLAs and audit rights
- Lead cross-functional AI governance committees with authority
- Produce audit-ready documentation for regulators and internal stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Define risk dimensions unique to AI vendors and map them to compliance domains.
12 chapters in this module
- Defining AI vendor risk
- Compliance vs. operational risk
- Regulatory landscape overview
- AI lifecycle stages
- Third-party dependency mapping
- Vendor ecosystem typology
- Risk escalation triggers
- Compliance ownership models
- Stakeholder alignment
- Governance integration
- Audit trail requirements
- Baseline assessment design
Module 2. Due Diligence Frameworks
Structure pre-contract evaluations for AI vendors using compliance-first checklists.
12 chapters in this module
- Pre-engagement scoping
- Vendor documentation requests
- Model transparency assessment
- Data provenance verification
- Security posture review
- Compliance certification mapping
- Third-party audit rights
- Reference validation
- Financial stability checks
- Incident response readiness
- Change management protocols
- Exit strategy planning
Module 3. Contractual Risk Mitigation
Embed enforceable compliance terms into AI vendor agreements.
12 chapters in this module
- Performance SLA definition
- Bias and fairness commitments
- Model retraining obligations
- Audit access clauses
- Data ownership terms
- IP rights and usage limits
- Subprocessor disclosure
- Breach notification timelines
- Liability thresholds
- Termination for noncompliance
- Compliance certification updates
- Dispute resolution frameworks
Module 4. Model Risk Assessment
Evaluate AI model integrity through technical and compliance lenses.
12 chapters in this module
- Model validation principles
- Accuracy benchmarking
- Bias detection methods
- Explainability requirements
- Drift monitoring setup
- Ground truth data review
- Version control audit
- Human-in-the-loop design
- Error impact classification
- Fallback mechanism review
- Red team testing scope
- Model documentation standards
Module 5. Data Governance Alignment
Ensure AI vendor data practices meet enterprise data compliance standards.
12 chapters in this module
- Data lineage mapping
- Consent management verification
- Cross-border data flow checks
- Data minimization compliance
- Retention policy alignment
- Anonymization effectiveness
- Subject access request handling
- Data breach response coordination
- Processor vs. controller status
- Data protection impact assessments
- Vendor subprocessing oversight
- Audit log accessibility
Module 6. Compliance Integration
Align AI vendor risk practices with existing compliance programs.
12 chapters in this module
- Mapping to SOX controls
- GDPR alignment
- CCPA/CPRA integration
- HIPAA considerations
- Industry-specific mandates
- Internal audit coordination
- Regulatory reporting integration
- Policy documentation updates
- Training for compliance staff
- Escalation procedures
- Compliance dashboard design
- Continuous monitoring rules
Module 7. Ongoing Monitoring
Implement continuous risk assessment for AI vendor performance and compliance.
12 chapters in this module
- Performance KPI tracking
- Automated alert systems
- Quarterly compliance reviews
- Model drift detection
- Incident response testing
- Vendor change notifications
- Audit trail analysis
- Stakeholder reporting cycles
- Compliance exception tracking
- Remediation workflows
- Escalation protocols
- Vendor scorecarding
Module 8. Incident Response Planning
Prepare for AI-specific incidents with structured response protocols.
12 chapters in this module
- AI failure classification
- Model bias incident response
- Data leakage scenarios
- Reputational risk protocols
- Vendor notification requirements
- Internal escalation paths
- Regulatory disclosure rules
- Public statement coordination
- Forensic investigation steps
- Remediation validation
- Post-incident audit
- Lessons learned documentation
Module 9. Audit Readiness
Generate regulator-ready documentation for AI vendor risk programs.
12 chapters in this module
- Audit package structure
- Evidence collection protocols
- Compliance assertion writing
- Vendor documentation requests
- Internal control testing
- Gap remediation tracking
- Regulator Q&A preparation
- Third-party attestation collection
- Policy alignment statements
- Risk register updates
- Audit communication strategy
- Follow-up action planning
Module 10. Cross-Functional Leadership
Lead AI vendor risk initiatives across legal, IT, security, and business units.
12 chapters in this module
- Stakeholder identification
- Governance committee setup
- RACI matrix design
- Communication protocols
- Decision rights framework
- Conflict resolution models
- Executive reporting templates
- Budget alignment
- Resource planning
- Change management
- Training rollout
- Success metric definition
Module 11. Emerging Standards Alignment
Stay ahead of evolving AI governance standards from NIST, ISO, and sector regulators.
12 chapters in this module
- NIST AI RMF integration
- ISO 42001 alignment
- EU AI Act implications
- Sector-specific guidance
- Regulatory sandbox participation
- Standards mapping exercises
- Compliance gap analysis
- Future-proofing strategies
- Vendor conformance claims
- Certification pathways
- Public reporting expectations
- Global harmonization efforts
Module 12. Program Maturity and Scaling
Evolve from ad hoc assessments to enterprise-wide AI vendor risk management.
12 chapters in this module
- Maturity model application
- Centralized oversight models
- Automation opportunities
- Vendor risk platform evaluation
- Training program development
- Lessons learned integration
- Benchmarking against peers
- Executive sponsorship
- Continuous improvement cycle
- Resource scaling
- Technology enablement
- Strategic roadmap creation
How this maps to your situation
- Compliance teams adopting third-party AI models
- Organizations facing regulatory scrutiny on AI use
- Enterprises scaling AI deployments across business units
- Risk officers building AI-specific governance frameworks
Before vs. after
Before
Unstructured, reactive evaluations of AI vendors with limited compliance leverage.
After
A repeatable, audit-ready AI vendor risk program that strengthens compliance posture and enables faster, safer AI adoption.
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 completion within 90 days.
If nothing changes
Without a formalized approach, compliance teams face increased exposure to regulatory penalties, reputational incidents, and operational failures stemming from opaque AI vendor practices.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk overviews, this program delivers implementation-grade frameworks specifically for compliance officers managing third-party AI risk, complete with enforceable contract terms, audit-ready documentation, and operational playbooks.
Frequently asked
Who is this course designed for?
Compliance Officers, Risk Managers, and Governance Leads responsible for overseeing third-party AI vendor relationships in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical AI knowledge required?
No. The course is designed for compliance professionals and focuses on governance, risk, and audit, not model building or data science.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced completion within 90 days..
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