Skip to main content
Image coming soon

Strategic AI Vendor Risk Assessment for Audit Teams

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Strategic AI Vendor Risk Assessment for Audit Teams

Mastering governance, control, and compliance in third-party AI engagements

$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 criteria, or practical tooling, leading to inconsistent evaluations and governance gaps.

The situation this course is for

As organizations accelerate AI adoption through third-party vendors, audit functions are under pressure to provide assurance without mature methodologies. Many teams rely on ad hoc checklists or repurposed IT audit practices that miss critical AI-specific risks, from model drift and data provenance to vendor lock-in and ethical alignment. Without a strategic, standardized approach, audit insights risk being overlooked in high-stakes vendor decisions.

Who this is for

Business and technology audit professionals in mid-to-senior roles who are responsible for evaluating third-party technology providers, especially in environments adopting AI-driven solutions at scale.

Who this is not for

This course is not for entry-level auditors, developers building AI models, or vendors marketing AI solutions. It is specifically designed for audit practitioners focused on governance and control.

What you walk away with

  • Apply a structured framework to evaluate AI vendor risk across technical, operational, ethical, and compliance dimensions
  • Leverage standardized assessment templates to increase consistency and reduce evaluation time
  • Integrate AI vendor risk findings into broader audit reporting and governance workflows
  • Influence procurement and vendor oversight decisions with credible, evidence-based insights
  • Anticipate emerging regulatory expectations around algorithmic accountability and third-party AI governance

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Audit
Establish core concepts, audit relevance, and the evolving control landscape for third-party AI.
12 chapters in this module
  1. Defining AI vendor risk in the audit context
  2. Key differences between traditional and AI vendor audits
  3. Regulatory drivers shaping AI vendor oversight
  4. The role of audit in AI governance frameworks
  5. Stakeholder expectations across legal, compliance, and operations
  6. Common failure points in AI vendor engagements
  7. Risk taxonomy for third-party AI systems
  8. Mapping AI vendor risk to organizational objectives
  9. Audit readiness assessment for AI vendor review
  10. Building cross-functional alignment early
  11. Case study: Financial services vendor audit
  12. Self-assessment: Current audit maturity level
Module 2. Vendor Landscape and Market Intelligence
Analyze the AI vendor ecosystem to identify risk profiles, market trends, and differentiation factors.
12 chapters in this module
  1. Categories of AI vendors and their risk implications
  2. Assessing vendor maturity and longevity
  3. Evaluating funding, ownership, and business model stability
  4. Benchmarking vendor offerings against audit needs
  5. Geopolitical factors in vendor selection
  6. Open source vs. proprietary AI vendor models
  7. Vendor ecosystem dependencies and supply chain risks
  8. Monitoring vendor reputation and incident history
  9. Using public disclosures for risk insight
  10. Third-party certifications and their audit value
  11. Case study: Healthcare AI vendor evaluation
  12. Template: Vendor intelligence scorecard
Module 3. Governance and Accountability Frameworks
Evaluate vendor governance structures, leadership accountability, and ethical AI commitments.
12 chapters in this module
  1. Assessing AI ethics and responsible AI programs
  2. Vendor board oversight of AI development
  3. Leadership incentives and accountability mechanisms
  4. Transparency in AI design and deployment
  5. Handling bias, fairness, and model explainability
  6. Incident response and escalation protocols
  7. Audit rights and access provisions in contracts
  8. Subcontractor and partner network oversight
  9. Whistleblower and reporting channels
  10. Vendor AI policy documentation review
  11. Case study: Bias disclosure in a talent platform
  12. Checklist: Governance due diligence
Module 4. Data Provenance and Privacy Compliance
Examine how vendors source, manage, and protect data across the AI lifecycle.
12 chapters in this module
  1. Data lineage and provenance in AI training sets
  2. Consent and lawful basis for data use
  3. PII handling and anonymization techniques
  4. Cross-border data transfer mechanisms
  5. Data minimization and retention policies
  6. Vendor access controls and data segregation
  7. Third-party data sourcing risks
  8. Audit trails for data processing activities
  9. Privacy impact assessments and documentation
  10. GDPR, CCPA, and global privacy alignment
  11. Case study: Data leakage in a customer insights tool
  12. Template: Data compliance matrix
Module 5. Model Development and Technical Controls
Review vendor practices for model development, testing, and technical risk mitigation.
12 chapters in this module
  1. Model development lifecycle oversight
  2. Version control and change management
  3. Testing rigor and validation protocols
  4. Model documentation and reproducibility
  5. Security practices in model training environments
  6. Access controls for model development teams
  7. Code review and vulnerability scanning
  8. Model hardening and adversarial testing
  9. Use of synthetic data and its implications
  10. Vendor tooling and infrastructure choices
  11. Case study: Model poisoning in a fraud detection system
  12. Checklist: Technical controls review
Module 6. Model Performance and Monitoring
Assess how vendors monitor model behavior, detect drift, and maintain performance over time.
12 chapters in this module
  1. Performance metrics and KPIs for AI models
  2. Model drift detection and response
  3. Real-time monitoring and alerting
  4. Feedback loops and continuous improvement
  5. Handling concept and data drift
  6. Model decay and retraining schedules
  7. Auditability of model performance logs
  8. Benchmarking against industry standards
  9. Explainability tools and techniques
  10. Human-in-the-loop validation processes
  11. Case study: Declining accuracy in a credit scoring model
  12. Template: Model monitoring assessment
Module 7. Operational Resilience and Business Continuity
Evaluate vendor operational stability, disaster recovery, and service continuity planning.
12 chapters in this module
  1. Service level agreements and uptime guarantees
  2. Disaster recovery and failover capabilities
  3. Incident response planning and communication
  4. Capacity planning and scalability
  5. Redundancy in infrastructure and data
  6. Vendor dependency on critical third parties
  7. Business continuity testing and results
  8. Geographic distribution of operations
  9. Change management and deployment windows
  10. Support availability and escalation paths
  11. Case study: Outage in a cloud-based AI service
  12. Checklist: Resilience due diligence
Module 8. Security and Cyber Risk Posture
Analyze the vendor's cybersecurity practices, threat model, and incident history.
12 chapters in this module
  1. Cybersecurity framework alignment (e.g., NIST, ISO)
  2. Threat modeling for AI systems
  3. Penetration testing and red team results
  4. Vulnerability disclosure and patching cadence
  5. Endpoint and network security controls
  6. Identity and access management practices
  7. Encryption standards in transit and at rest
  8. Zero trust architecture adoption
  9. Security awareness and training programs
  10. Third-party audit reports (SOC 2, ISO 27001)
  11. Case study: Breach via vendor API exposure
  12. Template: Security control assessment
Module 9. Legal and Contractual Risk Management
Review contractual terms, liability, indemnification, and exit strategies.
12 chapters in this module
  1. Liability for AI-generated decisions
  2. Indemnification clauses and risk transfer
  3. IP ownership and model copyright
  4. Warranties and service guarantees
  5. Termination rights and data portability
  6. Exit strategies and model handover
  7. Force majeure and dispute resolution
  8. Insurance coverage for AI risks
  9. Regulatory change clauses
  10. Audit rights and access frequency
  11. Case study: Contract dispute over model ownership
  12. Checklist: Legal clause review
Module 10. Integration and Change Management
Assess how AI systems are deployed, updated, and integrated into client environments.
12 chapters in this module
  1. API security and integration patterns
  2. Change management and version control
  3. Deployment pipelines and CI/CD practices
  4. Impact assessment for model updates
  5. Rollback and fallback mechanisms
  6. User training and adoption support
  7. Monitoring integration points
  8. Handling configuration drift
  9. Vendor collaboration with internal teams
  10. Change communication protocols
  11. Case study: Integration failure in CRM system
  12. Template: Integration risk assessment
Module 11. Reporting and Audit Documentation
Develop clear, actionable audit reports and maintain defensible documentation.
12 chapters in this module
  1. Structuring AI vendor risk findings for impact
  2. Risk rating methodologies and consistency
  3. Evidence collection and chain of custody
  4. Documenting technical and governance gaps
  5. Linking findings to organizational risk appetite
  6. Executive summaries for board reporting
  7. Follow-up and remediation tracking
  8. Maintaining audit independence and objectivity
  9. Versioning and archiving audit workpapers
  10. Using visuals to communicate complex risks
  11. Case study: Audit report influencing vendor renegotiation
  12. Template: Audit finding write-up guide
Module 12. Scaling AI Vendor Risk Across the Enterprise
Operationalize AI vendor risk assessment across multiple teams, systems, and business units.
12 chapters in this module
  1. Building a centralized AI vendor risk function
  2. Standardizing assessment across business units
  3. Integrating with enterprise risk management
  4. Training internal audit teams on AI risk
  5. Automating data collection and scoring
  6. Dashboarding and executive reporting
  7. Continuous monitoring strategies
  8. Feedback loops with procurement and legal
  9. Updating frameworks as AI evolves
  10. Benchmarking against peer organizations
  11. Case study: Enterprise rollout in a global bank
  12. Playbook: Scaling your AI audit program

How this maps to your situation

  • Audit teams facing first-time AI vendor review
  • Organizations scaling AI adoption through third parties
  • Regulatory-driven demand for stronger vendor oversight
  • Cross-functional initiatives requiring audit alignment

Before vs. after

Before
Audit teams conduct AI vendor reviews inconsistently, relying on fragmented checklists and lacking standardized criteria or influence in procurement decisions.
After
Audit teams lead with confidence using a structured, repeatable framework to assess AI vendors, produce credible reports, and shape vendor governance across the organization.

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 36 hours of total engagement, designed for flexible, self-paced learning with practical application between modules.

If nothing changes
Without a strategic approach, audit teams risk oversight gaps, inconsistent evaluations, and diminished influence in critical AI vendor decisions, potentially exposing the organization to undetected risks in performance, compliance, and security.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level risk overviews, this program delivers audit-specific, implementation-grade content with templates, case studies, and a playbook tailored to real-world vendor assessment, not theory.

Frequently asked

Who is this course designed for?
Audit professionals in business or technology roles who are responsible for evaluating third-party AI vendors and want a structured, repeatable assessment framework.
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 awarded after finishing all modules and passing the final assessment.
$199 one-time. Approximately 36 hours of total engagement, designed for flexible, 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
!