A tailored course, built for your situation
Compliance-Ready AI Vendor Risk Assessment for Audit Teams
Master implementation-grade risk assessment frameworks for AI vendor oversight in regulated environments.
$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.
Audit teams face increasing pressure to validate AI vendor compliance without clear, standardized frameworks.
The situation this course is for
AI vendors often operate in gray areas between innovation and regulation. Traditional audit practices struggle to assess model transparency, data provenance, and dynamic risk exposure. Without structured, compliance-ready methods, teams risk delays, misalignment with legal standards, or incomplete risk coverage.
Who this is for
Compliance officers, internal auditors, risk managers, and technology governance leads in regulated industries managing third-party AI vendor engagements.
Who this is not for
This is not for data scientists focused solely on model building, or executives seeking high-level AI strategy only. It’s for practitioners who need to implement and validate risk controls in real-world audits.
What you walk away with
- Apply a structured framework to assess AI vendor compliance with regulatory expectations
- Evaluate AI systems for data integrity, bias, and operational resilience
- Map vendor documentation to audit-ready control requirements
- Build repeatable risk-scoring models for ongoing vendor monitoring
- Lead cross-functional validation efforts with legal, IT, and procurement teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Define AI vendor risk in the context of compliance, data governance, and audit readiness.
12 chapters in this module
- Defining AI vendor risk domains
- Regulatory landscape for AI oversight
- Key differences from traditional vendor risk
- Audit team responsibilities in AI procurement
- Stakeholder alignment across legal and IT
- Risk taxonomy for AI systems
- Common vendor claims vs. audit requirements
- Data lifecycle considerations
- Model transparency expectations
- Third-party dependency mapping
- Emerging compliance frameworks
- Building the business case for structured assessment
Module 2. Compliance Frameworks and AI
Align AI vendor evaluation with existing compliance standards.
12 chapters in this module
- Mapping AI risk to ISO 37000 principles
- Integrating NIST AI Risk Management Framework
- GDPR and AI data processing rules
- SOC 2 considerations for AI vendors
- HIPAA implications for health AI
- FINRA and AI in financial services
- Cross-jurisdictional compliance challenges
- Audit trail requirements for AI decisions
- Model documentation standards
- Version control and change logging
- Vendor attestation expectations
- Readiness assessment against compliance baselines
Module 3. Vendor Due Diligence Process
Implement a step-by-step due diligence workflow for AI vendors.
12 chapters in this module
- Pre-engagement risk screening
- Request for information (RFI) design
- Questionnaire structuring for AI systems
- Evaluating vendor SOC reports
- Assessing model validation practices
- Reviewing AI training data sources
- Bias and fairness testing disclosures
- Model drift and retraining policies
- Incident response planning
- Penetration testing access rights
- Sub-processor transparency
- Exit strategy and data portability
Module 4. Control Mapping and Evidence
Translate compliance requirements into actionable audit controls.
12 chapters in this module
- Control framework selection
- Mapping AI risks to control objectives
- Designing testable control procedures
- Evidence types for AI audits
- Automated vs. manual control validation
- Sampling strategies for AI outputs
- Logging and monitoring requirements
- Time-stamped decision records
- Access control reviews
- Model input/output integrity checks
- Change approval workflows
- Control exception handling
Module 5. Risk Scoring Models
Build quantitative models to prioritize AI vendor risk.
12 chapters in this module
- Risk dimension definition
- Weighting compliance, data, and operational factors
- Scoring model transparency
- Threshold setting for escalation
- Dynamic risk scoring over time
- Vendor self-assessment integration
- Audit team override mechanisms
- Benchmarking across vendor portfolio
- Risk heat mapping techniques
- Reporting risk scores to leadership
- Third-party scoring validation
- Model recalibration triggers
Module 6. Data Integrity and Provenance
Ensure AI vendor data practices meet audit standards.
12 chapters in this module
- Data lineage tracking methods
- Training data representativeness
- Data anonymization techniques
- Labeling process transparency
- Data refresh and retention policies
- Cross-border data transfer compliance
- Consent management for training data
- Synthetic data use disclosure
- Data poisoning risk mitigation
- Data quality audits
- Vendor data governance documentation
- Right to erasure implementation
Module 7. Model Transparency and Explainability
Evaluate AI model interpretability for audit purposes.
12 chapters in this module
- Levels of model explainability
- SHAP and LIME for audit validation
- Model cards and technical documentation
- Feature importance reporting
- Counterfactual explanations
- Black-box vs. white-box tradeoffs
- Regulatory expectations for interpretability
- Audit trail of model decisions
- Human-in-the-loop requirements
- Explainability for high-risk domains
- Model confidence scoring
- Third-party model validation
Module 8. Operational Resilience
Assess AI vendor operational stability and continuity.
12 chapters in this module
- Uptime and SLA evaluation
- Disaster recovery planning
- Model rollback capabilities
- Incident escalation procedures
- Redundancy in inference infrastructure
- Model monitoring in production
- Drift detection thresholds
- Automated alerting systems
- Human oversight protocols
- Vendor breach response timelines
- Business continuity testing
- Service degradation protocols
Module 9. Ethical and Bias Audits
Conduct structured assessments of AI fairness and ethical alignment.
12 chapters in this module
- Bias definition in audit context
- Protected class identification
- Disparate impact testing
- Fairness metric selection
- Bias mitigation techniques
- Audit documentation of fairness tests
- Stakeholder feedback mechanisms
- Ethical use policy enforcement
- Model purpose alignment checks
- Community impact considerations
- Bias audit reporting
- Remediation tracking
Module 10. Third-Party Ecosystem Risk
Map and assess risks from vendor dependencies.
12 chapters in this module
- Sub-processor inventory
- Cloud infrastructure dependencies
- Open-source model usage
- API integration risks
- Vendor merger implications
- Supply chain transparency
- License compliance for AI models
- Model fine-tuning on third-party data
- Security practices of sub-vendors
- Contractual risk flow-down
- Exit readiness for sub-processor failure
- Resilience of supporting infrastructure
Module 11. Audit Evidence Curation
Build comprehensive, defensible audit packages.
12 chapters in this module
- Evidence collection workflow
- Version-controlled documentation
- Timestamping and hashing records
- Secure storage of audit artifacts
- Redaction protocols for sensitive data
- Cross-team evidence validation
- Automated evidence capture tools
- Sampling justification documentation
- Risk-based evidence depth
- Re-inspection readiness
- Evidence retention policies
- Audit trail completeness checks
Module 12. Continuous Monitoring and Reporting
Implement ongoing oversight for AI vendor performance.
12 chapters in this module
- Post-audit monitoring plan design
- Key risk indicator tracking
- Automated control monitoring
- Quarterly vendor review meetings
- Performance scorecarding
- Threshold-based alerting
- Remediation tracking systems
- Escalation protocols for non-compliance
- Regulatory change impact assessment
- Vendor innovation monitoring
- Audit readiness maintenance
- Lessons learned integration
How this maps to your situation
- Audit teams preparing for first AI vendor review
- Compliance leads building internal frameworks
- Risk managers expanding third-party oversight
- Governance teams aligning AI with enterprise standards
Before vs. after
Before
Uncertainty in assessing AI vendor compliance, reliance on ad-hoc checklists, inconsistent evidence standards, and limited cross-functional alignment.
After
Confidence in leading structured, audit-ready AI vendor assessments with standardized frameworks, repeatable processes, and cross-team validation.
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 4-6 hours per module, designed for self-paced study with immediate applicability to real-world audit planning.
If nothing changes
Without structured assessment methods, audit teams risk non-compliance findings, incomplete risk coverage, or delayed AI adoption due to unresolved vendor concerns.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance webinars, this course delivers implementation-grade workflows, control mappings, and audit-specific templates used by leading organizations in regulated sectors.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk managers, and technology governance professionals in regulated industries who assess third-party AI vendors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a digital certificate is issued upon finishing all modules and assessments.
$199 one-time. Approximately 4-6 hours per module, designed for self-paced study with immediate applicability to real-world audit planning..
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