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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 leaders navigating AI vendor compliance

$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.
The challenge of proving AI vendor due diligence to internal auditors and external regulators

The situation this course is for

Teams in regulated industries face rising scrutiny when deploying AI through third parties. Without a structured, repeatable assessment process, even well-intentioned initiatives can stall under audit or fail to meet compliance expectations. The gap isn't intent, it's methodology.

Who this is for

Compliance officers, risk managers, technology leads, and vendor governance professionals in financial services, healthcare, insurance, energy, and other regulated sectors who need to implement and document AI vendor risk controls with confidence.

Who this is not for

This course is not for developers building core AI models or for teams focused solely on consumer-facing AI products without regulatory oversight.

What you walk away with

  • Apply a proven framework to evaluate AI vendors against regulatory and internal control standards
  • Document due diligence with audit-ready assessments and evidence trails
  • Align legal, risk, and technical teams around shared vendor evaluation criteria
  • Reduce time-to-approval for AI vendor engagements by up to 50%
  • Lead AI governance initiatives with structured, repeatable processes

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Introduces core principles, regulatory expectations, and risk taxonomy specific to AI vendors in compliance-heavy environments.
12 chapters in this module
  1. Defining AI vendor risk in context
  2. Regulatory drivers across sectors
  3. Key roles in vendor oversight
  4. Lifecycle stages of vendor engagement
  5. Risk vs. innovation balance
  6. Industry-specific considerations
  7. Control framework alignment
  8. Stakeholder mapping
  9. Risk tolerance thresholds
  10. Third-party dependency models
  11. Baseline assessment design
  12. Common pitfalls to avoid
Module 2. Regulatory Landscape and Compliance Expectations
Covers current expectations from major regulators and standards bodies governing AI use in financial services, healthcare, and critical infrastructure.
12 chapters in this module
  1. Global regulatory trends in AI oversight
  2. SEC and FINRA guidance on AI use
  3. HIPAA and AI-enabled health tools
  4. GDPR and automated decision-making
  5. NIST AI Risk Management Framework
  6. ISO/IEC standards for AI systems
  7. Enforcement case patterns
  8. Jurisdictional overlap challenges
  9. Compliance by design principles
  10. Audit preparation fundamentals
  11. Regulator communication strategies
  12. Emerging regional frameworks
Module 3. Vendor Due Diligence Framework Design
Guides the creation of a scalable, repeatable due diligence process tailored to AI vendor assessments.
12 chapters in this module
  1. Due diligence vs. ongoing monitoring
  2. Designing tiered assessment models
  3. Questionnaire architecture
  4. Evidence collection protocols
  5. Automated screening tools
  6. Human-in-the-loop validation
  7. Scoring methodology design
  8. Risk-based segmentation
  9. Third-party certification review
  10. Reference and case study validation
  11. Time-to-complete benchmarks
  12. Internal signoff workflows
Module 4. AI Model Transparency and Explainability Assessment
Teaches how to evaluate vendor claims around model interpretability, bias detection, and decision logic disclosure.
12 chapters in this module
  1. Model cards and system cards overview
  2. Assessing explainability claims
  3. Bias detection methodology
  4. Ground truth validation techniques
  5. Feature importance analysis
  6. Counterfactual testing
  7. Documentation completeness review
  8. Third-party model audits
  9. User-facing transparency
  10. Model decay monitoring
  11. Explainability tool limitations
  12. Reporting to non-technical stakeholders
Module 5. Data Governance and Privacy Review
Covers evaluation of vendor data practices including sourcing, storage, access, and privacy safeguards.
12 chapters in this module
  1. Data provenance and lineage
  2. Training data bias risks
  3. Data retention policies
  4. Cross-border data flows
  5. Encryption in transit and at rest
  6. Access control models
  7. Subprocessor transparency
  8. Data minimization adherence
  9. Right to be forgotten workflows
  10. Audit log availability
  11. Incident response readiness
  12. Privacy impact assessments
Module 6. Security and Resilience Validation
Provides methodology to assess AI vendor cybersecurity posture, including infrastructure, access, and threat response.
12 chapters in this module
  1. SOC 2 and ISO 27001 review
  2. Penetration testing evidence
  3. Red teaming results evaluation
  4. API security design
  5. Model poisoning defenses
  6. Adversarial attack resistance
  7. Incident response plans
  8. Business continuity testing
  9. Access privilege review
  10. Zero-trust alignment
  11. Vendor breach history analysis
  12. Resilience metrics tracking
Module 7. Legal and Contractual Risk Mitigation
Covers contract design, liability allocation, IP ownership, and exit rights in AI vendor agreements.
12 chapters in this module
  1. Scope of use definitions
  2. IP ownership clauses
  3. Liability caps and indemnities
  4. Warranties and representations
  5. Exit assistance terms
  6. Data portability rights
  7. Model retraining obligations
  8. Subcontractor restrictions
  9. Termination triggers
  10. Dispute resolution mechanisms
  11. Jurisdiction and venue
  12. Force majeure considerations
Module 8. Ongoing Monitoring and Performance Tracking
Teaches how to establish continuous oversight of AI vendors post-onboarding.
12 chapters in this module
  1. Key risk indicators design
  2. Model performance drift detection
  3. Service level agreement tracking
  4. Escalation pathways
  5. Quarterly review cadence
  6. Audit right execution
  7. Change management processes
  8. Model update validation
  9. Incident reporting timelines
  10. Scorecard development
  11. Stakeholder communication plans
  12. Corrective action tracking
Module 9. Audit Readiness and Evidence Packaging
Guides the preparation of documentation packages for internal and external audits.
12 chapters in this module
  1. Audit scope definition
  2. Evidence collection checklist
  3. Control mapping to frameworks
  4. Risk rating documentation
  5. Vendor assessment archives
  6. Gap analysis reporting
  7. Remediation plan templates
  8. Stakeholder interview prep
  9. Regulatory inquiry response
  10. Historical trend reporting
  11. Version control of assessments
  12. Retention policy alignment
Module 10. Cross-Functional Alignment and Governance
Covers strategies for aligning legal, risk, IT, compliance, and business units around AI vendor oversight.
12 chapters in this module
  1. Governance committee design
  2. RACI matrix development
  3. Escalation protocols
  4. Decision authority mapping
  5. Change advisory boards
  6. Cross-team communication
  7. Policy harmonization
  8. Training for non-experts
  9. Stakeholder feedback loops
  10. Conflict resolution models
  11. Metrics for leadership reporting
  12. Board-level update design
Module 11. Implementation Playbook Integration
Demonstrates how to operationalize course frameworks using the included implementation playbook.
12 chapters in this module
  1. Playbook structure overview
  2. Customization guidelines
  3. Template adaptation
  4. Stakeholder onboarding
  5. Pilot program design
  6. Feedback collection
  7. Version control
  8. Integration with GRC tools
  9. Change management
  10. Success metrics tracking
  11. Lessons learned documentation
  12. Scaling best practices
Module 12. Future-Proofing AI Vendor Risk Strategy
Prepares teams for emerging trends including generative AI, real-time monitoring, and AI-specific regulations.
12 chapters in this module
  1. Generative AI risk considerations
  2. Real-time model monitoring
  3. AI-specific legislation preview
  4. Insurance and liability shifts
  5. Model marketplace risks
  6. Open-source model dependencies
  7. AI audit trail standards
  8. Explainability evolution
  9. Human oversight models
  10. Regulatory sandboxes
  11. Industry consortium participation
  12. Long-term strategy update

How this maps to your situation

  • Onboarding a new AI vendor under regulatory scrutiny
  • Preparing for an internal audit of third-party AI tools
  • Designing a company-wide AI vendor assessment policy
  • Responding to a regulatory inquiry about AI use

Before vs. after

Before
Uncertainty in evaluating AI vendors, inconsistent documentation, and reactive compliance posture
After
Confident, structured, and audit-ready AI vendor risk assessments that align with regulatory expectations and internal governance standards

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 professionals to complete at their own pace with implementation-focused exercises.

If nothing changes
Organizations that lack a formal AI vendor risk assessment process face increased audit friction, delayed innovation, and potential regulatory penalties, all of which can be mitigated with a proven, repeatable framework.

How this compares to the alternatives

Unlike generic vendor risk courses, this program is tailored specifically to AI systems in regulated environments, with up-to-date frameworks, implementation-grade templates, and regulatory alignment not found in off-the-shelf training or university courses.

Frequently asked

Who is this course designed for?
Compliance, risk, and technology leaders in regulated industries who need to assess and manage AI vendor risk with confidence and audit readiness.
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 assessments in the learning environment.
$199 one-time. Approximately 3 hours per module, designed for professionals to complete at their own pace with implementation-focused exercises..

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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