Skip to main content
Image coming soon

Enterprise-Class AI Vendor Risk Assessment for Risk-Adverse Boards

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Enterprise-Class AI Vendor Risk Assessment for Risk-Adverse Boards

A 12-module implementation-grade course for business and technology leaders navigating board-level 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.
Board-level scrutiny of AI initiatives is increasing, but most risk assessments lack the depth and structure to satisfy governance expectations.

The situation this course is for

AI vendor decisions are being elevated to the boardroom, yet teams often rely on checklists that don't address model provenance, third-party liability, or long-term compliance drift. This creates friction, delays, and erodes trust in technical leadership.

Who this is for

Business and technology professionals responsible for AI governance, vendor risk, compliance, or technology strategy who need to deliver credible, board-ready assessments.

Who this is not for

This course is not for engineers seeking hands-on coding labs or vendors promoting their own tools. It is not an introductory overview of AI ethics.

What you walk away with

  • Apply a structured framework to assess AI vendors across 12 risk dimensions
  • Translate technical risk findings into executive-ready board summaries
  • Negotiate vendor contracts with embedded risk mitigations
  • Build repeatable assessment workflows that scale across the organization
  • Anticipate and respond to board-level questions with confidence

The 12 modules (with all 144 chapters)

Module 1. The Evolving Role of the Board in AI Governance
Understand how board expectations for AI oversight have shifted and what drives current scrutiny.
12 chapters in this module
  1. From innovation oversight to risk stewardship
  2. Key drivers of board-level AI inquiry
  3. Regulatory signals shaping governance appetite
  4. Case study: Board intervention in AI procurement
  5. Defining the scope of board accountability
  6. Mapping board questions to risk domains
  7. The rise of the AI risk committee
  8. Balancing innovation speed and governance rigor
  9. Engagement models between board and technical teams
  10. Benchmarking governance maturity across sectors
  11. Signals that trigger deeper board involvement
  12. Preparing the first AI risk briefing for directors
Module 2. Foundations of Enterprise AI Risk
Establish a common language and taxonomy for AI-related risks across technical, legal, and operational domains.
12 chapters in this module
  1. What makes AI risk different from traditional IT risk
  2. Model lifecycle risks from training to decommissioning
  3. Data provenance and lineage in third-party models
  4. Bias, fairness, and unintended outcomes
  5. Model drift and performance degradation
  6. Explainability requirements across use cases
  7. Geopolitical risks in AI supply chains
  8. Intellectual property and model ownership
  9. Third-party dependency risk stacking
  10. Incident response for AI-enabled systems
  11. Reputational risk from AI failures
  12. Insurance and liability coverage gaps
Module 3. Vendor Risk Assessment Frameworks
Compare and select the right frameworks for enterprise-class AI vendor evaluation.
12 chapters in this module
  1. NIST AI RMF: Core principles and application
  2. ISO/IEC 42001 and its role in vendor contracts
  3. SOC 2 for AI: Limitations and extensions
  4. Customizing frameworks for organizational context
  5. Weighting risk domains by business impact
  6. Integrating AI risk into existing vendor management
  7. Third-party audit readiness for AI vendors
  8. Using control maturity models for scoring
  9. Benchmarking against peer organizations
  10. Adapting frameworks for high-regulation sectors
  11. Mapping controls to board reporting needs
  12. Versioning and updating assessment frameworks
Module 4. Model Transparency and Documentation
Evaluate vendor model cards, data sheets, and system documentation for completeness and credibility.
12 chapters in this module
  1. What a model card should include
  2. Assessing data provenance and annotation practices
  3. Evaluating training data representativeness
  4. Detecting red flags in model performance claims
  5. Understanding evaluation methodology transparency
  6. Third-party model auditing feasibility
  7. Version control and update transparency
  8. Model decommissioning and sunset policies
  9. Human oversight mechanisms in model operation
  10. Incident reporting and model rollback procedures
  11. Assessing model card completeness scores
  12. Requesting supplemental documentation
Module 5. Data Governance and Privacy Compliance
Assess how AI vendors handle data across jurisdictions, consent models, and retention policies.
12 chapters in this module
  1. Data flow mapping in AI vendor systems
  2. Consent and lawful basis for training data
  3. Cross-border data transfer mechanisms
  4. Right to deletion and model retraining
  5. PIA and DPIA integration with AI procurement
  6. Anonymization and synthetic data use
  7. Data minimization in model design
  8. Vendor access to customer data
  9. Subprocessor transparency and control
  10. Breach notification timelines and obligations
  11. Data subject request fulfillment processes
  12. Auditing data practices post-contract
Module 6. Security and Resilience Controls
Evaluate AI vendor security posture beyond standard certifications.
12 chapters in this module
  1. Adversarial attacks on machine learning models
  2. Model inversion and membership inference risks
  3. Secure model deployment and inference
  4. API security for AI services
  5. Model poisoning and training data attacks
  6. Zero-trust integration with vendor systems
  7. Incident response playbooks for AI breaches
  8. Penetration testing AI interfaces
  9. Secure update and patching cycles
  10. Monitoring for anomalous model behavior
  11. Encryption of models and data in transit and at rest
  12. Third-party vulnerability disclosure policies
Module 7. Contractual Risk Mitigation
Structure agreements that enforce risk controls and enable accountability.
12 chapters in this module
  1. Defining model performance guarantees
  2. Service level objectives for AI systems
  3. Liability for harmful model outputs
  4. Indemnification for IP and compliance failures
  5. Right to audit and inspection clauses
  6. Exit strategies and data portability
  7. Model retraining and drift correction obligations
  8. Change control and version notification
  9. Subcontractor approval processes
  10. Termination for ethical or risk violations
  11. Dispute resolution for model performance
  12. Insurance requirements for AI vendors
Module 8. Bias, Fairness, and Ethical Risk
Assess vendor approaches to fairness, equity, and ethical alignment.
12 chapters in this module
  1. Defining fairness metrics for specific use cases
  2. Bias testing across demographic groups
  3. Fairness in training data sampling
  4. Vendor accountability for discriminatory outcomes
  5. Ethics review boards and oversight
  6. Stakeholder consultation practices
  7. Handling edge cases and vulnerable populations
  8. Transparency in ethical trade-offs
  9. Redress mechanisms for affected individuals
  10. Monitoring for disparate impact post-deployment
  11. Auditing fairness claims with third parties
  12. Documenting ethical risk acceptance
Module 9. Operational Risk and Integration
Evaluate how AI vendor systems integrate into existing operations and create long-term dependencies.
12 chapters in this module
  1. Integration complexity and technical debt
  2. Monitoring and observability capabilities
  3. Vendor support response times and SLAs
  4. Documentation quality and accessibility
  5. Training and knowledge transfer
  6. Single points of failure in vendor architecture
  7. Disaster recovery and business continuity
  8. Model performance under load
  9. Scalability and usage limits
  10. Customization vs. configuration trade-offs
  11. Vendor roadmap alignment with business needs
  12. Long-term sustainability of vendor business
Module 10. Regulatory and Compliance Alignment
Ensure vendor practices align with current and emerging legal requirements.
12 chapters in this module
  1. GDPR and AI: Automated decision-making rules
  2. CCPA and opt-out rights for profiling
  3. NYDFS and financial services AI rules
  4. EU AI Act: High-risk classification and obligations
  5. Sector-specific regulations (healthcare, finance, education)
  6. Accessibility requirements for AI interfaces
  7. Truth-in-advertising for AI capabilities
  8. Export controls on AI technologies
  9. Workplace surveillance and employee rights
  10. Children's data and AI interactions
  11. Political and electoral use restrictions
  12. Preparing for upcoming AI legislation
Module 11. Board Communication and Reporting
Translate technical assessments into clear, actionable insights for directors.
12 chapters in this module
  1. Structuring the AI risk dashboard
  2. Visualizing risk exposure trends
  3. Narrative framing for risk tolerance
  4. Scenario planning for board discussion
  5. Presenting vendor risk comparisons
  6. Linking AI risk to strategic objectives
  7. Defining escalation triggers
  8. Reporting frequency and format
  9. Engaging non-technical directors
  10. Balancing transparency and confidentiality
  11. Documenting board risk acceptance
  12. Annual AI risk posture summaries
Module 12. Scaling and Institutionalizing AI Risk Assessment
Build organization-wide capability for consistent, repeatable AI vendor evaluation.
12 chapters in this module
  1. Centralizing AI risk ownership
  2. Creating cross-functional assessment teams
  3. Standardizing intake and review workflows
  4. Building a vendor risk knowledge base
  5. Training business units on risk criteria
  6. Integrating with procurement systems
  7. Automating risk scoring and reporting
  8. Continuous monitoring of active vendors
  9. Benchmarking across the vendor portfolio
  10. Feedback loops from operations to procurement
  11. Updating frameworks based on incidents
  12. Maturity model for AI risk programs

How this maps to your situation

  • When board members ask for AI risk briefings
  • During due diligence for new AI vendor procurement
  • When updating enterprise risk management frameworks
  • When responding to regulatory inquiries about AI use

Before vs. after

Before
Assessing AI vendors feels reactive, inconsistent, and disconnected from board expectations.
After
You lead with a structured, credible, and repeatable process that earns board confidence and accelerates 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 60 hours of total engagement, designed for completion over 8, 10 weeks with flexible pacing.

If nothing changes
Organizations that lack formal AI vendor risk practices may face delayed deployments, regulatory scrutiny, or loss of board trust when incidents occur.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level overviews, this program delivers implementation-grade tools, real-world templates, and board-focused communication strategies not found in public frameworks or vendor-led training.

Frequently asked

Who is this course designed for?
It's for business and technology professionals responsible for AI governance, vendor risk, compliance, or technology strategy who need to deliver credible, board-ready assessments.
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 60 hours of total engagement, designed for completion over 8, 10 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
!