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Modern AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Modern AI Vendor Risk Assessment for Regulated Industries

Master third-party AI governance with implementation-grade frameworks tailored for compliance, risk, and technology leaders.

$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.
Falling between compliance mandates and aggressive AI vendor promises leaves teams exposed to oversight gaps and operational friction.

The situation this course is for

AI adoption is accelerating, but vendor risk frameworks haven't kept pace. Professionals in regulated industries face mounting pressure to validate AI claims, ensure audit readiness, and maintain control across complex supply chains , all without standardized tools or clear accountability models.

Who this is for

Compliance officers, risk managers, technology leaders, and governance professionals in financial services, healthcare, insurance, and other regulated sectors who are responsible for evaluating, approving, or overseeing third-party AI solutions.

Who this is not for

This course is not for software developers building core AI models, freelance consultants without enterprise oversight responsibilities, or individuals seeking theoretical overviews without implementation tools.

What you walk away with

  • Apply a structured framework to assess AI vendor claims with confidence
  • Navigate regulatory expectations across jurisdictions with precision
  • Implement audit-ready documentation practices for third-party AI systems
  • Integrate risk controls into procurement and contract negotiation workflows
  • Lead cross-functional discussions with legal, security, and business units using shared assessment criteria

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core definitions, regulatory touchpoints, and risk categories unique to AI vendors.
12 chapters in this module
  1. Defining AI vendor risk in regulated environments
  2. Key differences between traditional and AI-specific vendor risk
  3. Regulatory drivers shaping oversight expectations
  4. Jurisdictional variations in enforcement approaches
  5. Core responsibilities of oversight roles
  6. Common misconceptions about AI model transparency
  7. Vendor due diligence evolution in the AI era
  8. Mapping AI risk to existing compliance frameworks
  9. Understanding model lifecycle claims
  10. Evaluating vendor marketing language critically
  11. Baseline expectations for board reporting
  12. Common pitfalls in early-stage vendor engagement
Module 2. Governance Frameworks and Accountability Models
Design clear ownership structures and decision rights for AI vendor oversight.
12 chapters in this module
  1. Establishing RACI models for AI procurement
  2. Aligning vendor risk with enterprise risk appetite
  3. Creating escalation paths for model performance issues
  4. Documenting governance decisions systematically
  5. Integrating vendor oversight into existing committees
  6. Defining thresholds for executive escalation
  7. Balancing innovation speed with control rigor
  8. Role of legal and compliance in vendor onboarding
  9. Cross-functional alignment between IT and risk teams
  10. Managing conflicting priorities across departments
  11. Vendor risk as a shared responsibility
  12. Building accountability into vendor contract terms
Module 3. Third-Party AI Risk Taxonomy Development
Build a repeatable classification system for evaluating AI vendor risks.
12 chapters in this module
  1. Categorizing AI systems by risk severity
  2. Mapping data sensitivity to model architecture
  3. Identifying high-risk use cases in practice
  4. Developing risk scoring criteria
  5. Weighting factors for model opacity and complexity
  6. Assessing vendor transparency commitments
  7. Evaluating training data provenance claims
  8. Classifying models by autonomy level
  9. Risk implications of continuous learning systems
  10. Handling model drift in vendor-managed systems
  11. Scoring vendor incident response readiness
  12. Integrating taxonomy into procurement workflows
Module 4. Due Diligence Process Design for AI Vendors
Construct a scalable, evidence-based due diligence process.
12 chapters in this module
  1. Designing AI-specific RFP requirements
  2. Evaluating vendor documentation completeness
  3. Assessing model validation practices
  4. Reviewing third-party audit reports
  5. Verifying compliance with sector-specific standards
  6. Assessing data handling and privacy safeguards
  7. Evaluating bias detection and mitigation claims
  8. Reviewing model monitoring infrastructure
  9. Validating disaster recovery and business continuity
  10. Assessing vendor financial and operational stability
  11. Conducting technical interviews with vendor teams
  12. Documenting due diligence findings systematically
Module 5. Contractual Risk Mitigation Strategies
Embed enforceable risk controls into vendor agreements.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Defining model performance guarantees
  3. Establishing service level expectations
  4. Incorporating audit rights and access provisions
  5. Managing intellectual property ownership
  6. Addressing model retraining and updates
  7. Defining data ownership and usage rights
  8. Establishing incident reporting obligations
  9. Including termination triggers for compliance failures
  10. Negotiating liability and indemnification terms
  11. Handling model decommissioning requirements
  12. Ensuring portability and exit strategies
Module 6. Model Performance and Output Monitoring
Implement ongoing oversight of AI vendor model behavior.
12 chapters in this module
  1. Designing output validation frameworks
  2. Establishing baseline performance metrics
  3. Detecting model drift in production systems
  4. Monitoring for unintended consequences
  5. Validating consistency across geographies
  6. Assessing fairness and bias over time
  7. Tracking model accuracy degradation
  8. Evaluating vendor-provided monitoring tools
  9. Implementing independent verification methods
  10. Managing feedback loops from end users
  11. Documenting performance deviations systematically
  12. Escalating unresolved performance issues
Module 7. Regulatory Alignment and Audit Readiness
Ensure vendor practices meet evolving regulatory expectations.
12 chapters in this module
  1. Mapping vendor controls to regulatory requirements
  2. Preparing for supervisory inquiries
  3. Documenting risk assessments for auditors
  4. Responding to regulatory requests efficiently
  5. Demonstrating due diligence in vendor selection
  6. Maintaining audit trails for model decisions
  7. Aligning with cross-border regulatory expectations
  8. Updating documentation as regulations evolve
  9. Coordinating vendor responses during audits
  10. Managing regulatory exams involving third parties
  11. Demonstrating continuous improvement in oversight
  12. Avoiding common regulatory pitfalls
Module 8. Data Governance and Privacy Integration
Integrate data protection principles into AI vendor oversight.
12 chapters in this module
  1. Assessing data lineage and provenance
  2. Verifying compliance with privacy regulations
  3. Evaluating cross-border data transfer mechanisms
  4. Managing consent management claims
  5. Assessing data minimization practices
  6. Reviewing data retention and deletion policies
  7. Evaluating anonymization and pseudonymization techniques
  8. Handling subject access requests through vendors
  9. Assessing vendor responses to data breaches
  10. Validating data security controls
  11. Managing third-party data processors
  12. Ensuring alignment with internal data policies
Module 9. Bias, Fairness, and Ethical Risk Assessment
Evaluate AI vendor claims around ethical AI with rigor.
12 chapters in this module
  1. Defining fairness metrics for business contexts
  2. Assessing bias detection methodologies
  3. Evaluating mitigation strategies for identified bias
  4. Reviewing fairness reporting practices
  5. Assessing demographic representation in training data
  6. Evaluating model performance across subgroups
  7. Handling contested fairness definitions
  8. Establishing fairness review boards
  9. Managing stakeholder expectations on fairness
  10. Documenting ethical risk decisions
  11. Balancing fairness with business objectives
  12. Responding to public scrutiny of AI outcomes
Module 10. Incident Response and Breach Management
Prepare for and respond to AI-related incidents involving vendors.
12 chapters in this module
  1. Defining AI-specific incident types
  2. Establishing notification timelines
  3. Assessing vendor incident response capabilities
  4. Coordinating joint response plans
  5. Managing reputational risks from AI failures
  6. Handling regulatory reporting obligations
  7. Conducting root cause analysis with vendors
  8. Implementing corrective action plans
  9. Managing stakeholder communications
  10. Updating risk assessments post-incident
  11. Learning from industry-wide AI failures
  12. Strengthening controls based on incident data
Module 11. Vendor Exit and Transition Planning
Design orderly transitions away from underperforming or non-compliant AI vendors.
12 chapters in this module
  1. Triggering exit clauses in contracts
  2. Assessing knowledge transfer requirements
  3. Preserving model documentation and artifacts
  4. Ensuring data portability
  5. Managing model decommissioning securely
  6. Evaluating successor vendor readiness
  7. Maintaining audit continuity during transitions
  8. Handling intellectual property handovers
  9. Documenting lessons learned
  10. Managing workforce impacts
  11. Ensuring business continuity during transition
  12. Avoiding vendor lock-in scenarios
Module 12. Strategic Oversight and Board-Level Reporting
Communicate AI vendor risk posture effectively to executive leadership.
12 chapters in this module
  1. Designing executive risk dashboards
  2. Translating technical risks into business terms
  3. Establishing regular reporting cycles
  4. Prioritizing risks for board attention
  5. Demonstrating risk mitigation progress
  6. Aligning vendor risk with business strategy
  7. Managing executive expectations on AI benefits
  8. Communicating oversight challenges transparently
  9. Building credibility with board members
  10. Incorporating external benchmarking data
  11. Positioning risk management as strategic enabler
  12. Evolving oversight as AI capabilities advance

How this maps to your situation

  • Evaluating new AI vendors for procurement
  • Responding to regulatory inquiries about third-party AI use
  • Managing underperforming or non-compliant AI vendors
  • Preparing for board-level discussions on AI risk

Before vs. after

Before
Uncertain about how to validate AI vendor claims or demonstrate due diligence to regulators and internal stakeholders.
After
Confidently lead AI vendor assessments with structured frameworks, documented processes, and board-ready reporting.

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 hours per module, designed for flexible, self-paced learning around professional commitments.

If nothing changes
Without a structured approach, organizations risk regulatory penalties, operational disruptions, and reputational damage from AI vendor failures , while missing opportunities to position risk management as a strategic advantage.

How this compares to the alternatives

Unlike generic vendor risk courses, this program focuses exclusively on AI-specific challenges in regulated environments, with implementation-grade tools and real-world scenarios not available in academic or certification programs.

Frequently asked

Who is this course designed for?
Compliance officers, risk managers, technology leaders, and governance professionals in regulated industries responsible for overseeing third-party AI solutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or conceptual?
It's implementation-grade , designed for practitioners who need actionable frameworks, not just theory.
$199 one-time. Approximately 3 hours per module, designed for flexible, self-paced learning around professional commitments..

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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