A tailored course, built for your situation
Audit-Tested AI Vendor Risk Assessment for Established Enterprises
Master enterprise-grade AI risk validation 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.
Deploying AI vendors without audit-ready validation creates invisible exposure in procurement and compliance cycles
The situation this course is for
Teams adopt AI-powered vendors rapidly, but internal audit and compliance functions increasingly flag gaps in due diligence. Without a standardized, evidence-backed assessment method, risk leaders face rework, delays, and scrutiny during financial and regulatory reviews. The cost isn't just financial, it's credibility at the leadership table.
Who this is for
Mid-to-senior risk, compliance, or technology governance professionals in established enterprises adopting third-party AI solutions
Who this is not for
Startups using off-the-shelf AI tools, individual contributors without vendor oversight responsibility, or teams focused only on model development
What you walk away with
- Apply a repeatable framework to assess AI vendor risk across technical, legal, and operational domains
- Align assessments with internal audit expectations and regulatory scrutiny
- Reduce review cycle time by 40% with evidence-structured documentation
- Lead cross-functional vendor evaluations with confidence and clarity
- Position risk function as an enabler, not a bottleneck
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Enterprise Contexts
Establish core definitions, scope boundaries, and governance alignment for AI vendor risk programs.
12 chapters in this module
- Defining AI vendor risk in enterprise environments
- Regulatory drivers shaping current expectations
- Distinguishing AI risk from traditional software procurement
- Governance models for cross-functional oversight
- Roles and responsibilities in vendor assessment
- Integrating with existing risk frameworks (e.g., NIST, ISO)
- Stakeholder alignment across legal, IT, and security
- Procurement lifecycle touchpoints
- Risk tiering for AI vendors
- Common pitfalls in early-stage assessments
- Building executive sponsorship
- Case study: Global bank onboarding AI fraud detection
Module 2. Audit Readiness Principles for AI Systems
Prepare assessments to withstand internal and external audit scrutiny.
12 chapters in this module
- What auditors look for in AI vendor reviews
- Evidence standards for compliance documentation
- Traceability from control to policy to implementation
- Version control and audit trails for assessments
- Third-party validation expectations
- Document retention and storage policies
- Preparing for surprise audits
- Responding to auditor findings
- Leveraging audit feedback for program improvement
- Aligning with SOC 2 and ISO 27001 requirements
- Working with external counsel during audits
- Case study: Tech firm passing AI vendor audit with zero findings
Module 3. Vendor Onboarding Risk Triage
Implement a scalable triage process for incoming AI vendors.
12 chapters in this module
- Initial vendor classification framework
- High-risk vs. low-risk AI service indicators
- Automated pre-screening questionnaires
- Human-in-the-loop review triggers
- Data handling red flags
- Model transparency expectations
- API security and access controls
- Subprocessor disclosures
- Jurisdictional compliance concerns
- Incident response readiness
- Fallback and exit strategy review
- Case study: Healthcare provider assessing diagnostic AI vendor
Module 4. Technical Due Diligence for AI Vendors
Evaluate the underlying technology stack and model integrity.
12 chapters in this module
- Model development lifecycle review
- Training data provenance and bias mitigation
- Model performance metrics and benchmarks
- Explainability and interpretability standards
- Adversarial robustness testing
- Model monitoring in production
- Versioning and retraining processes
- API security and rate limiting
- Encryption in transit and at rest
- Access control and role-based permissions
- Incident logging and alerting
- Case study: Financial services firm evaluating credit scoring AI
Module 5. Legal and Contractual Risk Validation
Ensure vendor contracts align with organizational risk thresholds.
12 chapters in this module
- Key clauses for AI vendor contracts
- Liability for model errors or bias
- IP ownership of models and outputs
- Data licensing and usage rights
- Audit rights and access to logs
- Right to exit and data portability
- Indemnification for regulatory penalties
- Subprocessor change notifications
- Jurisdiction and dispute resolution
- GDPR and CCPA compliance commitments
- Model drift and performance guarantees
- Case study: Retailer renegotiating AI personalization contract
Module 6. Operational Resilience and Business Continuity
Assess vendor reliability and continuity planning.
12 chapters in this module
- Uptime SLAs and reporting transparency
- Disaster recovery and failover plans
- Vendor financial stability indicators
- Single points of failure in architecture
- Redundancy and geographic distribution
- Change management processes
- Incident response timelines
- Communication protocols during outages
- Business continuity testing results
- Third-party dependency mapping
- Exit strategy and data recovery
- Case study: Logistics company preparing for AI routing failure
Module 7. Ethical AI and Bias Mitigation Oversight
Evaluate vendor approaches to fairness, accountability, and transparency.
12 chapters in this module
- Defining ethical AI in enterprise context
- Bias detection in training and inference
- Fairness metrics and reporting
- Demographic parity and equal opportunity
- Human oversight mechanisms
- Model explainability for non-technical stakeholders
- Bias remediation processes
- Stakeholder feedback loops
- Ethics review board involvement
- Transparency in marketing claims
- Ongoing monitoring for drift
- Case study: Insurer reviewing AI underwriting tool
Module 8. Data Privacy and Security Alignment
Validate vendor data handling against enterprise privacy policies.
12 chapters in this module
- Data classification and handling policies
- Anonymization and pseudonymization techniques
- Consent management integration
- Data minimization compliance
- Cross-border data transfer mechanisms
- Penetration testing results review
- SOC 2 and ISO 27001 report analysis
- Security certification validity
- Employee access controls
- Breach notification timelines
- Data retention and deletion policies
- Case study: University assessing AI tutoring platform
Module 9. Cross-Functional Assessment Workflows
Orchestrate reviews across legal, IT, security, and business units.
12 chapters in this module
- Designing assessment workflows
- RACI matrix for vendor review
- Centralized vendor risk repository setup
- Automated escalation paths
- Meeting cadence and decision gates
- Dispute resolution framework
- Executive summary creation
- Feedback loops for process improvement
- Training for reviewers
- Metrics for program effectiveness
- Integration with GRC platforms
- Case study: Manufacturer streamlining AI vendor reviews
Module 10. Evidence Packaging for Audit Defense
Assemble defensible documentation packages for auditors.
12 chapters in this module
- Standardized evidence naming conventions
- Version-controlled assessment records
- Cross-referencing policy to control
- Appendix structure for technical details
- Executive summary templates
- Highlighting areas of strength
- Disclosing and mitigating weaknesses
- Third-party validation integration
- Change logs and update history
- Stakeholder sign-off documentation
- Retention and archiving strategy
- Case study: Passing unannounced regulator audit
Module 11. Continuous Monitoring and Reassessment
Establish ongoing oversight beyond initial onboarding.
12 chapters in this module
- Triggers for reassessment
- Model drift detection thresholds
- Ongoing performance monitoring
- Vendor update notification systems
- Subprocessor change alerts
- Regulatory change impact analysis
- Automated risk score updates
- Quarterly review cadence
- Incident-driven reassessment
- Stakeholder feedback integration
- Exit readiness monitoring
- Case study: SaaS provider updating AI search vendor
Module 12. Scaling AI Vendor Risk Across the Enterprise
Expand the program to handle growing AI vendor adoption.
12 chapters in this module
- Centralized vs. decentralized models
- Training regional teams
- Global compliance harmonization
- Automation opportunities
- Integration with procurement systems
- Vendor risk scorecards
- Executive reporting dashboards
- Budgeting for risk operations
- Hiring and skill development
- Third-party assessment firms
- Benchmarking against peers
- Case study: Global enterprise scaling AI risk program
How this maps to your situation
- Onboarding a new AI vendor with upcoming audit
- Expanding AI use across departments with compliance concerns
- Responding to auditor findings on AI vendor oversight
- Building a centralized AI risk function
Before vs. after
Before
Uncertainty in vendor risk assessment, reactive responses to audit findings, fragmented cross-team collaboration
After
Structured, audit-ready vendor evaluations with clear documentation, stakeholder alignment, and proactive risk mitigation
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 asynchronous learning with practical application between sections.
If nothing changes
Without a standardized approach, organizations face increased audit findings, vendor-related incidents, and erosion of trust in risk and compliance functions during leadership reviews.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance webinars, this program delivers implementation-grade workflows, audit-tested documentation standards, and enterprise-specific risk triage frameworks not available in public resources or vendor training.
Frequently asked
Who is this course designed for?
Risk, compliance, and technology governance professionals in established enterprises managing third-party AI vendor adoption.
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 the course doesn't meet expectations.
$199 one-time. Approximately 3-4 hours per module, designed for asynchronous learning with practical application between sections..
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