A tailored course, built for your situation
Enterprise-Class AI Vendor Risk Assessment for Audit Teams
Master the next generation of AI vendor governance with implementation-grade frameworks for audit readiness
$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 face increasing pressure to assess AI vendors, but lack standardized, scalable frameworks. Ad-hoc checklists lead to inconsistent outcomes, missed risk vectors, and delayed approvals. Without a structured approach, teams struggle to justify findings or align with enterprise risk appetite.
Who this is for
Compliance officers, internal auditors, risk analysts, and technology governance professionals in mid-to-large enterprises implementing or scaling AI vendor oversight.
Who this is not for
This course is not for individual contributors focused solely on model development, nor for organizations without formal vendor audit processes.
What you walk away with
- Apply a standardized 5-layer AI vendor risk assessment model
- Conduct defensible, repeatable evaluations aligned with enterprise risk appetite
- Leverage automated scoring templates to reduce assessment time by up to 60%
- Integrate AI vendor reviews into existing audit workflows and control frameworks
- Produce audit-ready documentation packages accepted by regulators and internal stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Enterprise Audit
Establish core concepts, risk domains, and audit relevance of AI vendor assessments.
12 chapters in this module
- Defining AI vendor risk in the enterprise context
- Evolution of vendor audit from IT to AI systems
- Key differences between traditional and AI vendor risks
- Regulatory expectations and audit defensibility
- Stakeholder alignment: Legal, Security, Procurement, Audit
- Risk appetite frameworks and vendor tiering
- Common failure modes in AI vendor deployments
- Case study: Financial services AI audit review
- Building the business case for structured assessment
- Governance models for cross-functional oversight
- Audit lifecycle integration points
- Measuring maturity of AI vendor risk practices
Module 2. AI Vendor Risk Domains and Taxonomy
Break down AI vendor risk into 12 measurable domains with audit-specific indicators.
12 chapters in this module
- Data provenance and lineage controls
- Model transparency and explainability standards
- Bias detection and fairness validation
- Performance monitoring and drift detection
- Security and access control architecture
- Resilience and failover capabilities
- Third-party dependency mapping
- IP and licensing compliance
- Change management and version control
- Incident response and breach notification
- Ethical AI principles and alignment
- Sustainability and compute efficiency
Module 3. Assessment Design and Scoring Methodology
Design weighted scoring models, evidence collection plans, and risk tiering logic.
12 chapters in this module
- Developing risk-weighted question sets
- Evidence-based verification techniques
- Scoring rubrics: High-medium-low vs numeric scales
- Automated scoring with template logic
- Risk aggregation across domains
- Threshold setting for go/no-go decisions
- Handling partial or missing evidence
- Peer review and validation protocols
- Versioning assessment frameworks
- Benchmarking against industry peers
- Integrating control testing results
- Adjusting for organizational risk tolerance
Module 4. Evidence Collection and Vendor Engagement
Structure request lists, manage vendor responses, and verify claims effectively.
12 chapters in this module
- Designing efficient evidence request packages
- Standardizing vendor response formats
- Follow-up protocols for incomplete submissions
- Conducting technical validation interviews
- Onsite vs remote assessment planning
- Using third-party audit reports (SOC, ISO)
- Leveraging API-based data collection
- Validating model performance claims
- Testing for adversarial robustness
- Reviewing training data documentation
- Assessing model monitoring dashboards
- Documenting exceptions and compensating controls
Module 5. Integration with Existing Audit Frameworks
Align AI vendor assessments with SOX, ISO 27001, NIST, and internal control frameworks.
12 chapters in this module
- Mapping AI risks to SOX control objectives
- Integrating with ISO 27001 Annex A controls
- Aligning with NIST AI Risk Management Framework
- Incorporating into internal audit risk assessments
- Linking to enterprise risk management (ERM)
- Coordination with cybersecurity audit programs
- Reporting to audit committees and boards
- Using AI vendor findings in control testing
- Updating audit plans based on vendor risk
- Cross-referencing with third-party risk platforms
- Automating control evidence collection
- Maintaining audit trail and documentation
Module 6. Automated Assessment Workflows
Implement scalable, repeatable workflows using templates and tooling.
12 chapters in this module
- Designing reusable assessment templates
- Building automated scoring engines in spreadsheets
- Using conditional logic for risk flagging
- Integrating with GRC platforms
- Workflow automation with low-code tools
- Setting up dashboard reporting
- Version control for assessment updates
- User access and role-based permissions
- Audit logging and change tracking
- Data privacy in assessment storage
- Integrating with procurement systems
- Scaling across global audit teams
Module 7. Bias, Fairness, and Ethical AI Assessment
Evaluate AI vendor claims on fairness, equity, and ethical alignment.
12 chapters in this module
- Defining fairness metrics for different use cases
- Assessing bias in training data
- Reviewing model performance across subgroups
- Evaluating mitigation strategies
- Auditing algorithmic decision-making
- Reviewing ethical AI policies and governance
- Stakeholder feedback mechanisms
- Handling high-risk decision domains
- Compliance with AI ethics guidelines
- Third-party bias audit reports
- Documentation of fairness testing
- Remediation planning for biased outcomes
Module 8. Model Performance and Reliability Validation
Verify vendor claims on accuracy, robustness, and operational reliability.
12 chapters in this module
- Reviewing model validation reports
- Assessing performance on representative data
- Testing for concept and data drift
- Evaluating uncertainty quantification
- Stress testing under edge cases
- Reviewing monitoring and alerting
- Incident response for model failures
- Failover and fallback mechanisms
- Human-in-the-loop requirements
- Version rollback capabilities
- Performance benchmarking
- Documentation of testing results
Module 9. Security and Data Protection Review
Assess AI vendor security posture, data handling, and compliance.
12 chapters in this module
- Data encryption in transit and at rest
- Access control and identity management
- API security and rate limiting
- Penetration testing and vulnerability management
- Data residency and cross-border transfer
- Anonymization and pseudonymization
- Compliance with privacy regulations
- Third-party security certifications
- Incident response and notification
- Logging and monitoring capabilities
- Secure development lifecycle
- Supply chain security for AI components
Module 10. Resilience, Scalability, and Operational Readiness
Evaluate AI vendor infrastructure, uptime, and support capabilities.
12 chapters in this module
- Service level agreements and uptime guarantees
- Disaster recovery and backup processes
- Scalability under peak load
- Monitoring and observability
- Change management and release cycles
- Vendor support and escalation paths
- Documentation and knowledge transfer
- Redundancy and failover design
- Capacity planning and forecasting
- Incident management processes
- Post-mortem review practices
- Operational risk assessment
Module 11. Legal, IP, and Contractual Risk Assessment
Review AI vendor contracts, IP rights, and liability coverage.
12 chapters in this module
- Ownership of models and outputs
- Licensing terms and restrictions
- Liability for erroneous decisions
- Indemnification clauses
- Warranties and representations
- Termination and exit rights
- Data ownership and deletion
- Audit rights and access
- Subprocessor management
- Compliance with export controls
- Jurisdiction and dispute resolution
- Force majeure and business continuity
Module 12. Reporting, Remediation, and Continuous Monitoring
Produce audit reports, track remediation, and establish ongoing oversight.
12 chapters in this module
- Structuring audit findings and risk ratings
- Writing clear, actionable recommendations
- Prioritizing remediation efforts
- Tracking open issues to closure
- Establishing continuous monitoring
- Setting up periodic reassessment cycles
- Vendor performance dashboards
- Escalation paths for unresolved risks
- Integrating with third-party risk management
- Reporting to executive leadership
- Maintaining audit trail
- Lessons learned and framework improvement
How this maps to your situation
- Audit teams launching first AI vendor review
- Compliance functions scaling AI oversight across business units
- Risk teams integrating AI into third-party risk frameworks
- Technology governance establishing AI procurement controls
Before vs. after
Before
Ad-hoc, inconsistent AI vendor reviews with limited audit defensibility and stakeholder alignment.
After
Standardized, scalable, and defensible AI vendor risk assessments integrated into audit workflows.
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 40-50 hours of self-paced learning, designed for busy professionals with modular, implementation-focused content.
If nothing changes
Without a structured approach, audit teams risk inconsistent evaluations, increased exposure to AI-related incidents, and reduced influence in strategic technology decisions.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk primers, this program delivers audit-specific, implementation-grade frameworks with templates and playbooks used by leading enterprise audit teams.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk analysts, and technology governance professionals in mid-to-large enterprises implementing or scaling AI vendor oversight.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after finishing all modules and passing the final assessment.
$199 one-time. Approximately 40-50 hours of self-paced learning, designed for busy professionals with modular, implementation-focused content..
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