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Risk-Managed AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Risk-Managed AI Vendor Risk Assessment for Regulated Industries

A 12-module implementation-grade course for business and technology professionals advancing AI governance 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.
Even advanced teams struggle to consistently assess AI vendors under regulatory scrutiny, without a repeatable, auditable method.

The situation this course is for

Regulated industries face increasing pressure to adopt AI while maintaining compliance, data integrity, and operational resilience. Yet most risk assessment practices remain ad hoc, inconsistent, or too high-level to guide procurement decisions. Without a structured, cross-functional framework, organizations risk delays, non-compliance findings, or ineffective vendor onboarding.

Who this is for

Compliance officers, risk managers, technology leads, and procurement professionals in financial services, healthcare, logistics, and other regulated sectors implementing AI solutions through third-party vendors.

Who this is not for

This course is not for executives seeking only high-level overviews, or developers focused solely on model building. It is designed for implementers who need to operationalize risk controls.

What you walk away with

  • Apply a standardized framework to assess AI vendor risk across 12 critical dimensions
  • Align vendor evaluations with regulatory expectations in data protection, model governance, and audit readiness
  • Use downloadable templates to accelerate due diligence and reduce assessment cycle time
  • Build defensible documentation for internal audit and oversight committees
  • Integrate vendor risk practices into existing procurement and risk management workflows

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core principles, regulatory touchpoints, and the evolving expectations for third-party AI governance.
12 chapters in this module
  1. Defining AI vendor risk in compliance-heavy environments
  2. Regulatory drivers shaping vendor oversight
  3. Differences between traditional and AI-enabled vendor risk
  4. The role of governance bodies in vendor approval
  5. Risk tolerance and organizational risk appetite statements
  6. Mapping AI use cases to risk categories
  7. Key frameworks influencing vendor assessment (NIST, ISO, etc.)
  8. Stakeholder alignment across legal, risk, and technology
  9. Common pitfalls in early-stage vendor evaluations
  10. Building cross-functional assessment teams
  11. Vendor lifecycle management overview
  12. Integrating vendor risk into enterprise risk frameworks
Module 2. Pre-Assessment Planning and Scoping
Design assessment scope, identify critical systems, and prepare for engagement with vendors.
12 chapters in this module
  1. Determining assessment scope based on AI impact level
  2. Classifying vendors by criticality and data sensitivity
  3. Developing risk-based assessment tiers
  4. Creating vendor intake questionnaires
  5. Identifying internal subject matter experts
  6. Setting assessment timelines and milestones
  7. Documenting assumptions and constraints
  8. Preparing for vendor engagement and follow-up
  9. Aligning with procurement and contracting teams
  10. Using risk registers to prioritize vendors
  11. Establishing escalation paths for high-risk findings
  12. Leveraging past assessments for benchmarking
Module 3. Data Governance and Privacy Compliance
Evaluate how vendors handle data sourcing, storage, processing, and privacy obligations.
12 chapters in this module
  1. Assessing vendor data provenance and lineage
  2. Data minimization and purpose limitation in AI systems
  3. Vendor adherence to privacy regulations (GDPR, CCPA, etc.)
  4. Cross-border data transfer mechanisms
  5. Encryption standards for data in transit and at rest
  6. Access controls and identity management practices
  7. Data retention and deletion policies
  8. Third-party data sharing disclosures
  9. Data subject rights fulfillment support
  10. Vendor breach notification procedures
  11. Audit rights for data processing activities
  12. Data protection impact assessment (DPIA) alignment
Module 4. Model Transparency and Explainability
Evaluate the interpretability, documentation, and fairness of AI models provided by vendors.
12 chapters in this module
  1. Assessing model documentation completeness
  2. Understanding model inputs, outputs, and logic
  3. Vendor approaches to model explainability (XAI)
  4. Use of interpretable vs. black-box models
  5. Model versioning and change tracking
  6. Performance metrics and validation reporting
  7. Bias detection and mitigation strategies
  8. Fairness audits and demographic impact analysis
  9. Handling edge cases and model drift
  10. Providing user-facing explanations
  11. Third-party model validation support
  12. Model card and system card adoption
Module 5. Security and Resilience Controls
Assess the vendor’s cybersecurity posture, infrastructure resilience, and incident response readiness.
12 chapters in this module
  1. Evaluating SOC 2, ISO 27001, and other certifications
  2. Penetration testing and vulnerability management
  3. Secure software development lifecycle (SDLC) practices
  4. Infrastructure hardening and network segmentation
  5. Multi-factor authentication and privileged access
  6. Zero trust architecture adoption
  7. Incident detection and response capabilities
  8. Disaster recovery and business continuity planning
  9. Third-party dependency risk management
  10. API security and rate limiting
  11. Logging, monitoring, and alerting practices
  12. Threat intelligence integration
Module 6. Compliance and Regulatory Alignment
Ensure vendor practices align with industry-specific regulatory requirements and audit expectations.
12 chapters in this module
  1. Mapping vendor controls to regulatory obligations
  2. Demonstrating compliance with sector-specific rules
  3. Audit trail completeness and retention
  4. Regulatory reporting capabilities
  5. Handling regulatory inspections and inquiries
  6. Maintaining compliance documentation
  7. Licensing and legal authorization checks
  8. Sector-specific constraints (e.g., HIPAA, GLBA, PCI-DSS)
  9. Regulatory change management processes
  10. Vendor’s approach to regulatory updates
  11. Engagement with regulators and enforcement bodies
  12. Compliance self-assessments and gap remediation
Module 7. Contractual and Legal Safeguards
Structure contracts to enforce risk management, liability, and exit rights.
12 chapters in this module
  1. Defining AI-specific contract clauses
  2. Service level agreements (SLAs) for AI performance
  3. Liability caps and indemnification terms
  4. Intellectual property ownership and usage rights
  5. Model ownership and retraining rights
  6. Data ownership and portability provisions
  7. Right to audit and inspection rights
  8. Termination and exit strategy clauses
  9. Subcontractor and fourth-party oversight
  10. Warranties and representations on model behavior
  11. Insurance requirements for AI vendors
  12. Dispute resolution and jurisdiction
Module 8. Performance Monitoring and Ongoing Oversight
Establish continuous monitoring, KPIs, and reassessment cycles for live vendor relationships.
12 chapters in this module
  1. Designing ongoing monitoring dashboards
  2. Key risk indicators (KRIs) for vendor performance
  3. Model performance degradation alerts
  4. Automated compliance checks and scans
  5. Regular reassessment frequency and triggers
  6. Handling model updates and retraining
  7. Vendor communication and reporting cadence
  8. Managing vendor relationship changes
  9. Tracking SLA breaches and service disruptions
  10. Updating risk ratings over time
  11. Integrating feedback from end users
  12. Escalation and remediation workflows
Module 9. Third-Party Ecosystem and Supply Chain Risk
Assess risks introduced by the vendor’s own dependencies and subcontractors.
12 chapters in this module
  1. Mapping the vendor’s technology supply chain
  2. Identifying critical third-party components
  3. Open-source software usage and licensing
  4. Vendor oversight of subcontractors
  5. Software bill of materials (SBOM) availability
  6. Dependency vulnerability management
  7. Concentration risk in vendor ecosystems
  8. Resilience of underlying cloud infrastructure
  9. Geopolitical risks in supply chain locations
  10. Certifications and audits of sub-vendors
  11. Transparency in component sourcing
  12. Incident response coordination across layers
Module 10. Change Management and Version Control
Evaluate how vendors manage updates, patches, and model retraining.
12 chapters in this module
  1. Version control for AI models and datasets
  2. Change approval and deployment workflows
  3. Rollback and fallback mechanisms
  4. Notification processes for updates
  5. Impact assessment for model changes
  6. Retraining data provenance and quality
  7. Model drift detection and correction
  8. User communication during changes
  9. Documentation updates with each release
  10. Testing and validation before deployment
  11. Deprecation and sunset policies
  12. Audit trails for model and system changes
Module 11. Ethical AI and Responsible Use Governance
Assess vendor commitment to ethical AI principles and societal impact.
12 chapters in this module
  1. Vendor AI ethics principles and public commitments
  2. Human oversight and intervention capabilities
  3. Preventing misuse and dual-use risks
  4. Monitoring for harmful content generation
  5. Handling deepfakes and synthetic media
  6. Transparency in AI-generated content
  7. Community and stakeholder feedback mechanisms
  8. AI fairness and inclusion initiatives
  9. Environmental impact of AI systems
  10. Responsible innovation governance boards
  11. Whistleblower and reporting channels
  12. Alignment with global AI ethics guidelines
Module 12. Integration and Institutionalization
Embed vendor risk assessment into organizational workflows and culture.
12 chapters in this module
  1. Aligning with enterprise risk management (ERM)
  2. Training teams on AI vendor risk practices
  3. Creating centralized vendor risk repositories
  4. Integrating with procurement systems
  5. Executive reporting and dashboarding
  6. Lessons learned and continuous improvement
  7. Scaling assessments across business units
  8. Building internal expertise and centers of excellence
  9. Benchmarking against industry peers
  10. External validation and certification paths
  11. Future-proofing for emerging regulations
  12. Sustaining momentum and leadership support

How this maps to your situation

  • You're evaluating your first AI vendor under regulatory scrutiny
  • You're scaling AI adoption and need consistent vendor assessment
  • You're responding to audit findings on third-party risk
  • You're building a centralized AI governance function

Before vs. after

Before
Assessments are inconsistent, reactive, and lack audit-ready documentation.
After
You lead structured, repeatable, and defensible AI vendor risk evaluations aligned with compliance and operational needs.

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 to real-world assessments.

If nothing changes
Without a formalized approach, organizations risk regulatory findings, operational disruptions, or reputational damage from poorly vetted AI vendors.

How this compares to the alternatives

Unlike generic risk management courses or high-level AI ethics content, this program delivers implementation-grade tools specifically for regulated industry professionals assessing third-party AI solutions.

Frequently asked

Who is this course designed for?
Compliance officers, risk managers, technology leaders, and procurement professionals in regulated industries implementing AI through third-party vendors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after 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 to real-world assessments..

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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