A tailored course, built for your situation
Scalable AI Vendor Risk Assessment for Audit Teams
Implementation-grade frameworks for audit leaders advancing AI 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.
Manual, inconsistent AI vendor reviews slow down procurement and weaken audit defensibility
The situation this course is for
Audit teams are increasingly asked to assess AI vendors without standardized frameworks. This leads to ad-hoc evaluations, inconsistent risk scoring, and difficulty scaling due diligence across growing portfolios. The lack of structured methodology creates friction with procurement and exposes organizations to compliance drift.
Who this is for
Audit, compliance, and governance professionals in financial services and asset management leading AI vendor due diligence
Who this is not for
Individuals seeking introductory AI literacy or technical model development skills
What you walk away with
- Apply a repeatable, auditable framework for AI vendor risk assessment
- Evaluate AI vendors across model transparency, data governance, and compliance readiness
- Integrate risk scoring into procurement workflows for faster decision cycles
- Produce defensible audit documentation aligned with evolving regulatory expectations
- Scale assessments across vendor portfolios using templated evaluation playbooks
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Define scope, risk domains, and governance alignment for AI vendor assessments
12 chapters in this module
- Defining AI vendor risk in regulated environments
- Mapping risk to compliance frameworks
- Stakeholder roles in vendor evaluation
- Differentiating AI from traditional software risk
- Establishing governance boundaries
- Risk taxonomy for AI systems
- Vendor lifecycle stages
- Regulatory drivers shaping assessment criteria
- Benchmarking organizational maturity
- Common pitfalls in early-stage evaluations
- Aligning with internal audit standards
- Setting success metrics for due diligence
Module 2. AI Procurement Landscape
Understand vendor ecosystem dynamics and procurement integration points
12 chapters in this module
- Types of AI vendors and deployment models
- Procurement cycle touchpoints
- Vendor claims vs. implementation reality
- Evaluating solution fit for purpose
- Commercial terms and risk exposure
- SLAs and performance guarantees
- Data ownership and portability clauses
- Exit strategy considerations
- Multi-vendor integration risks
- Third-party dependency mapping
- Open-source components in vendor offerings
- Supply chain transparency expectations
Module 3. Model Transparency and Explainability
Assess model interpretability and documentation completeness
12 chapters in this module
- Defining explainability for audit purposes
- Model cards and technical documentation review
- Algorithmic bias assessment criteria
- Performance metrics validation
- Training data provenance checks
- Feature importance reporting
- Counterfactual reasoning in models
- Human-in-the-loop design patterns
- Model drift detection mechanisms
- Uncertainty quantification review
- Post-hoc explanation tools evaluation
- Documentation completeness scoring
Module 4. Data Governance and Lineage
Evaluate data sourcing, handling, and audit trail integrity
12 chapters in this module
- Data provenance and collection methods
- Consent and licensing verification
- PII handling and anonymization techniques
- Data retention and deletion policies
- Cross-border data flow compliance
- Data quality assurance processes
- Versioning and lineage tracking
- Training vs. inference data separation
- Data poisoning risk mitigation
- Audit trail completeness for data operations
- Data access logging standards
- Vendor data subprocessing oversight
Module 5. Compliance and Regulatory Alignment
Map vendor practices to current and emerging regulatory expectations
12 chapters in this module
- GDPR and AI processing requirements
- APRA CPS 234 implications for AI
- ASIC regulatory guidance on automated systems
- Model risk management expectations
- Responsible AI principles alignment
- Bias and fairness audit requirements
- Consumer protection considerations
- Recordkeeping obligations
- Third-party oversight rules
- Regulatory reporting readiness
- Auditability of decision logic
- Escalation pathways for non-compliance
Module 6. Security and System Integrity
Evaluate cybersecurity practices and system resilience
12 chapters in this module
- Secure development lifecycle review
- Penetration testing evidence review
- Model inversion attack resistance
- Adversarial robustness testing
- API security and authentication
- Infrastructure hardening standards
- Incident response readiness
- Zero-day vulnerability management
- Access control and role segregation
- Model update validation processes
- Supply chain attack surface analysis
- Cyber resilience documentation review
Module 7. Operational Resilience and Monitoring
Assess ongoing performance tracking and failure response
12 chapters in this module
- Model performance monitoring design
- Drift detection and retraining triggers
- Fallback mechanism adequacy
- Uptime and availability guarantees
- Error logging and root cause analysis
- Human override capabilities
- Performance degradation thresholds
- Vendor incident communication protocols
- Service continuity planning
- Disaster recovery readiness
- Monitoring tool integration
- Alerting and escalation workflows
Module 8. Ethical AI and Bias Mitigation
Evaluate fairness, accountability, and societal impact safeguards
12 chapters in this module
- Bias detection across demographic groups
- Fairness metric selection and thresholds
- Representativeness of training data
- Disparate impact testing methods
- Ethics review board involvement
- Stakeholder feedback mechanisms
- Redress pathways for affected parties
- Societal impact assessment
- Transparency in decision outcomes
- Bias mitigation technique effectiveness
- Ongoing fairness monitoring
- Public trust considerations
Module 9. Audit Trail and Documentation Standards
Ensure complete, verifiable records for audit defense
12 chapters in this module
- Model version tracking requirements
- Change logging for model updates
- Decision audit trail completeness
- Data input and output logging
- User interaction recording
- Access logging for model queries
- Immutable recordkeeping approaches
- Timestamp accuracy verification
- Chain of custody documentation
- Regulatory inspection readiness
- Third-party audit access provisions
- Documentation retention periods
Module 10. Vendor Due Diligence Workflows
Implement scalable, repeatable assessment processes
12 chapters in this module
- Standardized questionnaire design
- Risk-based tiering of vendors
- Automated screening tools integration
- Cross-functional review coordination
- Evidence collection protocols
- Scoring rubric development
- Risk exception management
- Approval workflow design
- Continuous monitoring setup
- Remediation tracking
- Due diligence reporting
- Audit readiness preparation
Module 11. Integration with Enterprise Risk Frameworks
Align AI vendor risk with broader organizational risk management
12 chapters in this module
- Enterprise risk taxonomy mapping
- Risk appetite alignment
- Key risk indicator development
- Board reporting integration
- Risk register updates
- Third-party risk program alignment
- Internal audit coordination
- Risk mitigation validation
- Escalation thresholds
- Risk treatment options
- Oversight committee reporting
- Risk culture considerations
Module 12. Scaling Assessment Across Portfolios
Extend frameworks to manage multiple vendors efficiently
12 chapters in this module
- Centralized assessment repository design
- Automated risk scoring engines
- Vendor performance benchmarking
- Consolidated reporting dashboards
- Resource allocation for due diligence
- Tiered assessment depth strategies
- Vendor risk heat mapping
- Continuous monitoring automation
- Third-party audit reliance strategies
- Knowledge transfer protocols
- Lessons learned integration
- Maturity progression roadmap
How this maps to your situation
- Onboarding new AI vendors
- Reassessing existing vendor contracts
- Preparing for regulatory audit
- Scaling due diligence across growing portfolios
Before vs. after
Before
Reactive, inconsistent AI vendor evaluations with limited audit defensibility
After
Proactive, standardized assessments producing defensible, scalable risk decisions
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 integration into existing workflows.
If nothing changes
Continuing with ad-hoc evaluations increases compliance exposure and slows procurement, limiting your organization's ability to adopt AI responsibly at scale.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk overviews, this program delivers implementation-grade frameworks specifically for audit teams managing vendor due diligence in regulated environments.
Frequently asked
Who is this course designed for?
Audit, compliance, and governance professionals leading AI vendor risk assessments in regulated industries.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical AI development knowledge required?
No. The course is designed for audit and governance professionals, not data scientists.
$199 one-time. Approximately 3-4 hours per module, designed for integration into existing workflows..
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