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Practical AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Practical AI Vendor Risk Assessment for Regulated Industries

A structured, implementation-grade framework for managing AI vendor risk in compliance-sensitive environments

$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.
Assessing AI vendors today often means navigating vague checklists and outdated frameworks that don’t reflect current regulatory expectations or technical realities.

The situation this course is for

Compliance teams are being asked to evaluate sophisticated AI systems without clear criteria. Procurement lacks standardized methods to compare vendors. Legal teams struggle to draft enforceable terms. The result is delayed deployments, increased exposure, and misalignment across functions, all while leadership expects confident, auditable decisions.

Who this is for

Business and technology professionals in regulated industries (financial services, healthcare, energy, government) responsible for third-party risk, compliance, AI governance, or technology procurement.

Who this is not for

This course is not for software developers building AI models or executives seeking high-level overviews. It’s designed for practitioners who must operationalize risk assessment.

What you walk away with

  • Apply a repeatable framework to evaluate AI vendors against regulatory and operational standards
  • Identify critical control gaps in vendor documentation, security posture, and model governance
  • Align assessment workflows across legal, compliance, IT, and procurement teams
  • Produce auditable assessment reports using standardized templates
  • Deploy an organization-specific implementation playbook to accelerate future evaluations

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core definitions, regulatory touchpoints, and the evolving expectations for AI oversight.
12 chapters in this module
  1. Defining AI vendor risk in financial, healthcare, and public sectors
  2. Mapping compliance obligations to vendor assessment
  3. Understanding the lifecycle of AI procurement
  4. Key differences between traditional and AI-enabled vendors
  5. Regulatory bodies and their emerging guidance
  6. The role of internal audit and oversight committees
  7. Common failure points in vendor onboarding
  8. Establishing cross-functional ownership
  9. Risk categorization frameworks for AI services
  10. Thresholds for enhanced due diligence
  11. Data residency and sovereignty implications
  12. Baseline expectations for transparency and documentation
Module 2. Regulatory Alignment Across Key Jurisdictions
Navigate sector-specific requirements from major regulators and standards bodies.
12 chapters in this module
  1. U.S. federal guidance on AI in financial services
  2. HIPAA and AI in healthcare data processing
  3. EU AI Act implications for U.S.-based vendors
  4. NIST AI Risk Management Framework alignment
  5. SEC expectations for AI use in public companies
  6. State-level privacy laws affecting vendor contracts
  7. Cross-border data transfer considerations
  8. Sector-specific enforcement trends
  9. Aligning with ISO/IEC standards for AI
  10. Preparing for regulatory examinations
  11. Documenting compliance rationale for auditors
  12. Benchmarking against peer institution practices
Module 3. Vendor Due Diligence Workflow Design
Build a scalable, consistent process for initiating and managing assessments.
12 chapters in this module
  1. Trigger points for vendor risk assessments
  2. Designing intake forms and scoping questionnaires
  3. Assigning risk tiers based on data and functionality
  4. Integrating with existing third-party risk platforms
  5. Automating initial screening steps
  6. Coordinating with legal and procurement teams
  7. Setting SLAs for assessment completion
  8. Managing vendor response fatigue
  9. Validating self-reported information
  10. Escalation paths for incomplete submissions
  11. Version control for assessment records
  12. Audit trail requirements for regulators
Module 4. Technical Control Validation for AI Systems
Evaluate vendor claims around model performance, security, and operational resilience.
12 chapters in this module
  1. Assessing model documentation completeness
  2. Validating training data provenance and bias mitigation
  3. Reviewing model monitoring and drift detection
  4. Evaluating adversarial robustness and red-teaming
  5. Security controls for model APIs and endpoints
  6. Access control and authentication practices
  7. Incident response planning for AI components
  8. Penetration testing expectations for vendors
  9. Logging and observability standards
  10. Backup and failover mechanisms for AI services
  11. Model versioning and update transparency
  12. Third-party dependency risk in AI stacks
Module 5. Model Governance and Explainability Requirements
Ensure vendors meet expectations for accountability, interpretability, and ethical use.
12 chapters in this module
  1. Evaluating model cards and system cards
  2. Assessing explainability methods for regulated decisions
  3. Human-in-the-loop and override capabilities
  4. Bias and fairness testing protocols
  5. Ethical AI principles in vendor policies
  6. Monitoring for discriminatory outcomes
  7. Stakeholder communication plans for AI use
  8. Handling contested AI-driven decisions
  9. Transparency obligations to customers and regulators
  10. Redress mechanisms for affected parties
  11. Ongoing model performance reporting
  12. Governance board engagement with vendor AI
Module 6. Contractual and Legal Safeguards
Draft and negotiate terms that protect your organization’s interests.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Data ownership and usage rights
  3. Model output liability and indemnification
  4. Audit rights and access to logs
  5. Subcontractor and third-party dependencies
  6. IP ownership of fine-tuned models
  7. Termination and exit strategies
  8. Right to export and retrain models
  9. Service level agreements for AI performance
  10. Penalties for non-compliance with commitments
  11. Dispute resolution for AI-related failures
  12. Regulatory change clauses
Module 7. Data Privacy and Protection Integration
Ensure AI vendors comply with privacy obligations across data lifecycles.
12 chapters in this module
  1. PII detection and handling in training data
  2. Anonymization and de-identification standards
  3. Consent management for AI processing
  4. Data minimization in model design
  5. Cross-border data flow compliance
  6. Vendor access to sensitive internal data
  7. Logging data access and queries
  8. Right to be forgotten implementation
  9. Data retention and deletion policies
  10. Breach notification timelines and coordination
  11. Privacy impact assessments for AI use
  12. DPO engagement with vendor risk processes
Module 8. Ongoing Monitoring and Reassessment
Shift from point-in-time reviews to continuous oversight.
12 chapters in this module
  1. Designing periodic reassessment schedules
  2. Triggers for unscheduled reviews
  3. Integrating with SIEM and risk dashboards
  4. Vendor incident reporting expectations
  5. Monitoring for model performance decay
  6. Tracking regulatory changes affecting vendors
  7. Benchmarking vendor updates against peers
  8. Customer complaint analysis for AI issues
  9. Automated alerting on policy deviations
  10. Quarterly health checks with vendor contacts
  11. Updating risk ratings dynamically
  12. Documenting ongoing oversight for auditors
Module 9. Cross-Functional Collaboration Frameworks
Align legal, compliance, IT, security, and business units around common standards.
12 chapters in this module
  1. Defining roles in the vendor assessment workflow
  2. Establishing RACI matrices for AI vendors
  3. Creating shared glossaries and definitions
  4. Conducting joint review sessions
  5. Resolving conflicting team priorities
  6. Standardizing scoring across departments
  7. Escalation paths for unresolved risks
  8. Training non-technical reviewers
  9. Communicating risk decisions to leadership
  10. Integrating with enterprise risk management
  11. Feedback loops for process improvement
  12. Metrics for team collaboration effectiveness
Module 10. Documentation and Audit Readiness
Produce clear, defensible records that satisfy internal and external reviewers.
12 chapters in this module
  1. Assembling the vendor assessment dossier
  2. Standardizing risk rating documentation
  3. Justifying exceptions and risk acceptances
  4. Versioning and retention of assessment files
  5. Preparing for internal audit inquiries
  6. Responding to regulator requests
  7. Redacting sensitive vendor information
  8. Demonstrating due diligence in litigation
  9. Using templates for consistency
  10. Automating report generation
  11. Linking controls to regulatory requirements
  12. Conducting pre-audit readiness checks
Module 11. Implementation at Scale
Deploy the framework across multiple vendors and business units.
12 chapters in this module
  1. Piloting the framework with high-risk vendors
  2. Training assessors across departments
  3. Customizing templates for different use cases
  4. Integrating with procurement systems
  5. Measuring time-to-assessment reduction
  6. Reducing redundant review efforts
  7. Building a center of excellence for AI risk
  8. Scaling with limited headcount
  9. Leveraging AI to assist in vendor reviews
  10. Benchmarking maturity over time
  11. Sharing best practices across teams
  12. Continuous improvement of the framework
Module 12. Future-Proofing and Emerging Challenges
Anticipate next-generation risks and adapt the framework accordingly.
12 chapters in this module
  1. Generative AI and large language model risks
  2. Open-source model dependencies
  3. AI supply chain transparency
  4. Deepfake detection and mitigation
  5. National security concerns in vendor selection
  6. Geopolitical risk in AI infrastructure
  7. Sustainability and carbon footprint of AI models
  8. Workforce displacement considerations
  9. Regulatory sandboxes and experimental approvals
  10. AI insurance and risk transfer options
  11. Preparing for mandatory disclosure rules
  12. Building organizational resilience to AI disruption

How this maps to your situation

  • You're launching an AI procurement initiative and need a defensible process
  • You're responding to auditor findings on vendor oversight gaps
  • You're building an AI governance program from the ground up
  • You're scaling AI adoption and must standardize risk assessment

Before vs. after

Before
Fragmented, ad-hoc evaluations that lack consistency, auditability, and cross-functional alignment.
After
A standardized, defensible, and scalable AI vendor risk assessment process that meets regulatory and operational demands.

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 with immediate applicability.

If nothing changes
Without a structured approach, organizations face inconsistent evaluations, increased compliance exposure, delayed AI adoption, and heightened scrutiny during audits or incidents.

How this compares to the alternatives

Unlike generic third-party risk courses or academic AI ethics programs, this course delivers a specific, actionable framework tailored to regulated industry needs, with implementation tools not found in open-source guides or vendor-provided checklists.

Frequently asked

Who is this course designed for?
Business and technology professionals in regulated industries responsible for AI vendor risk, compliance, procurement, or governance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning with immediate applicability..

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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