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Board-Level AI Vendor Risk Assessment for Senior Leaders

$199.00
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A tailored course, built for your situation

Board-Level AI Vendor Risk Assessment for Senior Leaders

Master the governance, risk, and compliance frameworks shaping AI adoption at scale

$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.
Navigating AI vendor risk without a structured, board-aligned framework can slow innovation and increase exposure

The situation this course is for

Senior leaders are being asked to assess AI vendors quickly, but often lack standardized tools to evaluate risk across legal, operational, and ethical dimensions. Without a unified approach, decisions become reactive, inconsistent, or overly centralized, hindering agility and trust.

Who this is for

Business and technology leaders in regulated environments who influence or own vendor evaluation, AI governance, or enterprise risk strategy

Who this is not for

This is not for individual contributors focused only on technical AI implementation or for teams seeking off-the-shelf risk software tools

What you walk away with

  • Apply a board-ready framework to assess AI vendor risk across legal, ethical, and operational domains
  • Differentiate between commodity and critical AI vendors using risk tiering models
  • Lead cross-functional assessments with legal, compliance, security, and procurement teams
  • Communicate risk posture and mitigation plans effectively to executive and board audiences
  • Deploy a repeatable vendor evaluation playbook tailored to your organization’s risk appetite

The 12 modules (with all 144 chapters)

Module 1. The Evolving Role of Boards in AI Governance
Understand how board responsibilities are expanding to include AI oversight and strategic risk stewardship
12 chapters in this module
  1. From passive to proactive board engagement
  2. Regulatory expectations for board-level AI literacy
  3. Case studies in board-led AI interventions
  4. Linking AI risk to enterprise risk management
  5. Board committee structures for technology oversight
  6. Defining fiduciary duty in AI decision-making
  7. Emerging disclosure requirements
  8. Benchmarking board maturity in AI governance
  9. Aligning AI strategy with corporate purpose
  10. Managing escalation pathways for AI incidents
  11. Balancing innovation velocity with risk tolerance
  12. Preparing board members for AI vendor reviews
Module 2. AI Vendor Risk Classification Frameworks
Build a taxonomy to categorize AI vendors by impact, autonomy, and data sensitivity
12 chapters in this module
  1. Principles of risk-based vendor segmentation
  2. High-impact vs. low-impact AI use cases
  3. Autonomy levels in AI decision systems
  4. Data classification and residency implications
  5. Scoring models for algorithmic transparency
  6. Vendor lock-in and exit strategy risks
  7. Third-party dependency mapping
  8. Open source vs. proprietary AI components
  9. Supply chain transparency requirements
  10. Model drift and performance decay monitoring
  11. Human-in-the-loop necessity assessments
  12. Creating dynamic risk heatmaps
Module 3. Due Diligence Workflows for AI Vendors
Design and deploy standardized assessment processes across legal, technical, and operational domains
12 chapters in this module
  1. Stages of AI vendor due diligence
  2. Pre-RFP risk screening checklists
  3. Request for Information (RFI) optimization
  4. Evaluating model development lifecycle practices
  5. Assessing training data provenance and bias
  6. Reviewing model validation and testing rigor
  7. Auditing third-party AI certifications
  8. Security posture evaluation for AI platforms
  9. Incident response and breach notification readiness
  10. Change management and version control policies
  11. Service level agreements for AI reliability
  12. Right-to-audit clauses and enforcement
Module 4. Ethical and Bias Risk Assessment
Evaluate fairness, accountability, and transparency in vendor AI systems
12 chapters in this module
  1. Defining ethical AI in financial services contexts
  2. Bias detection across demographic groups
  3. Fairness metrics and benchmarking
  4. Explainability requirements for stakeholders
  5. Impact assessment for vulnerable populations
  6. Ongoing monitoring for discriminatory outcomes
  7. Redress mechanisms for affected parties
  8. Vendor commitments to algorithmic equity
  9. Third-party bias audit readiness
  10. Transparency in model documentation
  11. Handling contested AI decisions
  12. Building public trust through ethical rigor
Module 5. Regulatory and Compliance Alignment
Ensure AI vendor strategies align with current and emerging regulatory expectations
12 chapters in this module
  1. Global regulatory landscape for AI
  2. U.S. federal and state-level AI guidance
  3. Compliance with fair lending and anti-discrimination laws
  4. Data privacy regulations affecting AI vendors
  5. Sector-specific rules in financial services
  6. Preparing for AI-specific regulatory exams
  7. Documentation standards for audit trails
  8. Cross-border data transfer compliance
  9. Vendor adherence to model risk management (MRM)
  10. Regulatory sandboxes and innovation offices
  11. Engaging with regulators on AI initiatives
  12. Future-proofing against regulatory shifts
Module 6. Third-Party Risk Management Integration
Embed AI vendor assessments into existing third-party risk frameworks
12 chapters in this module
  1. Mapping AI vendors to existing TPRM categories
  2. Extending vendor lifecycle management to AI
  3. Onboarding workflows for AI-specific risks
  4. Ongoing monitoring and key risk indicators
  5. Performance evaluation beyond uptime metrics
  6. Contractual risk transfer mechanisms
  7. Insurance coverage for AI-related incidents
  8. Exit planning and data portability
  9. Sub-vendor and supply chain visibility
  10. Centralized vendor risk dashboards
  11. Automating risk assessment updates
  12. Maintaining independence in vendor oversight
Module 7. Model Risk Management for External AI
Apply model risk principles to externally sourced AI systems
12 chapters in this module
  1. Extending MRM frameworks to vendor models
  2. Independent validation of third-party models
  3. Benchmarking against internal baselines
  4. Model performance decay detection
  5. Backtesting and stress testing approaches
  6. Scenario analysis for edge cases
  7. Documentation requirements for vendor models
  8. Change control and revalidation triggers
  9. Model inventory and registry practices
  10. Governance of ensemble and composite models
  11. Handling black-box vendor models
  12. Escalation paths for model failures
Module 8. Data Governance and Privacy in AI Vendors
Assess how vendors handle data sourcing, usage, and protection
12 chapters in this module
  1. Data provenance and lineage tracking
  2. Consent management in training data
  3. Synthetic data use and limitations
  4. PII detection and de-identification practices
  5. Data minimization in AI systems
  6. Right to be forgotten implementation
  7. Data retention and deletion policies
  8. Cross-functional data governance alignment
  9. Vendor access controls and logging
  10. Data breach response coordination
  11. Data subject request fulfillment
  12. Auditing data practices at scale
Module 9. Incident Response and Escalation Planning
Prepare for AI-related incidents with clear vendor coordination protocols
12 chapters in this module
  1. Defining AI incident types and severity levels
  2. Joint incident response playbooks with vendors
  3. Notification timelines and regulatory reporting
  4. Containment strategies for AI malfunctions
  5. Root cause analysis for algorithmic errors
  6. Reputational risk management during crises
  7. Customer communication frameworks
  8. Regulatory engagement during incidents
  9. Post-incident review and remediation
  10. Vendor accountability for incident resolution
  11. Escalation to board or executive leadership
  12. Stress testing incident response plans
Module 10. Board Communication and Reporting Strategies
Translate technical risk assessments into strategic insights for governance bodies
12 chapters in this module
  1. Tailoring risk messages for board audiences
  2. Visualizing AI risk exposure clearly
  3. Balancing transparency with confidentiality
  4. Reporting frequency and cadence decisions
  5. Highlighting strategic implications of risk
  6. Connecting AI risk to financial impact
  7. Presenting mitigation progress and gaps
  8. Preparing for board Q&A on AI vendors
  9. Building board-level risk dashboards
  10. Framing risk as enabler of innovation
  11. Documenting board oversight activities
  12. Evolving reporting as AI maturity grows
Module 11. Cross-Functional Alignment and Stakeholder Engagement
Orchestrate collaboration across legal, compliance, IT, security, and business units
12 chapters in this module
  1. Identifying key stakeholders in AI vendor reviews
  2. Designing RACI matrices for assessments
  3. Facilitating cross-functional workshops
  4. Resolving conflicting stakeholder priorities
  5. Building shared definitions of risk
  6. Creating centralized assessment repositories
  7. Standardizing feedback collection processes
  8. Managing timelines across departments
  9. Engaging business units in risk ownership
  10. Training teams on AI risk fundamentals
  11. Scaling coordination without bureaucracy
  12. Measuring alignment and decision quality
Module 12. Sustainable AI Vendor Governance Programs
Establish ongoing, adaptive governance structures that evolve with technology and risk
12 chapters in this module
  1. From project-based to program-based governance
  2. Defining operating model for AI oversight
  3. Staffing and resourcing considerations
  4. Continuous improvement through feedback loops
  5. Benchmarking against industry peers
  6. Updating policies and frameworks regularly
  7. Incorporating lessons from incidents
  8. Scaling governance with AI adoption
  9. Measuring program effectiveness
  10. Securing executive sponsorship
  11. Integrating with enterprise risk appetite
  12. Future trends in AI governance

How this maps to your situation

  • Board is asking questions about AI vendor risk but no formal process exists
  • Multiple departments assess vendors inconsistently
  • Recent vendor incident highlighted gaps in oversight
  • Preparing for regulatory scrutiny on third-party AI use

Before vs. after

Before
AI vendor assessments are fragmented, reactive, and lack board-level alignment
After
You lead a structured, repeatable, and board-ready AI vendor risk program

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 minutes per module, designed for busy leaders to progress at their own pace.

If nothing changes
Without a formal approach, organizations risk inconsistent decisions, regulatory scrutiny, reputational damage, and missed opportunities to shape AI strategy proactively.

How this compares to the alternatives

Unlike generic risk management courses, this program focuses exclusively on AI vendor risk with board-level communication, implementation templates, and financial services context built in.

Frequently asked

Who is this course designed for?
Senior business and technology leaders responsible for AI governance, vendor risk, compliance, or strategic technology oversight in regulated environments.
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 assessments.
$199 one-time. Approximately 45, 60 minutes per module, designed for busy leaders to progress at their own pace..

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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