A tailored course, built for your situation
Modern AI Vendor Risk Assessment for Audit Teams
A 12-module implementation-grade course for audit professionals 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.
Audit teams are being asked to assess AI vendors without clear frameworks, consistent methodologies, or access to technical validation tools.
The situation this course is for
As AI adoption accelerates, audit functions face pressure to evaluate complex third-party systems with limited guidance. Generic risk checklists fail to capture model bias, data provenance, or dynamic performance drift. Without a structured approach, audit teams risk delivering shallow reviews or delaying critical deployments.
Who this is for
Compliance officers, internal auditors, risk leads, and technology assurance professionals in regulated industries who are responsible for evaluating third-party AI systems.
Who this is not for
This course is not for software developers building AI models or vendors marketing AI tools. It is designed specifically for audit and assurance professionals evaluating external AI solutions.
What you walk away with
- Apply a standardized framework to assess AI vendor risk across legal, technical, and operational domains
- Evaluate model fairness, explainability, and performance stability using audit-appropriate methods
- Construct evidence-based audit reports that satisfy regulators and executives
- Use customizable templates to accelerate assessment scoping and evidence collection
- Lead cross-functional AI vendor reviews with confidence and clarity
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk
Introduce core concepts, market trends, and audit relevance of third-party AI systems.
12 chapters in this module
- Defining AI in the vendor landscape
- Growth drivers for external AI adoption
- Audit's evolving role in AI governance
- Regulatory signals shaping vendor oversight
- Key risk categories in AI procurement
- Differences between traditional and AI vendor risk
- Stakeholder expectations across functions
- Case example: Cloud-based forecasting tool
- Case example: HR screening algorithm
- Case example: Fraud detection API
- Common misconceptions in AI risk
- Building the business case for structured assessment
Module 2. Legal and Compliance Alignment
Map AI vendor activities to current compliance obligations and emerging standards.
12 chapters in this module
- GDPR and automated decision-making
- Sector-specific rules: finance, healthcare, government
- Contractual obligations for AI performance
- Intellectual property and model ownership
- Liability frameworks for algorithmic harm
- Audit rights in vendor agreements
- Cross-border data and model hosting
- Regulatory sandboxes and compliance testing
- Documentation standards for audit trails
- Handling model updates and version changes
- Right-to-explanation requirements
- Compliance checklist for procurement teams
Module 3. Technical Due Diligence Framework
Evaluate vendor technical posture without requiring data science expertise.
12 chapters in this module
- Assessing model development lifecycle
- Data sourcing and bias mitigation practices
- Model validation methods used by vendors
- API security and integration risks
- Infrastructure resilience and uptime
- Monitoring for model drift and decay
- Access controls and user permissions
- Incident response planning for AI failures
- Red teaming and adversarial testing
- Third-party audits and attestation reports
- Open source component risks
- Vendor transparency scorecard
Module 4. Model Performance Validation
Verify that AI systems perform as promised under real-world conditions.
12 chapters in this module
- Defining performance metrics for audit
- Accuracy vs. precision vs. recall
- Fairness metrics across demographic groups
- Calibration and confidence scoring
- Benchmarking against baseline methods
- Testing for edge case behavior
- Handling class imbalance in training data
- Evaluating generalization across environments
- Time-series performance consistency
- Vendor-provided test results: what to trust
- Designing independent validation tests
- Performance reporting templates
Module 5. Bias and Fairness Auditing
Detect and document algorithmic bias in vendor systems.
12 chapters in this module
- Types of bias in AI systems
- Disparate impact analysis
- Protected attributes and proxy variables
- Pre-processing, in-processing, post-processing fixes
- Fairness metrics: demographic parity, equalized odds
- Case study: Credit scoring model
- Case study: Resume screening tool
- Sampling methods for bias testing
- Intersectional fairness assessment
- Bias mitigation transparency
- Reporting bias findings to leadership
- Bias remediation tracking
Module 6. Explainability and Transparency
Evaluate vendor claims about model interpretability and decision logic.
12 chapters in this module
- Global vs. local explainability
- SHAP, LIME, and other explanation methods
- Vendor-provided explanations: validity checks
- User-facing explanation requirements
- Regulatory expectations for transparency
- Trade-offs between accuracy and explainability
- Auditing black-box models
- Surrogate models for insight
- Explanation consistency over time
- Testing explanation fidelity
- Documentation standards for interpretability
- Explainability scorecard for vendors
Module 7. Data Governance and Provenance
Assess the quality, sourcing, and handling of data used to train and operate AI models.
12 chapters in this module
- Data lineage tracking
- Training data representativeness
- Synthetic data usage and limitations
- Data labeling quality controls
- Consent and licensing for training data
- Data retention and deletion policies
- PII handling in model inputs
- Data drift detection methods
- Vendor data partnerships and sourcing
- Data quality metrics
- Audit trails for data changes
- Data governance questionnaire
Module 8. Operational Risk and Monitoring
Evaluate ongoing operational integrity of AI vendor systems.
12 chapters in this module
- Real-time performance monitoring
- Alerting thresholds for model degradation
- Human-in-the-loop requirements
- Fallback mechanisms during failure
- Change management for model updates
- Version control and rollback capability
- Logging and audit trail completeness
- Incident reporting timelines
- Service level agreements for AI uptime
- Capacity planning for usage spikes
- Vendor support responsiveness
- Operational risk dashboard
Module 9. Security and Privacy Controls
Assess cybersecurity and privacy safeguards in AI vendor environments.
12 chapters in this module
- Penetration testing results review
- Encryption in transit and at rest
- Model inversion and membership inference risks
- Adversarial attacks on AI systems
- Secure model deployment practices
- Access logging and anomaly detection
- Third-party security certifications
- Vulnerability disclosure policies
- Privacy-preserving techniques (federated learning, differential privacy)
- Security incident history review
- Vendor security questionnaire
- Security control mapping
Module 10. Contract and SLA Evaluation
Review vendor contracts for enforceable risk management terms.
12 chapters in this module
- Performance guarantees and penalties
- Model accuracy commitments
- Remediation timelines for failures
- Audit rights and access provisions
- Data ownership and portability
- Termination clauses for non-compliance
- Indemnification for algorithmic harm
- Insurance requirements for AI vendors
- Change notification obligations
- Dispute resolution mechanisms
- Benchmarking clauses
- Contractual risk matrix
Module 11. Audit Reporting and Communication
Structure clear, actionable audit findings for technical and executive audiences.
12 chapters in this module
- Executive summary best practices
- Technical appendix structure
- Risk rating methodologies
- Visualizing model performance trends
- Communicating bias findings sensitively
- Recommendation prioritization
- Stakeholder communication plans
- Follow-up and remediation tracking
- Board-level reporting formats
- Regulator-facing documentation
- Version control for audit reports
- Reporting template library
Module 12. Scaling AI Risk Assessment Across the Enterprise
Design repeatable processes for managing multiple AI vendor audits.
12 chapters in this module
- Centralized AI vendor inventory
- Risk-based prioritization framework
- Tiered assessment protocols
- Cross-functional review committees
- Integration with third-party risk management
- Automated assessment tooling
- Training internal teams
- Continuous monitoring integration
- Benchmarking against peer organizations
- Maturity model for AI audit capability
- Roadmap for audit function evolution
- Enterprise scaling playbook
How this maps to your situation
- Assessing a new AI-powered analytics platform
- Auditing a vendor-provided chatbot for customer service
- Reviewing a machine learning model for credit underwriting
- Validating an AI-driven recruitment screening tool
Before vs. after
Before
Audit teams rely on ad-hoc reviews, lack standardized frameworks, and struggle to assess technical aspects of AI vendors confidently.
After
Audit teams apply a consistent, evidence-based methodology to evaluate AI vendors across legal, technical, and operational dimensions, producing clear, actionable reports.
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 45, 60 hours total, designed for self-paced learning with practical application between modules.
If nothing changes
Without structured AI vendor risk assessment, audit teams risk overlooking critical model flaws, enabling regulatory exposure, reputational damage, and operational disruption from poorly governed AI systems.
How this compares to the alternatives
Unlike generic risk management courses or academic AI ethics programs, this course delivers audit-specific frameworks, real-world templates, and implementation guidance tailored to evaluating commercial AI vendors in regulated environments.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk leads, and technology assurance professionals responsible for evaluating third-party AI systems.
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 audit professionals and provides clear explanations of technical concepts without requiring prior data science expertise.
$199 one-time. Approximately 45, 60 hours total, designed for self-paced learning with practical application between modules..
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