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Practical AI Vendor Risk Assessment for Senior Leaders

$199.00
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A tailored course, built for your situation

Practical AI Vendor Risk Assessment for Senior Leaders

A structured, implementation-grade framework for assessing AI vendor risk 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.
Evaluating AI vendors often feels like navigating without a map, balancing innovation with compliance, security, and long-term viability.

The situation this course is for

Senior leaders are increasingly expected to sign off on AI vendor decisions without clear frameworks to assess risk. The lack of standardized evaluation tools leads to inconsistent due diligence, delayed approvals, and potential downstream exposure. With AI adoption accelerating, the gap between technical complexity and leadership oversight is widening.

Who this is for

Business and technology leaders in regulated environments who influence or approve AI vendor engagements and need a practical, repeatable assessment methodology.

Who this is not for

Individual contributors focused only on technical implementation, or those seeking high-level AI awareness without actionable evaluation tools.

What you walk away with

  • Apply a structured 12-point AI vendor risk assessment framework
  • Differentiate between surface-level claims and verifiable vendor capabilities
  • Evaluate AI vendors against regulatory, security, and operational resilience criteria
  • Lead cross-functional due diligence discussions with confidence
  • Document and justify vendor decisions to stakeholders and auditors

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Introduce core concepts, risk categories, and the evolving landscape shaping vendor evaluation.
12 chapters in this module
  1. Defining AI vendor risk in modern organizations
  2. Key drivers: regulation, innovation, and public trust
  3. The shift from IT procurement to strategic governance
  4. Common misconceptions about AI transparency
  5. Vendor ecosystem complexity and interdependencies
  6. The role of leadership in risk stewardship
  7. Aligning AI risk with enterprise risk frameworks
  8. Emerging standards in AI accountability
  9. Mapping vendor risk to business outcomes
  10. Stakeholder expectations across functions
  11. The lifecycle of AI vendor engagement
  12. From pilot to scale: risk evolution over time
Module 2. Regulatory and Compliance Alignment
Navigate global and sector-specific requirements impacting AI vendor selection.
12 chapters in this module
  1. Overview of AI-relevant regulatory bodies
  2. Understanding jurisdictional risk exposure
  3. GDPR, HIPAA, and sector-specific data rules
  4. Algorithmic accountability mandates
  5. Audit readiness and documentation standards
  6. Third-party compliance validation techniques
  7. Managing cross-border data flows
  8. Vendor adherence to evolving AI laws
  9. Certifications and their limitations
  10. Preparing for regulatory scrutiny
  11. Internal policy alignment with external rules
  12. Compliance as a competitive differentiator
Module 3. Data Governance and Privacy Risk
Assess how vendors handle data sourcing, storage, access, and retention.
12 chapters in this module
  1. Data provenance and lineage verification
  2. Training data bias and representativeness
  3. Data minimization and purpose limitation
  4. Consent management in AI systems
  5. Anonymization and re-identification risks
  6. Data sharing agreements and clauses
  7. Right to access and deletion compliance
  8. Vendor data breach response protocols
  9. Shadow data and undocumented data use
  10. Data ownership and portability rights
  11. Third-party data suppliers and due diligence
  12. Monitoring data usage post-contract
Module 4. Model Transparency and Explainability
Evaluate the clarity and interpretability of AI models offered by vendors.
12 chapters in this module
  1. Black box vs. interpretable models: trade-offs
  2. Explainability requirements by use case
  3. Model cards and transparency documentation
  4. Performance metrics beyond accuracy
  5. Bias detection and mitigation reporting
  6. Counterfactual explanations and user trust
  7. Human-in-the-loop design principles
  8. Model decision logging and traceability
  9. Customer-facing explanation needs
  10. Regulatory expectations for model disclosure
  11. Vendor claims vs. verifiable evidence
  12. Tools for independent model validation
Module 5. Security and Cyber Resilience
Assess vendor cybersecurity practices specific to AI systems and infrastructure.
12 chapters in this module
  1. AI-specific attack vectors and threats
  2. Model inversion and membership inference risks
  3. Adversarial attacks on AI models
  4. Secure model deployment and hosting
  5. Access controls for model tuning and updates
  6. Penetration testing and red teaming results
  7. Incident response planning for AI failures
  8. Secure software development lifecycle (SDLC)
  9. Vendor security certifications and audits
  10. Third-party dependency risks
  11. Patch management and version control
  12. Monitoring for anomalous model behavior
Module 6. Operational Resilience and Scalability
Determine whether vendor solutions can perform reliably at scale.
12 chapters in this module
  1. Uptime SLAs and real-world performance data
  2. Disaster recovery and failover capabilities
  3. Model drift detection and correction
  4. Monitoring tools and alerting thresholds
  5. Scalability under peak load conditions
  6. Integration complexity with existing systems
  7. Vendor support response times and tiers
  8. Change management and update protocols
  9. Dependency on proprietary infrastructure
  10. Exit strategies and deprecation plans
  11. Resource consumption and cost predictability
  12. Long-term maintenance and roadmap alignment
Module 7. Ethical and Social Impact Assessment
Evaluate the broader societal implications of AI vendor solutions.
12 chapters in this module
  1. Identifying high-risk AI use cases
  2. Fairness and non-discrimination commitments
  3. Community and stakeholder impact analysis
  4. Human dignity and autonomy considerations
  5. Environmental impact of AI operations
  6. Labor displacement and workforce effects
  7. Transparency in AI decision-making
  8. Redress mechanisms for affected parties
  9. Vendor ethics board and oversight
  10. Public trust and reputational risk
  11. Whistleblower protections and reporting
  12. Balancing innovation with responsibility
Module 8. Contractual and Legal Safeguards
Structure agreements that protect organizational interests and enforce accountability.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Liability for incorrect or harmful outputs
  3. Indemnification and insurance requirements
  4. Intellectual property ownership clarity
  5. Audit rights and access to documentation
  6. Performance guarantees and penalties
  7. Termination rights and data return
  8. Subcontractor oversight and approval
  9. Warranties around model behavior
  10. Dispute resolution mechanisms
  11. Governing law and jurisdiction selection
  12. Renewal and pricing lock-in risks
Module 9. Financial and Business Viability
Assess the long-term sustainability of AI vendors.
12 chapters in this module
  1. Vendor funding stage and runway analysis
  2. Revenue model and customer concentration
  3. Profitability and burn rate trends
  4. Leadership team stability and expertise
  5. Market differentiation and competitive moat
  6. Customer retention and churn data
  7. Dependency on key personnel
  8. M&A risk and acquisition likelihood
  9. Open source reliance and licensing
  10. Path to profitability and scaling plans
  11. Third-party financial audits
  12. Contingency planning for vendor failure
Module 10. Cross-Functional Due Diligence
Coordinate input from legal, compliance, IT, security, and business units.
12 chapters in this module
  1. Building a cross-functional review team
  2. Role-specific evaluation criteria
  3. Consensus-building across stakeholders
  4. Risk prioritization frameworks
  5. Documentation standards for approvals
  6. Managing conflicting stakeholder priorities
  7. Escalation paths for unresolved concerns
  8. Vendor interaction protocols
  9. Questionnaires and scoring rubrics
  10. Workshops for alignment and education
  11. Tracking decisions and rationale
  12. Post-approval monitoring responsibilities
Module 11. Implementation Playbook Development
Create a customized, actionable guide for ongoing vendor assessments.
12 chapters in this module
  1. Tailoring the framework to your organization
  2. Defining risk thresholds and tolerances
  3. Creating standardized evaluation templates
  4. Integrating with procurement workflows
  5. Training teams on consistent application
  6. Version control and update cycles
  7. Automating risk scoring where possible
  8. Reporting dashboards for leadership
  9. Feedback loops for continuous improvement
  10. Benchmarking against peer practices
  11. Onboarding new team members
  12. Maintaining institutional knowledge
Module 12. Future-Proofing and Adaptive Governance
Prepare for evolving AI capabilities, regulations, and threats.
12 chapters in this module
  1. Anticipating next-generation AI risks
  2. Monitoring regulatory horizon changes
  3. Updating assessment criteria proactively
  4. Engaging with standards development
  5. Scenario planning for emerging threats
  6. Building internal AI literacy
  7. Vendor innovation tracking
  8. Adaptive policy frameworks
  9. Lessons from industry incidents
  10. Investing in continuous oversight
  11. Public communication strategies
  12. Leading with responsibility and vision

How this maps to your situation

  • Evaluating a new AI vendor for a critical function
  • Responding to increased board or regulator scrutiny
  • Scaling AI adoption across multiple departments
  • Building internal capability to assess AI risk independently

Before vs. after

Before
Uncertain, inconsistent, and reactive approaches to AI vendor evaluation that delay decisions and expose the organization to avoidable risk.
After
A confident, structured, and repeatable process for assessing AI vendors that aligns innovation with governance and earns stakeholder trust.

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 3-4 hours per module, designed for flexible, self-paced learning around executive schedules.

If nothing changes
Without a formal assessment framework, organizations risk approving vendors based on incomplete information, leading to compliance gaps, reputational damage, operational failures, and loss of stakeholder confidence.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level overviews, this program delivers an implementation-grade methodology with specific tools, checklists, and real-world scenarios tailored to senior leaders responsible for vendor oversight.

Frequently asked

Who is this course designed for?
Senior business and technology leaders who influence or approve AI vendor decisions and need a practical, repeatable assessment framework.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a digital certificate of completion is issued after finishing all modules and assessments.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning around executive schedules..

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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