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Board-Level AI Vendor Risk Assessment for Established Enterprises

$199.00
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A tailored course, built for your situation

Board-Level AI Vendor Risk Assessment for Established Enterprises

Master governance, compliance, and strategic oversight for AI vendor engagements 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.
Unclear accountability in AI vendor relationships undermines board confidence and slows innovation

The situation this course is for

As enterprises integrate AI vendors into core operations, leaders face mounting pressure to demonstrate control without stifling progress. Traditional procurement and risk frameworks fall short when applied to adaptive AI systems, creating ambiguity in liability, compliance, and oversight. Without a structured approach, teams default to reactive governance, delaying time-to-value and increasing exposure at the highest levels.

Who this is for

Senior risk, compliance, or technology leaders in established enterprises guiding AI adoption with board-level implications

Who this is not for

Startups in early product-market fit, individual contributors without cross-functional influence, or teams focused solely on building in-house AI models without third-party integration

What you walk away with

  • Apply a board-aligned risk assessment framework to AI vendor selection and oversight
  • Design clear accountability structures between legal, technical, and executive stakeholders
  • Evaluate vendor claims using standardized due diligence templates
  • Communicate risk posture confidently to non-technical leadership
  • Implement audit-ready controls that scale across vendor portfolios

The 12 modules (with all 144 chapters)

Module 1. The Evolving Role of the Board in AI Governance
Understand how board expectations are shifting in response to AI adoption and regulatory trends
12 chapters in this module
  1. From oversight to strategic engagement
  2. Emerging board-level concerns about AI
  3. Defining the scope of fiduciary duty
  4. Mapping AI risk to enterprise objectives
  5. Integrating AI into ERM frameworks
  6. Board composition and AI literacy
  7. Case study: Public company disclosures
  8. Engaging directors effectively
  9. Balancing innovation and prudence
  10. Benchmarking governance maturity
  11. Regulatory anticipation strategies
  12. Preparing quarterly reporting cycles
Module 2. AI Vendor Risk: Defining the Landscape
Establish a common taxonomy and classification system for AI vendor engagements
12 chapters in this module
  1. What makes AI vendor risk unique
  2. Differentiating AI from traditional SaaS
  3. Vendor categorization by risk tier
  4. Assessing model opacity and dependency
  5. Understanding data flow boundaries
  6. Identifying embedded AI in legacy tools
  7. Third-party model supply chains
  8. Licensing models and IP considerations
  9. Measuring vendor lock-in potential
  10. Benchmarking market offerings
  11. Emerging regulatory touchpoints
  12. Creating a vendor inventory framework
Module 3. Due Diligence Frameworks for AI Vendors
Build repeatable processes to evaluate AI vendors before engagement
12 chapters in this module
  1. Designing a structured questionnaire
  2. Evaluating model documentation standards
  3. Assessing training data provenance
  4. Reviewing bias and fairness testing
  5. Auditing model performance claims
  6. Verifying security and access controls
  7. Examining incident response plans
  8. Validating compliance certifications
  9. Assessing business continuity plans
  10. Scoring vendor responses objectively
  11. Benchmarking against peer assessments
  12. Creating a due diligence playbook
Module 4. Contractual Risk Allocation Strategies
Structure agreements that protect enterprise interests while enabling innovation
12 chapters in this module
  1. Defining liability thresholds and caps
  2. Allocating responsibility for model drift
  3. Specifying accuracy guarantees
  4. Managing indemnification clauses
  5. Addressing IP ownership conflicts
  6. Ensuring audit rights and transparency
  7. Enforcing model retraining obligations
  8. Handling data ownership disputes
  9. Negotiating exit rights and data portability
  10. Setting performance benchmarks
  11. Managing joint liability scenarios
  12. Creating adaptable contract templates
Module 5. Establishing Governance Committees
Design cross-functional teams to oversee AI vendor risk consistently
12 chapters in this module
  1. Defining committee roles and responsibilities
  2. Aligning legal, risk, and technical stakeholders
  3. Setting meeting cadence and agendas
  4. Creating escalation pathways
  5. Documenting decision rationales
  6. Integrating with existing governance bodies
  7. Onboarding new members effectively
  8. Managing distributed accountability
  9. Tracking open risks and actions
  10. Reporting to executive leadership
  11. Evaluating committee effectiveness
  12. Adapting structure over time
Module 6. Risk Scoring and Tiering Models
Implement a consistent methodology to prioritize vendor engagements
12 chapters in this module
  1. Defining risk dimensions and weights
  2. Assessing impact and likelihood independently
  3. Creating a scoring rubric
  4. Calibrating across departments
  5. Validating assumptions with real data
  6. Adjusting for organizational context
  7. Integrating with vendor management systems
  8. Automating scoring workflows
  9. Benchmarking against industry peers
  10. Updating models dynamically
  11. Communicating scores clearly
  12. Requiring remediation plans
Module 7. Ongoing Monitoring and Assurance
Maintain confidence in AI vendors throughout the lifecycle
12 chapters in this module
  1. Designing continuous monitoring protocols
  2. Tracking model performance degradation
  3. Validating periodic retraining
  4. Auditing data pipeline integrity
  5. Reviewing security incident logs
  6. Assessing changes in vendor ownership
  7. Monitoring regulatory developments
  8. Conducting surprise audits
  9. Reassessing risk tier annually
  10. Engaging third-party assessors
  11. Documenting assurance activities
  12. Reporting findings to oversight bodies
Module 8. Incident Response and Remediation
Prepare for and respond to AI-related incidents involving vendors
12 chapters in this module
  1. Defining what constitutes an AI incident
  2. Establishing notification timelines
  3. Coordinating with vendor response teams
  4. Assessing root causes and contributing factors
  5. Containing model-driven harm
  6. Communicating with stakeholders
  7. Evaluating legal exposure
  8. Initiating remediation plans
  9. Updating risk models post-incident
  10. Conducting post-mortems
  11. Enforcing vendor accountability
  12. Preventing recurrence systematically
Module 9. AI Ethics and Bias Oversight
Ensure fair, transparent, and accountable AI vendor practices
12 chapters in this module
  1. Defining organizational values for AI
  2. Evaluating vendor fairness testing
  3. Auditing for disparate impact
  4. Assessing transparency in model design
  5. Reviewing human-in-the-loop requirements
  6. Validating explainability claims
  7. Monitoring for drift in fairness metrics
  8. Engaging external auditors
  9. Publishing accountability reports
  10. Responding to bias complaints
  11. Benchmarking against industry standards
  12. Updating policies proactively
Module 10. Regulatory Readiness and Compliance
Stay ahead of evolving AI regulations across jurisdictions
12 chapters in this module
  1. Tracking global AI policy developments
  2. Mapping requirements to vendor contracts
  3. Assessing compliance with privacy laws
  4. Meeting sector-specific mandates
  5. Preparing for audits and inspections
  6. Documenting due diligence efforts
  7. Responding to regulatory inquiries
  8. Engaging legal counsel proactively
  9. Anticipating enforcement trends
  10. Aligning with international standards
  11. Updating policies as laws evolve
  12. Demonstrating good faith efforts
Module 11. Executive Communication and Reporting
Translate technical risk into strategic insights for leadership
12 chapters in this module
  1. Tailoring messages to board members
  2. Simplifying complex risk concepts
  3. Creating visual reporting dashboards
  4. Highlighting key risk indicators
  5. Balancing transparency and reassurance
  6. Preparing for Q&A sessions
  7. Aligning with strategic goals
  8. Reporting on mitigation progress
  9. Benchmarking against peers
  10. Updating risk appetite statements
  11. Facilitating informed decision-making
  12. Building trust through consistency
Module 12. Scaling Governance Across the Enterprise
Extend AI vendor risk practices across divisions and geographies
12 chapters in this module
  1. Creating central oversight functions
  2. Standardizing assessment criteria
  3. Training regional teams effectively
  4. Integrating with procurement workflows
  5. Leveraging technology platforms
  6. Reducing duplication of effort
  7. Sharing best practices globally
  8. Adapting to local regulations
  9. Measuring program maturity
  10. Optimizing resource allocation
  11. Driving continuous improvement
  12. Celebrating governance wins

How this maps to your situation

  • Enterprise AI adoption accelerating without clear oversight
  • Boards demanding greater assurance on AI vendor engagements
  • Regulatory scrutiny increasing on third-party AI use
  • Organizations seeking to standardize risk assessment at scale

Before vs. after

Before
Uncertainty about how to assess and manage AI vendor risk at the board level, leading to inconsistent practices and delayed decisions
After
Confidence in applying a structured, repeatable framework to evaluate, govern, and report on AI vendor engagements across the enterprise

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 flexible, self-paced learning over 8-12 weeks.

If nothing changes
Continuing without a formalized approach may result in inconsistent risk evaluations, increased exposure to regulatory scrutiny, and erosion of board confidence in AI initiatives.

How this compares to the alternatives

Unlike generic AI ethics courses or vendor-specific training, this program focuses exclusively on board-level risk assessment for third-party AI in established enterprises, combining governance strategy with implementation-grade tools.

Frequently asked

Who is this course designed for?
Senior risk, compliance, and technology leaders in established enterprises who guide AI vendor governance and board-level reporting.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is text-based with downloadable templates and a hand-built implementation playbook to support practical application.
$199 one-time. Approximately 4-6 hours per module, designed for flexible, self-paced learning over 8-12 weeks..

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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