A tailored course, built for your situation
Audit-Tested AI Vendor Risk Assessment for Audit Teams
Implement-ready training for compliance and technology leaders navigating AI procurement
$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.
Frequent, unstructured AI vendor reviews slow delivery and create inconsistency under audit scrutiny
The situation this course is for
Audit teams face increasing pressure to evaluate AI vendors without standardized assessment criteria. Ad hoc methods lead to rework, missed control gaps, and challenges justifying decisions to oversight bodies. As AI adoption accelerates, the lack of a unified framework undermines confidence and scalability.
Who this is for
Compliance officers, internal auditors, risk managers, and technology governance leads in regulated environments responsible for AI vendor due diligence
Who this is not for
Individuals seeking introductory AI awareness or general cybersecurity training without focus on vendor assessment or audit readiness
What you walk away with
- Apply a standardized, audit-tested framework to evaluate AI vendors
- Document risk assessments that meet internal and external audit expectations
- Identify critical control gaps in AI vendor practices across data, model governance, and security
- Streamline review cycles with reusable templates and decision guides
- Communicate findings clearly to technical and non-technical stakeholders
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Audit Contexts
Establish core principles for assessing AI vendors within regulated audit environments
12 chapters in this module
- Defining AI vendor risk scope
- Audit expectations for third-party AI
- Regulatory drivers shaping assessment criteria
- Key differences from traditional vendor review
- Roles and responsibilities in AI due diligence
- Risk taxonomy for AI systems
- Mapping AI risk to control frameworks
- Overview of audit-tested assessment lifecycle
- Stakeholder alignment strategies
- Common misconceptions in AI risk evaluation
- Integrating AI review into existing audit plans
- Course navigation and implementation roadmap
Module 2. Pre-Assessment Planning and Scoping
Design targeted review cycles based on vendor type, deployment model, and data sensitivity
12 chapters in this module
- Classifying AI vendors by risk tier
- Determining assessment depth based on use case
- Engaging legal and procurement early
- Setting evaluation timelines and milestones
- Resource planning for internal coordination
- Vendor pre-questionnaire design
- Data classification alignment
- Identifying board-level reporting needs
- Establishing cross-functional review teams
- Documenting assumptions and constraints
- Version control for assessment artifacts
- Leveraging past audits for baseline comparison
Module 3. Vendor Documentation Requirements
Specify evidence packages vendors must provide to support audit-grade reviews
12 chapters in this module
- Required policies and governance documents
- Model development lifecycle disclosures
- Data provenance and lineage expectations
- Security and access control documentation
- Incident response and breach notification terms
- Third-party dependency transparency
- Explainability and bias mitigation reports
- Change management procedures
- Certifications and attestation standards
- Service-level agreement alignment
- Data retention and deletion policies
- Documentation validation checklist
Module 4. Model Governance and Development Practices
Evaluate vendor model development for reproducibility, oversight, and compliance
12 chapters in this module
- Model design documentation standards
- Versioning and lineage tracking
- Bias detection and mitigation protocols
- Model validation and testing rigor
- Human-in-the-loop requirements
- Model drift monitoring procedures
- Red teaming and adversarial testing
- Model inventory maintenance
- Peer review practices
- Model decommissioning process
- Ethics board or review committee existence
- Model risk grading methodology
Module 5. Data Management and Privacy Compliance
Assess vendor data handling against privacy regulations and organizational policies
12 chapters in this module
- Data minimization practices
- Consent management mechanisms
- Cross-border data transfer safeguards
- Right to be forgotten implementation
- Data anonymization techniques
- Data subject access request handling
- Data breach response timelines
- Data retention enforcement
- Third-party data sharing disclosures
- Privacy by design integration
- Data protection officer availability
- Jurisdictional compliance alignment
Module 6. Security Controls and Infrastructure Hardening
Validate technical and organizational security measures protecting AI systems
12 chapters in this module
- Network segmentation practices
- Encryption standards in transit and at rest
- Access control and role-based permissions
- Multi-factor authentication enforcement
- Vulnerability management lifecycle
- Penetration testing frequency
- Secure software development lifecycle
- API security and rate limiting
- Threat detection and logging
- Incident response playbooks
- Zero trust architecture alignment
- Security audit trail completeness
Module 7. Bias, Fairness, and Ethical Alignment
Evaluate vendor approaches to algorithmic fairness and ethical use
12 chapters in this module
- Bias identification across model lifecycle
- Fairness metric selection and thresholds
- Disaggregated performance reporting
- Ethical use policy enforcement
- Prohibited use case restrictions
- Stakeholder feedback mechanisms
- Community impact assessments
- Model interpretability standards
- Bias mitigation technique documentation
- Ongoing fairness monitoring
- Redress mechanisms for affected parties
- Ethics review board engagement
Module 8. Transparency and Explainability Standards
Ensure vendor models provide meaningful explanations for decisions
12 chapters in this module
- Model interpretability requirements
- Local vs. global explanation methods
- User-facing explanation clarity
- Technical documentation depth
- Model card implementation
- System card standards
- Performance benchmarking disclosure
- Limitations and uncertainty communication
- Stakeholder-specific explanation formats
- Audit trail for model decisions
- Right to explanation compliance
- Explainability testing protocols
Module 9. Monitoring, Logging, and Incident Response
Review continuous monitoring capabilities and breach preparedness
12 chapters in this module
- Model performance monitoring
- Data quality tracking
- Anomaly detection systems
- Logging completeness and retention
- Incident classification schema
- Notification procedures within SLAs
- Forensic investigation readiness
- Model rollback capabilities
- Chaos engineering practices
- Uptime and availability reporting
- Disaster recovery testing
- Post-incident review process
Module 10. Contractual and Legal Safeguards
Identify critical legal terms that protect organizational interests
12 chapters in this module
- Liability allocation for AI errors
- IP ownership and licensing terms
- Indemnification clauses
- Audit rights and access frequency
- Subprocessor approval process
- Data ownership clarity
- Termination and exit assistance
- Data portability commitments
- Warranties and representations
- Insurance requirements
- Governing law and dispute resolution
- Enforceability of AI-specific clauses
Module 11. Audit Trail and Documentation Completeness
Ensure assessment outputs meet internal and external audit requirements
12 chapters in this module
- Evidence collection standards
- Version control for findings
- Finding severity classification
- Remediation tracking system
- Stakeholder review documentation
- Approval workflows
- Retention policy alignment
- Cross-referencing control frameworks
- Automated validation checks
- Reporting to audit committees
- Documentation for regulatory exams
- Lessons learned integration
Module 12. Scaling and Continuous Improvement
Establish feedback loops to refine AI vendor risk practices over time
12 chapters in this module
- Assessment cycle benchmarking
- Lessons learned from past audits
- Vendor performance tracking
- Control effectiveness measurement
- Framework update process
- Training for new team members
- Automation opportunities
- Metrics for program maturity
- Stakeholder satisfaction surveys
- Integration with enterprise risk management
- External benchmarking participation
- Future-proofing against emerging threats
How this maps to your situation
- Assessing AI vendors for financial services compliance
- Validating model risk controls in healthcare AI
- Reviewing cloud-based AI platforms for data residency
- Auditing third-party algorithms in automated decision systems
Before vs. after
Before
Uncertainty in evaluating AI vendors leads to inconsistent assessments and audit rework
After
Confidence in executing standardized, audit-ready evaluations that accelerate trusted AI adoption
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 hours of self-paced learning, designed to fit around professional responsibilities.
If nothing changes
Continuing with ad hoc assessment methods increases the likelihood of control gaps, delays in AI deployment, and findings during internal or external audits.
How this compares to the alternatives
Unlike general AI awareness courses or one-size-fits-all vendor checklists, this program delivers audit-tested, implementation-grade methodology tailored to regulated environments and governance professionals.
Frequently asked
Who is this course designed for?
Compliance officers, internal auditors, risk managers, and technology governance leads responsible for AI vendor due diligence in regulated sectors.
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 final knowledge checks.
$199 one-time. Approximately 45 hours of self-paced learning, designed to fit around professional responsibilities..
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