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Modern AI Vendor Risk Assessment for Compliance Officers

$199.00
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A tailored course, built for your situation

Modern AI Vendor Risk Assessment for Compliance Officers

A 12-module implementation-grade course for compliance and technology leaders navigating AI procurement with confidence

$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.
AI vendor assessments are often inconsistent, reactive, or siloed, leading to delayed deployments and compliance gaps

The situation this course is for

Compliance officers are increasingly asked to evaluate complex AI systems without clear frameworks, standardized checklists, or cross-functional alignment. Assessments happen late in the procurement cycle, create bottlenecks, and lack technical depth. This slows innovation and increases exposure to regulatory scrutiny.

Who this is for

Compliance officers, risk leads, and technology governance professionals in mid-to-large organizations adopting third-party AI solutions

Who this is not for

This is not for engineers building in-house AI models or for individuals seeking high-level AI awareness training

What you walk away with

  • Apply a structured, repeatable framework for AI vendor risk assessment
  • Align technical, legal, and compliance requirements across stakeholders
  • Evaluate AI vendors against evolving regulatory expectations
  • Use audit-ready documentation and assessment templates
  • Lead cross-functional AI procurement reviews with confidence

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Compliance
Establish core concepts, terminology, and the evolving role of compliance in AI procurement
12 chapters in this module
  1. Defining AI in the context of third-party risk
  2. The compliance officer’s evolving mandate
  3. Regulatory drivers shaping AI oversight
  4. Distinguishing AI risk from traditional vendor risk
  5. Mapping AI use cases to risk profiles
  6. Key stakeholders in AI procurement workflows
  7. Lifecycle view of AI vendor engagement
  8. Common pitfalls in early-stage assessments
  9. Building a risk taxonomy for AI vendors
  10. Benchmarking organizational readiness
  11. The role of policy in AI governance
  12. Foundational frameworks and reference models
Module 2. Regulatory Landscape and Compliance Alignment
Navigate current expectations from global regulators and standards bodies
12 chapters in this module
  1. Overview of AI-specific regulatory initiatives
  2. Interpreting FTC guidance on AI claims
  3. EU AI Act implications for procurement
  4. NIST AI RMF and organizational adoption
  5. Sector-specific rules in education and public service
  6. Data protection laws and AI processing
  7. Algorithmic accountability and fairness expectations
  8. Transparency requirements for third-party AI
  9. Enforcement trends and inspection readiness
  10. Aligning internal policies with external rules
  11. Preparing for regulatory inquiries
  12. Maintaining compliance posture over time
Module 3. Technical Due Diligence for Non-Engineers
Understand key technical components of AI systems without needing to code
12 chapters in this module
  1. How machine learning differs from traditional software
  2. Types of AI models and their risk implications
  3. Understanding training data sources and quality
  4. Model performance metrics that matter
  5. Evaluating bias and fairness testing practices
  6. Interpretability and explainability techniques
  7. API security and integration risks
  8. Model drift and monitoring capabilities
  9. Version control and update management
  10. Infrastructure and hosting considerations
  11. Third-party dependencies and supply chain risk
  12. Red teaming and adversarial testing disclosures
Module 4. Contractual Levers and Procurement Integration
Leverage procurement workflows and legal agreements to enforce risk standards
12 chapters in this module
  1. Timing AI assessments in the procurement cycle
  2. Pre-RFP risk screening checklists
  3. Incorporating AI-specific clauses in contracts
  4. Service level agreements for model performance
  5. Data rights and ownership provisions
  6. Audit rights and access to documentation
  7. Incident reporting and breach notification terms
  8. Vendor change management protocols
  9. Exit strategies and data portability
  10. Subprocessor oversight and transparency
  11. Liability allocation for AI-generated outcomes
  12. Renewal and performance review triggers
Module 5. Assessment Framework Design and Execution
Build and deploy a standardized assessment process across teams
12 chapters in this module
  1. Designing a tiered risk classification system
  2. Scoping assessments based on impact level
  3. Developing vendor self-assessment questionnaires
  4. Validating vendor responses with evidence checks
  5. Conducting technical interviews with vendor teams
  6. Using scoring models to prioritize risks
  7. Documenting findings and decision rationale
  8. Escalation paths for high-risk vendors
  9. Cross-functional review workflows
  10. Maintaining assessment version control
  11. Integrating with GRC platforms
  12. Reporting results to leadership and audit
Module 6. Bias, Fairness, and Ethical Risk Evaluation
Assess fairness practices and ethical safeguards in vendor AI systems
12 chapters in this module
  1. Defining fairness in organizational context
  2. Common bias types in training data and models
  3. Vendor documentation on bias testing
  4. Evaluating demographic parity and error rates
  5. Disaggregated performance reporting
  6. Mitigation strategies used by vendors
  7. Ongoing monitoring for fairness drift
  8. Stakeholder feedback mechanisms
  9. Handling contested AI outcomes
  10. Ethics review board disclosures
  11. Transparency in decision logic
  12. Public accountability and redress processes
Module 7. Data Governance and Privacy Integration
Ensure AI vendors comply with data handling and privacy standards
12 chapters in this module
  1. Data minimization in AI systems
  2. Purpose limitation and use case alignment
  3. Consent management for training data
  4. Anonymization and de-identification practices
  5. Cross-border data transfer mechanisms
  6. Right to access and deletion workflows
  7. Data retention and deletion schedules
  8. Logging and audit trail completeness
  9. Vendor data breach response plans
  10. Third-party data sourcing disclosures
  11. PIA and DPIA integration with AI reviews
  12. Data stewardship accountability
Module 8. Security and Resilience Verification
Evaluate the cybersecurity posture of AI vendors and their models
12 chapters in this module
  1. Secure development lifecycle practices
  2. Model inversion and membership inference risks
  3. Adversarial attacks and robustness testing
  4. API authentication and rate limiting
  5. Infrastructure security certifications
  6. Penetration testing disclosures
  7. Incident response and notification timelines
  8. Backup and recovery for AI components
  9. Zero trust architecture alignment
  10. Supply chain software bill of materials (SBOM)
  11. Vulnerability disclosure programs
  12. Security training for vendor development teams
Module 9. Audit Readiness and Documentation Standards
Prepare for internal and external audits with complete, defensible records
12 chapters in this module
  1. Building an AI vendor audit package
  2. Documenting assessment rationale and decisions
  3. Maintaining version-controlled evidence files
  4. Creating executive summaries for auditors
  5. Mapping controls to regulatory requirements
  6. Third-party attestation and certification review
  7. SOC 2 reports and AI-specific extensions
  8. Internal audit coordination strategies
  9. Preparing for surprise inspections
  10. Corrective action tracking and closure
  11. Retention policies for AI risk documentation
  12. Automating audit trail generation
Module 10. Cross-Functional Coordination and Influence
Lead alignment across legal, IT, procurement, and business units
12 chapters in this module
  1. Identifying key decision-makers in AI procurement
  2. Communicating risk in business-relevant terms
  3. Facilitating joint risk review sessions
  4. Negotiating trade-offs between speed and safety
  5. Building trust with technical teams
  6. Educating stakeholders on AI risk fundamentals
  7. Creating shared ownership of vendor outcomes
  8. Escalating unresolved conflicts effectively
  9. Developing playbooks for common scenarios
  10. Measuring team alignment over time
  11. Tracking cross-functional SLAs
  12. Celebrating risk-informed successes
Module 11. Ongoing Monitoring and Lifecycle Management
Maintain oversight throughout the vendor relationship
12 chapters in this module
  1. Designing continuous monitoring workflows
  2. Trigger-based reassessment protocols
  3. Performance dashboards for AI vendors
  4. Annual review planning and execution
  5. Change management oversight
  6. Model update validation processes
  7. Monitoring for regulatory changes
  8. Tracking vendor financial and operational health
  9. Customer support and escalation responsiveness
  10. Handling vendor acquisition or shutdown
  11. Renewal risk reassessment
  12. Lessons learned and process improvement
Module 12. Implementation Playbook and Organizational Adoption
Deploy the framework across your organization with confidence
12 chapters in this module
  1. Assessing organizational change readiness
  2. Piloting the framework with one team
  3. Customizing templates to your context
  4. Training others on the assessment process
  5. Integrating with existing risk management tools
  6. Gaining leadership buy-in and sponsorship
  7. Measuring adoption and impact
  8. Scaling across departments
  9. Maintaining consistency over time
  10. Updating the framework as AI evolves
  11. Sharing best practices externally
  12. Becoming a center of excellence

How this maps to your situation

  • You're being asked to assess AI vendors but lack a consistent method
  • You're coordinating across teams but struggling to align on risk criteria
  • You want to move from reactive reviews to proactive governance
  • You're preparing for increased regulatory scrutiny on AI use

Before vs. after

Before
AI vendor assessments are ad hoc, inconsistent, and stressful, conducted under time pressure without clear standards or tools
After
You lead structured, repeatable evaluations using a proven framework, with documentation, stakeholder alignment, and confidence in compliance

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 of total engagement, designed for completion over 8, 12 weeks with flexible pacing.

If nothing changes
Without a structured approach, organizations risk inconsistent decisions, delayed deployments, regulatory exposure, and erosion of trust in AI adoption processes.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance webinars, this program delivers implementation-grade detail specifically for third-party AI risk assessment, combining regulatory insight, technical literacy, and operational execution in one comprehensive package.

Frequently asked

Who is this course designed for?
Compliance officers, risk professionals, and governance leads responsible for evaluating third-party AI systems in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical background required?
No. The course is designed for non-engineers and includes clear explanations of technical concepts relevant to AI vendor assessment.
$199 one-time. Approximately 45, 60 hours of total engagement, designed for completion over 8, 12 weeks with flexible pacing..

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
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