Skip to main content
Image coming soon

Modern AI Vendor Risk Assessment for Regulated Industries

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Modern AI Vendor Risk Assessment for Regulated Industries

Master implementation-grade risk frameworks for AI procurement in highly regulated 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.
AI adoption is accelerating, but vendor risk practices haven't kept pace, especially in regulated sectors where oversight demands exceed standard frameworks.

The situation this course is for

Teams are expected to assess AI vendors with confidence, yet lack structured, up-to-date methodologies that account for model transparency, data provenance, regulatory alignment, and lifecycle governance. Generic risk checklists fall short. What's needed is a detailed, sector-aware approach that turns ambiguity into audit-ready rigor.

Who this is for

Compliance leads, technology risk officers, procurement strategists, and senior engineers in regulated industries who are responsible for evaluating or approving third-party AI systems.

Who this is not for

This course is not for entry-level analysts, academic researchers, or professionals outside regulated sectors seeking general AI literacy.

What you walk away with

  • Apply a structured, repeatable framework for assessing AI vendor risk in regulated environments
  • Evaluate vendor documentation for compliance readiness across major regulatory regimes
  • Map AI system architectures to risk exposure zones using implementation-grade checklists
  • Deploy audit-aligned review processes that satisfy internal and external oversight bodies
  • Lead cross-functional vendor assessments with confidence and precision

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core principles and scope for AI vendor risk management in compliance-heavy environments.
12 chapters in this module
  1. Defining AI vendor risk in regulated industries
  2. Key differences from traditional IT vendor assessment
  3. Regulatory drivers shaping AI risk expectations
  4. The role of governance committees and oversight bodies
  5. Risk taxonomy for AI systems: model, data, process
  6. Stakeholder mapping across legal, compliance, and tech
  7. Vendor lifecycle stages and risk touchpoints
  8. Benchmarking current organizational maturity
  9. Common failure modes in AI procurement
  10. Building the business case for structured assessment
  11. Ethical considerations in third-party AI
  12. Course navigation and implementation playbook overview
Module 2. Regulatory Landscape and Compliance Alignment
Navigate current compliance expectations across major jurisdictions and frameworks.
12 chapters in this module
  1. Overview of AI-related guidance from financial regulators
  2. Healthcare data and AI: HIPAA, FDA, and beyond
  3. Telecom infrastructure and national AI policy alignment
  4. Global privacy regimes and AI vendor implications
  5. Sector-specific enforcement trends and scrutiny areas
  6. Mapping vendor practices to compliance control objectives
  7. Preparing for audits involving third-party AI systems
  8. Cross-border data flow considerations for AI vendors
  9. Regulator communication strategies during vendor review
  10. Anticipating upcoming rule changes and guidance
  11. Compliance debt in inherited AI vendor relationships
  12. Using the compliance alignment worksheet
Module 3. Vendor Due Diligence: Scope and Information Gathering
Design and execute effective information requests and data collection strategies.
12 chapters in this module
  1. Scoping the assessment: criticality and impact level
  2. Tailoring questionnaires to AI-specific risk domains
  3. Requesting model cards, data sheets, and system documentation
  4. Validating vendor claims through evidence requirements
  5. Assessing transparency and disclosure practices
  6. Handling proprietary information and NDAs
  7. Engaging technical teams in documentation review
  8. Third-party audit reports and attestation letters
  9. Red flags in vendor documentation gaps
  10. Timeboxing the information gathering phase
  11. Automating intake with template workflows
  12. Using the due diligence intake pack
Module 4. Model Risk Assessment for Third-Party AI
Evaluate AI models for stability, fairness, and operational integrity.
12 chapters in this module
  1. Understanding model types and their risk profiles
  2. Assessing training data quality and provenance
  3. Bias detection strategies in vendor-supplied models
  4. Performance metrics beyond accuracy: drift, calibration, uncertainty
  5. Model explainability requirements by use case
  6. Testing strategies: adversarial, stress, and edge case
  7. Monitoring plans and vendor SLAs for model health
  8. Version control and update management practices
  9. Model decommissioning and transition planning
  10. Handling model repurposing by vendors
  11. Reviewing model risk management frameworks
  12. Using the model risk scoring matrix
Module 5. Data Governance and Provenance Evaluation
Assess how vendors handle data sourcing, lineage, and lifecycle controls.
12 chapters in this module
  1. Data lineage tracking in AI system development
  2. Validating consent and licensing for training data
  3. Synthetic data usage and its risk implications
  4. Data retention and deletion commitments
  5. Cross-system data flow mapping with vendors
  6. Data quality assurance processes
  7. Handling of PII and sensitive attributes
  8. Data breach response coordination plans
  9. Vendor subprocessing and subcontracting risks
  10. Data sovereignty and jurisdictional boundaries
  11. Auditing data practices remotely
  12. Using the data governance checklist
Module 6. Security and Infrastructure Controls Review
Evaluate the technical safeguards protecting AI systems and data.
12 chapters in this module
  1. Cloud architecture review for AI workloads
  2. Access controls and identity management practices
  3. Encryption standards for data at rest and in transit
  4. Penetration testing and vulnerability disclosure
  5. Incident response readiness and communication
  6. Secure development lifecycle adherence
  7. API security and integration risks
  8. Monitoring and logging capabilities
  9. Disaster recovery and business continuity
  10. Zero trust alignment in AI vendor environments
  11. Third-party penetration test validation
  12. Using the infrastructure control validator
Module 7. Contractual and Legal Risk Mitigation
Structure agreements that enforce risk management and accountability.
12 chapters in this module
  1. Key clauses for AI-specific vendor contracts
  2. Liability allocation for model failures
  3. Indemnification for IP and bias-related claims
  4. Service level agreements for model performance
  5. Right to audit and inspection terms
  6. Exit strategies and data portability
  7. Change control and update notification
  8. Subcontractor oversight requirements
  9. Insurance requirements for AI vendors
  10. Dispute resolution mechanisms
  11. Renewal and termination conditions
  12. Using the contract clause library
Module 8. Operational Resilience and Monitoring
Ensure ongoing oversight and performance tracking post-deployment.
12 chapters in this module
  1. Designing operational dashboards for AI vendors
  2. Establishing performance baselines and thresholds
  3. Monitoring for model drift and data skew
  4. Alerting protocols for degradation events
  5. Vendor communication cadence and reporting
  6. Escalation paths for performance issues
  7. Human-in-the-loop requirements
  8. Fallback and manual override mechanisms
  9. Capacity planning and scalability reviews
  10. Reviewing incident post-mortems from vendors
  11. Continuous control validation
  12. Using the operational monitoring template
Module 9. Ethics, Fairness, and Social Impact Assessment
Evaluate broader societal implications of third-party AI systems.
12 chapters in this module
  1. Defining ethical AI in vendor relationships
  2. Assessing fairness across demographic groups
  3. Transparency and user notification practices
  4. Potential for misuse or dual-use applications
  5. Community impact and stakeholder feedback
  6. Handling controversial use cases
  7. Vendor ethics board and oversight structure
  8. Bias mitigation techniques in model design
  9. Equity considerations in deployment
  10. Public trust and reputational risk
  11. Social audit frameworks
  12. Using the ethics review scorecard
Module 10. Integration and Interoperability Risk
Assess risks arising from system integration and data exchange.
12 chapters in this module
  1. API design and documentation quality
  2. Data format compatibility and schema evolution
  3. Error handling and retry logic
  4. Latency and throughput requirements
  5. Authentication and authorization flows
  6. Versioning and backward compatibility
  7. Testing integration points securely
  8. Monitoring cross-system dependencies
  9. Handling partial failures gracefully
  10. Vendor support for integration troubleshooting
  11. Change management for connected systems
  12. Using the integration risk matrix
Module 11. Audit Readiness and Documentation Standards
Prepare comprehensive, defensible assessment records.
12 chapters in this module
  1. Building an audit trail for vendor evaluations
  2. Documenting risk decisions and rationale
  3. Version control for assessment artifacts
  4. Storing evidence securely and accessibly
  5. Preparing for internal and external audits
  6. Responding to regulator inquiries
  7. Redacting sensitive information appropriately
  8. Maintaining independence in evaluation
  9. Review cycles and update frequency
  10. Cross-functional sign-off processes
  11. Archiving completed assessments
  12. Using the audit readiness pack
Module 12. Scaling AI Vendor Risk Across the Organization
Implement enterprise-wide standards and governance.
12 chapters in this module
  1. Developing a centralized AI vendor risk function
  2. Standardizing assessment methodologies
  3. Training teams on risk evaluation
  4. Creating risk tiering and scoping protocols
  5. Integrating with existing GRC platforms
  6. Reporting risk exposure to leadership
  7. Benchmarking against industry peers
  8. Continuous improvement of assessment practices
  9. Managing vendor risk in M&A contexts
  10. Fostering vendor accountability ecosystems
  11. Driving cultural alignment on AI risk
  12. Using the enterprise rollout playbook

How this maps to your situation

  • Assessing a high-impact AI vendor for the first time
  • Responding to internal audit findings on vendor oversight
  • Scaling AI procurement across multiple business units
  • Preparing for regulatory scrutiny on third-party AI use

Before vs. after

Before
Uncertainty in evaluating AI vendors, reliance on ad-hoc checklists, difficulty aligning with compliance teams, and lack of audit-ready documentation.
After
Confidence in leading structured assessments, alignment with regulatory expectations, repeatable processes, and comprehensive documentation that supports governance and oversight.

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 3-5 hours per module, designed for working professionals. Total estimated commitment: 40-60 hours over 8-12 weeks.

If nothing changes
Without a structured approach, organizations face increased exposure to compliance gaps, operational failures, and reputational harm, particularly as regulators focus more intensely on third-party AI accountability.

How this compares to the alternatives

Unlike generic vendor risk courses or academic AI ethics programs, this course delivers implementation-grade tools tailored to regulated industries, with specific focus on third-party AI systems, compliance alignment, and audit readiness.

Frequently asked

Who is this course designed for?
Compliance officers, risk managers, technology leaders, and procurement professionals in regulated industries responsible for assessing or approving third-party AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is awarded upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 3-5 hours per module, designed for working professionals. Total estimated commitment: 40-60 hours over 8-12 weeks..

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
!