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Board-Level AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Board-Level AI Vendor Risk Assessment for Regulated Industries

Master governance-ready AI vendor evaluation with implementation-grade frameworks

$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 contracts moving faster than risk frameworks can keep up

The situation this course is for

Regulated organizations are adopting third-party AI rapidly, but lack standardized, board-reportable methods to assess vendor integrity, compliance, and long-term operational resilience. This creates governance gaps even when technical performance meets expectations.

Who this is for

Compliance officers, risk managers, legal advisors, and technology leaders in financial services, healthcare, utilities, and other highly regulated sectors overseeing AI procurement and vendor due diligence

Who this is not for

Individual contributors not involved in vendor assessment, practitioners focused solely on model development, or teams operating outside regulated environments

What you walk away with

  • Apply a board-aligned framework to evaluate AI vendor risk across 12 critical dimensions
  • Produce auditable assessment reports that meet regulatory scrutiny
  • Integrate vendor risk checklists into procurement workflows
  • Anticipate emerging compliance requirements with forward-looking control design
  • Lead cross-functional vendor reviews with confidence and clarity

The 12 modules (with all 144 chapters)

Module 1. The Shift to Board-Level AI Oversight
Understand how AI governance has become a fiduciary concern
12 chapters in this module
  1. From IT procurement to board accountability
  2. Regulatory drivers shaping AI vendor expectations
  3. Case studies in governance failure
  4. Emerging standards in AI due diligence
  5. Roles and responsibilities across risk functions
  6. Defining 'reasonable assurance' in AI sourcing
  7. Mapping vendor risk to enterprise risk appetite
  8. Board reporting cycles and risk escalation paths
  9. Linking AI risk to financial controls
  10. Vendor lifecycle stages and governance touchpoints
  11. Balancing innovation speed with oversight rigor
  12. Establishing governance maturity baselines
Module 2. Legal and Regulatory Foundations
Identify binding and emerging obligations in AI procurement
12 chapters in this module
  1. Sector-specific compliance requirements
  2. Jurisdictional variation in AI regulation
  3. Contractual obligations and liability boundaries
  4. Data protection in third-party AI systems
  5. Audit rights and transparency expectations
  6. Export controls and national security implications
  7. Intellectual property in trained models
  8. Liability for AI-generated outputs
  9. Regulatory sandboxes and pre-compliance engagement
  10. Cross-border data flows and model hosting
  11. Enforcement trends in vendor oversight
  12. Future-proofing against regulatory change
Module 3. Vendor Risk Taxonomy Development
Build a custom classification system for AI-specific risks
12 chapters in this module
  1. Categorizing technical vs operational risk
  2. Identifying hidden dependencies in AI services
  3. Mapping supply chain transparency gaps
  4. Assessing model lineage and data provenance
  5. Evaluating retraining and drift management
  6. Scoring model interpretability and explainability
  7. Measuring resilience to adversarial inputs
  8. Vendor lock-in and exit strategy risks
  9. Third-party subprocessor oversight
  10. Geopolitical exposure in AI infrastructure
  11. Workforce stability and key person risk
  12. Cybersecurity maturity of vendor organizations
Module 4. Due Diligence Process Design
Structure repeatable, auditable assessment workflows
12 chapters in this module
  1. Designing multi-stage vendor review gates
  2. Creating standardized request for information templates
  3. Scoping on-site and remote assessments
  4. Integrating legal, security, and compliance reviews
  5. Establishing cross-functional review teams
  6. Setting risk-based thresholds for escalation
  7. Documenting rationale for approval or rejection
  8. Maintaining assessment archives for audit
  9. Versioning control across procurement cycles
  10. Automating evidence collection where possible
  11. Aligning with internal control frameworks
  12. Continuous monitoring triggers and thresholds
Module 5. Technical Evaluation Frameworks
Assess AI systems using implementation-grade checklists
12 chapters in this module
  1. Model card and system card interpretation
  2. Evaluating training data sourcing and bias mitigation
  3. Performance metrics across use-case contexts
  4. Robustness testing under edge conditions
  5. API security and integration risk
  6. Monitoring for concept and data drift
  7. Fail-safe and fallback mechanism review
  8. Computational efficiency and scalability
  9. Latency and reliability SLA validation
  10. Redundancy and disaster recovery design
  11. Model versioning and update protocols
  12. Patch management and vulnerability response
Module 6. Data Governance and Provenance
Ensure data integrity across AI vendor ecosystems
12 chapters in this module
  1. Data lineage tracking in third-party models
  2. Consent and licensing verification
  3. Anonymization and re-identification risk
  4. Data ownership and usage rights
  5. Retention and deletion obligations
  6. Cross-system data leakage prevention
  7. Audit trail completeness and access
  8. Data minimization in model design
  9. Labeling process transparency
  10. Synthetic data use and validation
  11. Data poisoning and contamination risks
  12. Vendor data handling certifications
Module 7. Model Explainability and Auditability
Evaluate transparency without requiring full model disclosure
12 chapters in this module
  1. Right to explanation in regulated contexts
  2. Local vs global interpretability methods
  3. Performance parity across demographic groups
  4. Bias detection and correction mechanisms
  5. Counterfactual explanations for decisions
  6. Feature importance and sensitivity analysis
  7. Audit trail generation for model outputs
  8. Third-party model inspection tools
  9. Explainability in low-data environments
  10. Documentation standards for regulators
  11. User-facing transparency requirements
  12. Trade secrets vs accountability balance
Module 8. Cybersecurity and Resilience
Assess AI vendor cyber posture with precision
12 chapters in this module
  1. Red teaming AI systems and APIs
  2. Model inversion and membership inference risks
  3. Secure model deployment practices
  4. Encryption in transit and at rest
  5. Access control and privilege management
  6. Incident response planning for AI outages
  7. Threat modeling AI-specific attack vectors
  8. Vendor penetration testing disclosures
  9. Security certification validation
  10. Zero-day vulnerability management
  11. Supply chain software integrity
  12. Resilience testing under denial-of-service
Module 9. Operational Continuity and Exit Planning
Ensure long-term resilience and smooth transitions
12 chapters in this module
  1. Service continuity and redundancy design
  2. Vendor financial health monitoring
  3. Exit clauses and data portability
  4. Re-training in-house or with new vendor
  5. Knowledge transfer requirements
  6. Model documentation completeness
  7. Fallback process design and testing
  8. Contractual termination triggers
  9. Data repatriation timelines
  10. Third-party escrow for model assets
  11. Transition cost estimation
  12. Maintaining compliance during migration
Module 10. Board Communication and Reporting
Translate technical risk into strategic insight
12 chapters in this module
  1. Risk summary dashboards for executives
  2. Translating technical findings into business impact
  3. Scenario planning for vendor failure
  4. Benchmarking against industry peers
  5. Reporting frequency and escalation paths
  6. Visualizing risk exposure over time
  7. Linking AI risk to financial performance
  8. Aligning with ESG and sustainability reporting
  9. Executive briefing templates
  10. Preparing for regulatory inquiries
  11. Balancing transparency with confidentiality
  12. Updating board materials quarterly
Module 11. Cross-Functional Collaboration Models
Align legal, risk, IT, and business teams effectively
12 chapters in this module
  1. Defining RACI matrices for vendor review
  2. Synchronizing review timelines across departments
  3. Creating shared risk language and definitions
  4. Conflict resolution in high-stakes decisions
  5. Legal sign-off workflows
  6. Security team integration points
  7. Compliance monitoring handoffs
  8. Business unit input in scoring
  9. Vendor negotiation boundaries
  10. Feedback loops for process improvement
  11. Training non-technical reviewers
  12. Maintaining consistency across regions
Module 12. Future-Proofing and Adaptive Governance
Design systems that evolve with regulatory and technical shifts
12 chapters in this module
  1. Monitoring emerging AI regulations
  2. Adaptive risk frameworks and control updates
  3. Scenario planning for disruptive change
  4. AI ethics board coordination
  5. Updating assessment criteria annually
  6. Benchmarking against evolving standards
  7. Incorporating lessons from incidents
  8. Scaling frameworks across vendor portfolios
  9. Investing in internal capability development
  10. Public reporting and stakeholder trust
  11. Long-term AI governance roadmap
  12. Sunset planning for legacy AI systems

How this maps to your situation

  • New AI vendor contract under review
  • Board request for AI risk posture summary
  • Regulatory audit preparation
  • Post-incident vendor reassessment

Before vs. after

Before
Uncertainty in vendor evaluations, inconsistent reporting, and reactive risk responses
After
Structured, repeatable, and board-ready AI vendor risk assessments with auditable documentation

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 4-6 hours per module, designed for asynchronous, self-paced learning with practical application between sections.

If nothing changes
Without a formalized approach, organizations risk non-compliance, reputational damage, and board-level accountability gaps when AI vendor incidents occur.

How this compares to the alternatives

Unlike general AI ethics courses or technical model auditing guides, this program focuses specifically on board-level vendor risk in regulated environments, combining legal, operational, and technical due diligence into a single actionable framework.

Frequently asked

Who is this course designed for?
Compliance, risk, legal, and technology leaders in regulated industries evaluating third-party AI solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if we haven't adopted AI yet?
Yes, this prepares your team to assess vendors confidently when adoption begins.
$199 one-time. Approximately 4-6 hours per module, designed for asynchronous, self-paced learning with practical application between sections..

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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