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Risk-Managed AI Vendor Risk Assessment for Senior Leaders

$199.00
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A tailored course, built for your situation

Risk-Managed AI Vendor Risk Assessment for Senior Leaders

Master governance, due diligence, and oversight of AI vendors with confidence and clarity

$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 partnerships without a structured risk framework can lead to oversight gaps and misaligned expectations

The situation this course is for

Senior leaders are increasingly accountable for AI vendor decisions but often lack standardized methods to assess risk exposure, validate controls, or ensure compliance alignment. This creates friction in procurement, delays in deployment, and uncertainty at the board level.

Who this is for

Senior leaders in business, technology, compliance, or risk functions responsible for overseeing AI vendor selection, integration, and governance

Who this is not for

Individual contributors focused solely on coding, non-managerial IT staff, or vendors selling AI tools without governance oversight responsibilities

What you walk away with

  • Apply a standardized framework to evaluate AI vendor risk posture
  • Design due diligence processes that align with organizational risk appetite
  • Structure contracts and SLAs with built-in compliance and audit readiness
  • Communicate vendor risk posture clearly to executive leadership and board members
  • Implement continuous monitoring strategies for ongoing vendor performance and security

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Introduce core concepts, terminology, and the evolving landscape of third-party AI risk.
12 chapters in this module
  1. Defining AI vendor risk in modern organizations
  2. Key differences between traditional and AI-driven vendor assessments
  3. Regulatory expectations and industry benchmarks
  4. The role of senior leadership in vendor governance
  5. Mapping AI use cases to risk profiles
  6. Common misconceptions about AI vendor security
  7. Vendor lifecycle stages and risk touchpoints
  8. Organizational roles in vendor oversight
  9. Integrating AI risk into enterprise risk management
  10. Case study: Early-stage AI vendor misalignment
  11. Building a cross-functional assessment team
  12. Self-assessment: Current vendor risk maturity
Module 2. Due Diligence Framework Design
Develop a repeatable process for evaluating AI vendors before engagement.
12 chapters in this module
  1. Creating a vendor assessment charter
  2. Defining scope and objectives for due diligence
  3. Developing risk-based evaluation criteria
  4. Leveraging existing frameworks (e.g., NIST, ISO)
  5. Assessing data handling and privacy practices
  6. Evaluating model transparency and explainability
  7. Reviewing AI ethics and bias mitigation plans
  8. Validating security and infrastructure claims
  9. Analyzing vendor resilience and incident response
  10. Benchmarking against peer organizations
  11. Documenting findings and escalation paths
  12. Template: Due diligence checklist
Module 3. Contract Architecture and SLAs
Structure agreements that enforce accountability and performance standards.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Defining model performance guarantees
  3. Establishing data ownership and usage rights
  4. Setting boundaries for retraining and updates
  5. Incorporating audit and inspection rights
  6. Addressing liability and indemnification
  7. Exit strategy and data portability terms
  8. SLA design for AI service levels
  9. Penalty structures for non-compliance
  10. Managing intellectual property rights
  11. Handling jurisdictional and legal conflicts
  12. Template: Contract clause library
Module 4. Security and Compliance Validation
Verify that vendors meet security, compliance, and regulatory requirements.
12 chapters in this module
  1. Assessing SOC 2, ISO 27001, and other certifications
  2. Conducting independent third-party audits
  3. Evaluating penetration testing results
  4. Reviewing access control and identity management
  5. Analyzing encryption and key management
  6. Validating data residency and transfer policies
  7. GDPR, CCPA, and other privacy law alignment
  8. AI-specific compliance considerations
  9. Third-party attestation processes
  10. Continuous compliance monitoring tools
  11. Incident reporting expectations
  12. Template: Security validation scorecard
Module 5. Model Risk and Performance Oversight
Establish methods to monitor AI model behavior post-deployment.
12 chapters in this module
  1. Defining model risk thresholds
  2. Performance benchmarking over time
  3. Detecting model drift and degradation
  4. Monitoring for bias and fairness shifts
  5. Establishing retraining triggers
  6. Human-in-the-loop oversight design
  7. Version control and change tracking
  8. Shadow model validation strategies
  9. Alerting and escalation protocols
  10. Audit trails for model decisions
  11. Vendor transparency requirements
  12. Template: Model performance dashboard
Module 6. Data Governance and Lineage
Ensure transparency and control over data used in AI systems.
12 chapters in this module
  1. Mapping data flows in AI pipelines
  2. Verifying data quality and completeness
  3. Tracking data provenance and lineage
  4. Assessing training data representativeness
  5. Detecting data leakage risks
  6. Data minimization and retention policies
  7. Consent and opt-out mechanisms
  8. Synthetic data usage and limitations
  9. Data labeling and annotation practices
  10. Vendor data handling audits
  11. Right-to-explanation implications
  12. Template: Data governance questionnaire
Module 7. Ethics and Bias Mitigation
Integrate ethical principles into vendor assessment and oversight.
12 chapters in this module
  1. Identifying high-risk AI use cases
  2. Assessing fairness and equity frameworks
  3. Reviewing bias detection methodologies
  4. Evaluating diversity in training data
  5. Monitoring for disparate impact
  6. Establishing redress mechanisms
  7. Ethics review board requirements
  8. Transparency in algorithmic decision-making
  9. Stakeholder communication plans
  10. Handling edge cases and exceptions
  11. Vendor ethics audit processes
  12. Template: Bias impact assessment
Module 8. Resilience and Business Continuity
Evaluate vendor readiness for disruptions and long-term sustainability.
12 chapters in this module
  1. Assessing vendor financial health
  2. Reviewing disaster recovery plans
  3. Testing failover and redundancy capabilities
  4. Evaluating supply chain dependencies
  5. Monitoring for vendor lock-in risks
  6. Exit strategy and transition planning
  7. Data escrow and source code access
  8. Force majeure and termination clauses
  9. Single points of failure identification
  10. Alternative vendor benchmarking
  11. Long-term support commitments
  12. Template: Business continuity checklist
Module 9. Board and Executive Reporting
Translate technical risk into strategic insights for leadership.
12 chapters in this module
  1. Translating AI risk for non-technical leaders
  2. Designing executive dashboards
  3. Reporting risk appetite alignment
  4. Communicating incident response readiness
  5. Summarizing audit findings clearly
  6. Highlighting key risk indicators (KRIs)
  7. Balancing innovation with prudence
  8. Benchmarking against industry peers
  9. Updating board members regularly
  10. Managing reputational risk narratives
  11. Preparing for regulatory inquiries
  12. Template: Board-level risk report
Module 10. Continuous Monitoring and Audit
Implement ongoing oversight to maintain compliance and performance.
12 chapters in this module
  1. Designing continuous monitoring workflows
  2. Automating risk signal detection
  3. Scheduling periodic reassessments
  4. Tracking vendor KPIs and SLAs
  5. Conducting surprise audits
  6. Leveraging third-party monitoring tools
  7. Updating risk profiles dynamically
  8. Managing vendor relationship changes
  9. Handling vendor mergers or acquisitions
  10. Revising contracts as needs evolve
  11. Documenting audit trails
  12. Template: Continuous monitoring calendar
Module 11. Cross-Functional Collaboration
Align legal, compliance, IT, and business teams around vendor risk.
12 chapters in this module
  1. Defining team roles and responsibilities
  2. Establishing governance councils
  3. Creating escalation protocols
  4. Facilitating joint assessments
  5. Standardizing communication templates
  6. Managing conflicting priorities
  7. Building shared risk lexicons
  8. Training teams on AI-specific risks
  9. Coordinating with procurement
  10. Integrating with vendor management platforms
  11. Resolving interdepartmental disputes
  12. Template: Cross-functional RACI chart
Module 12. Implementation and Scaling
Deploy and scale the framework across the organization.
12 chapters in this module
  1. Piloting the assessment process
  2. Gaining leadership buy-in
  3. Training assessors and reviewers
  4. Integrating with existing GRC tools
  5. Scaling across vendor portfolios
  6. Measuring program effectiveness
  7. Updating frameworks with new threats
  8. Sharing best practices across units
  9. Creating feedback loops
  10. Benchmarking maturity over time
  11. Future-proofing for emerging AI models
  12. Template: Implementation roadmap

How this maps to your situation

  • Leaders facing pressure to adopt AI faster while maintaining control
  • Organizations expanding AI vendor portfolios without standardized oversight
  • Compliance teams needing structured due diligence for audits
  • Executives preparing for board-level AI risk discussions

Before vs. after

Before
Uncertainty in evaluating AI vendors, inconsistent due diligence, and fragmented oversight across teams
After
Confidence in vendor selection, standardized assessment processes, and clear executive reporting on AI risk

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 36 hours total , designed for self-paced learning with practical application exercises.

If nothing changes
Without structured vendor risk practices, organizations may face compliance gaps, reputational damage, or operational failures during AI integration , especially as regulatory scrutiny increases.

How this compares to the alternatives

Unlike generic cybersecurity or compliance courses, this program focuses exclusively on AI vendor risk with implementation-grade detail. It goes beyond theory to provide actionable frameworks, templates, and playbooks tailored for senior decision-makers.

Frequently asked

Who is this course designed for?
Senior leaders in business, technology, compliance, or risk roles who oversee AI vendor selection, integration, and governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 36 hours total , designed for self-paced learning with practical application exercises..

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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