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Practical AI Vendor Risk Assessment for Compliance Officers

$199.00
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A tailored course, built for your situation

Practical AI Vendor Risk Assessment for Compliance Officers

Master AI governance with real-world frameworks for vendor due diligence and compliance integration

$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.
Compliance teams face increasing pressure to evaluate AI vendors without clear, actionable frameworks.

The situation this course is for

AI adoption is accelerating, but many compliance officers lack structured methods to assess vendor transparency, model risk, data governance, and audit readiness, leading to delays, rework, and misalignment with legal and operational standards.

Who this is for

Compliance, risk, and governance professionals in mid-to-large organizations adopting AI through third-party vendors.

Who this is not for

This is not for software developers or data scientists building models. It is not for executives seeking high-level overviews without implementation detail.

What you walk away with

  • Apply a structured framework to assess AI vendor risk across technical, legal, and operational domains
  • Integrate compliance requirements into vendor RFPs, contracts, and audit workflows
  • Use practical templates to evaluate model documentation, bias testing, and incident response readiness
  • Lead cross-functional assessments with confidence using standardized evaluation criteria
  • Translate regulatory expectations into actionable vendor due diligence steps

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk
Establish core concepts in AI risk, compliance domains, and third-party lifecycle management.
12 chapters in this module
  1. Defining AI vendor risk in compliance contexts
  2. Key regulatory drivers shaping vendor oversight
  3. Stakeholder roles in AI procurement and review
  4. Emerging expectations from global standards bodies
  5. Mapping AI use cases to risk tiers
  6. Vendor landscape overview: AI-as-a-service providers
  7. Compliance officer responsibilities in AI governance
  8. Integrating AI risk into existing frameworks
  9. Common pitfalls in early-stage vendor assessment
  10. Case study: AI adoption in customer-facing operations
  11. Risk taxonomy for AI-powered services
  12. Building a cross-functional assessment team
Module 2. Due Diligence Frameworks
Implement standardized checklists and evaluation criteria for pre-contract review.
12 chapters in this module
  1. Designing a vendor due diligence workflow
  2. Essential documentation requests for AI vendors
  3. Assessing model development lifecycle maturity
  4. Reviewing data sourcing and labeling practices
  5. Evaluating model validation and testing protocols
  6. Understanding training data provenance
  7. Checking for bias detection and mitigation steps
  8. Reviewing model versioning and updates
  9. Assessing model explainability commitments
  10. Evaluating incident reporting mechanisms
  11. Contractual red flags in AI vendor agreements
  12. Benchmarking against industry peers
Module 3. Model Risk Management Integration
Align AI vendor assessments with internal model risk policies and controls.
12 chapters in this module
  1. Mapping vendor models to internal MRM frameworks
  2. Classifying AI models by risk tier
  3. Establishing model inventory requirements
  4. Vendor responsibilities in model monitoring
  5. Performance drift detection expectations
  6. Model revalidation triggers and timelines
  7. Third-party model documentation standards
  8. Audit trail requirements for AI decisions
  9. Handling model decommissioning by vendors
  10. Incident escalation pathways
  11. Model change management protocols
  12. Integrating vendor models into governance dashboards
Module 4. Data Governance and Privacy
Evaluate vendor data handling with privacy, sovereignty, and retention compliance.
12 chapters in this module
  1. Data flow mapping in AI vendor ecosystems
  2. Assessing compliance with global privacy laws
  3. Vendor data access and access logging
  4. Data retention and deletion commitments
  5. Cross-border data transfer mechanisms
  6. Subprocessor transparency and control
  7. Data minimization in AI systems
  8. Consent and lawful basis verification
  9. PIA and DPIA alignment with vendor models
  10. Vendor responses to data subject requests
  11. Encryption and pseudonymization practices
  12. Data breach notification timelines
Module 5. Explainability and Fairness
Assess vendor commitments to model transparency, fairness testing, and bias mitigation.
12 chapters in this module
  1. Defining explainability in AI compliance contexts
  2. Vendor obligations for model interpretability
  3. Techniques for explaining black-box models
  4. Bias testing methodologies and frequency
  5. Fairness metrics and thresholds
  6. Demographic data usage policies
  7. Bias mitigation strategies in training
  8. Post-deployment fairness monitoring
  9. Handling contested AI decisions
  10. Documentation of fairness testing results
  11. Third-party audit readiness for bias claims
  12. Transparency reporting expectations
Module 6. Security and Resilience
Evaluate vendor security posture, incident response, and system resilience.
12 chapters in this module
  1. Vendor security certifications and attestations
  2. Penetration testing and red teaming disclosure
  3. Model inversion and membership attack risks
  4. Adversarial robustness testing
  5. Secure API design and authentication
  6. Logging and monitoring for AI systems
  7. Incident response planning with vendors
  8. Ransomware and model sabotage scenarios
  9. Disaster recovery and model rollback plans
  10. Uptime SLAs and performance guarantees
  11. Vendor redundancy and failover design
  12. Cyber insurance and liability coverage
Module 7. Compliance Integration Patterns
Embed vendor risk assessments into existing compliance workflows and controls.
12 chapters in this module
  1. Integrating AI risk into compliance checklists
  2. Updating internal audit programs
  3. Training compliance teams on AI terminology
  4. Vendor risk scoring systems
  5. Automating due diligence inputs
  6. Compliance dashboard integration
  7. Reporting to board and executive leadership
  8. Linking AI oversight to SOX and GDPR
  9. Audit evidence collection strategies
  10. Cross-departmental coordination models
  11. Compliance exception tracking
  12. Lessons from early adopters
Module 8. Contractual and Legal Guardrails
Structure agreements with enforceable terms for performance, liability, and exit.
12 chapters in this module
  1. Key clauses in AI vendor contracts
  2. Model performance guarantees
  3. Liability for erroneous or harmful outputs
  4. Indemnification and insurance requirements
  5. Right to audit and inspection rights
  6. Data ownership and IP rights
  7. Exit strategies and model migration
  8. Knowledge transfer obligations
  9. Penalties for non-compliance
  10. Service level agreement enforcement
  11. Dispute resolution mechanisms
  12. Renewal and termination triggers
Module 9. Audit and Assurance Readiness
Prepare for internal and external audits of AI vendor engagements.
12 chapters in this module
  1. Preparing for internal audit requests
  2. Documenting vendor assessment decisions
  3. Evidence collection for compliance teams
  4. Third-party audit coordination
  5. Responding to regulator inquiries
  6. Audit trail completeness checks
  7. Version control of model artifacts
  8. Certifications and attestation handling
  9. Handling auditor challenges to AI use
  10. Preparing executive summaries
  11. Vendor cooperation expectations
  12. Post-audit action planning
Module 10. Cross-Border and Sector-Specific Risks
Navigate jurisdictional complexity and industry-specific regulatory demands.
12 chapters in this module
  1. AI regulation in financial services
  2. Healthcare AI compliance requirements
  3. AI in customer service and contact centers
  4. Regulatory expectations in EMEA
  5. Sector-specific data localization laws
  6. Export controls on AI technologies
  7. Vendor compliance with local labor laws
  8. Language and cultural adaptation risks
  9. Local regulatory liaison requirements
  10. Harmonizing global standards
  11. AI ethics board expectations
  12. Sector-specific incident reporting
Module 11. Monitoring and Ongoing Oversight
Implement continuous monitoring and re-evaluation cycles for active vendors.
12 chapters in this module
  1. Ongoing risk assessment frequency
  2. Key risk indicators for vendor models
  3. Performance drift detection systems
  4. Vendor reporting obligations
  5. Quarterly compliance reviews
  6. Updating risk assessments with new data
  7. Handling model updates and version changes
  8. Reassessment triggers and thresholds
  9. Feedback loops from operations teams
  10. Escalation protocols for anomalies
  11. Renewal due diligence refresh
  12. Lessons from vendor incidents
Module 12. Building a Scalable AI Compliance Function
Develop repeatable processes and team capabilities for long-term success.
12 chapters in this module
  1. Designing a center of excellence for AI compliance
  2. Compliance role specialization paths
  3. Training programs for new hires
  4. Vendor assessment automation tools
  5. Knowledge management for AI risk
  6. Metrics for compliance effectiveness
  7. Stakeholder communication plans
  8. Budgeting for AI governance
  9. Scaling across business units
  10. Succession planning for compliance roles
  11. External benchmarking and peer learning
  12. Future trends in AI compliance

How this maps to your situation

  • Assessing a new AI vendor for customer service automation
  • Re-evaluating an existing AI partner after a model update
  • Preparing for an internal audit of AI vendor contracts
  • Designing a compliance framework for AI adoption roadmap

Before vs. after

Before
Overwhelmed by vague vendor claims and inconsistent assessment methods, struggling to align technical risk with compliance requirements.
After
Confidently lead AI vendor evaluations with structured frameworks, clear documentation, and stakeholder alignment.

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 40, 50 hours of self-paced learning, designed for professionals balancing full-time roles.

If nothing changes
Without structured assessment methods, organizations risk delayed AI adoption, compliance gaps, audit findings, or reputational harm from poorly governed vendor relationships.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance webinars, this program delivers implementation-grade frameworks, real-world templates, and vendor-specific evaluation workflows used by leading organizations.

Frequently asked

Who is this course designed for?
Compliance, risk, and governance professionals responsible for evaluating or overseeing AI vendors in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical?
It is designed for non-engineers who need to assess technical risk. No coding is required, but fluency in AI concepts is developed.
$199 one-time. Approximately 40, 50 hours of self-paced learning, designed for professionals balancing full-time roles..

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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