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Mid-Market AI Vendor Risk Assessment for Regulated Industries

$199.00
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A tailored course, built for your situation

Mid-Market AI Vendor Risk Assessment for Regulated Industries

Implementation-grade risk governance for AI procurement in financial, healthcare, and public-sector 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.
Navigating AI vendor risk without clear frameworks leads to stalled deployments and compliance rework

The situation this course is for

Mid-market organizations in regulated industries face increasing pressure to adopt AI-driven solutions while maintaining compliance with evolving standards. Without a structured approach to vendor assessment, teams experience delays, misalignment between legal, risk, and technical units, and inconsistent documentation that complicates audits and scaling.

Who this is for

Risk officers, compliance leads, and technology architects in mid-sized organizations operating under financial, healthcare, or public-sector regulatory frameworks

Who this is not for

Enterprise-level vendor risk teams with dedicated AI governance staff or startups without regulatory exposure

What you walk away with

  • Apply a standardized framework to assess AI vendor compliance across jurisdictions
  • Classify model risk levels based on data sensitivity and decision impact
  • Draft enforceable contract terms for third-party AI systems
  • Prepare audit-ready documentation packages for regulators
  • Lead cross-functional assessments integrating legal, security, and operations

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Introduces core concepts, regulatory drivers, and risk taxonomy specific to mid-market AI adoption.
12 chapters in this module
  1. Defining AI vendor risk in regulated environments
  2. Overview of compliance frameworks impacting AI procurement
  3. Key differences: enterprise vs. mid-market risk capacity
  4. Sector-specific considerations: finance, healthcare, government
  5. The role of model risk management
  6. Third-party risk lifecycle stages
  7. Regulatory expectations for due diligence
  8. Common pitfalls in early-stage vendor selection
  9. Understanding data lineage and provenance
  10. Risk appetite frameworks for AI
  11. Governance roles: risk, legal, technical ownership
  12. Building a cross-functional assessment team
Module 2. Regulatory Landscape and Compliance Mapping
Covers current standards from NIST, EU AI Act, FDA, and sector-specific mandates.
12 chapters in this module
  1. NIST AI Risk Management Framework alignment
  2. EU AI Act: implications for vendor classification
  3. HIPAA and AI in healthcare workflows
  4. SEC expectations for algorithmic transparency
  5. FDA guidance on AI/ML-enabled medical devices
  6. OSFI and APRA rules in financial services
  7. Mapping controls to regulatory clauses
  8. Jurisdictional overlap and conflict resolution
  9. Compliance-by-design principles
  10. Vendor self-attestation reliability
  11. Audit trail requirements for AI systems
  12. Preparing for regulatory inquiries
Module 3. AI Vendor Due Diligence Framework
Structured methodology for evaluating technical, operational, and compliance readiness.
12 chapters in this module
  1. Pre-assessment scoping and objectives
  2. Request for Information (RFI) design
  3. Technical capability assessment criteria
  4. Model development lifecycle review
  5. Data governance and privacy safeguards
  6. Security posture evaluation
  7. Bias and fairness testing protocols
  8. Explainability and interpretability standards
  9. Incident response and model monitoring
  10. Change management and version control
  11. Third-party dependencies and supply chain
  12. Scoring and risk tiering methodology
Module 4. Model Risk Classification and Impact Analysis
Categorize AI systems by risk level and operational impact.
12 chapters in this module
  1. High vs. medium vs. low-risk AI applications
  2. Decision impact scoring matrix
  3. Data sensitivity classification
  4. Autonomy level assessment
  5. Human oversight requirements
  6. Fail-safe and fallback mechanisms
  7. Error consequence modeling
  8. Scalability and load testing expectations
  9. Bias impact across demographic groups
  10. Reputation risk from AI decisions
  11. Legal liability exposure by use case
  12. Risk re-evaluation triggers
Module 5. Contractual Guardrails and SLA Design
Draft enforceable agreements with clear performance, compliance, and exit terms.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Performance benchmarks and accuracy thresholds
  3. Model drift detection and response obligations
  4. Data ownership and usage rights
  5. Audit access and transparency requirements
  6. Subcontractor and cloud provider disclosure
  7. Liability caps and indemnification
  8. Termination and data portability clauses
  9. IP ownership and model reuse restrictions
  10. Incident reporting timelines
  11. Regulatory change adaptation clauses
  12. Exit strategy and transition planning
Module 6. Security and Data Protection Integration
Align AI vendor practices with organizational security policies and data protection laws.
12 chapters in this module
  1. Vendor security certification assessment
  2. SOC 2 and ISO 27001 applicability
  3. Penetration testing and red teaming expectations
  4. Encryption standards in transit and at rest
  5. Access control and identity management
  6. Data residency and cross-border transfer rules
  7. PIA and DPIA integration
  8. Zero-trust alignment with vendor systems
  9. Incident response coordination
  10. Log retention and forensic readiness
  11. API security and threat modeling
  12. Third-party penetration test validation
Module 7. Bias, Fairness, and Ethical Assurance
Implement testing and monitoring for algorithmic equity.
12 chapters in this module
  1. Defining fairness metrics by use case
  2. Bias detection across training data
  3. Disparate impact analysis methods
  4. Representative testing cohorts
  5. Ongoing monitoring for drift
  6. Transparency in model decision paths
  7. Stakeholder feedback loops
  8. Ethics review board integration
  9. Remediation protocols for biased outcomes
  10. Documentation for regulatory review
  11. Public reporting expectations
  12. Community impact considerations
Module 8. Explainability and Audit Readiness
Ensure AI decisions are interpretable and defensible under scrutiny.
12 chapters in this module
  1. Levels of explainability by risk tier
  2. Model documentation standards
  3. Feature importance and attribution methods
  4. Counterfactual explanation design
  5. Audit trail generation and retention
  6. Regulator-facing summary reports
  7. Internal review workflows
  8. Version history and change logs
  9. Decision logging for high-risk applications
  10. Human-in-the-loop validation
  11. Model card and datasheet integration
  12. Third-party audit facilitation
Module 9. Cross-Functional Alignment and Governance
Orchestrate collaboration between legal, risk, IT, and business units.
12 chapters in this module
  1. Stakeholder mapping and engagement plan
  2. Governance committee structure
  3. Risk escalation pathways
  4. Legal and compliance alignment
  5. IT integration and infrastructure fit
  6. Business unit adoption support
  7. Training and change management
  8. Vendor performance review cycles
  9. Issue escalation and resolution
  10. Budget and resource planning
  11. Success metrics and KPIs
  12. Board-level reporting templates
Module 10. Implementation Playbook: From Assessment to Approval
Step-by-step guide to executing a full vendor review cycle.
12 chapters in this module
  1. Project kickoff and scoping
  2. RFI distribution and vendor response
  3. Initial screening and shortlisting
  4. Deep-dive assessment planning
  5. Cross-functional workshop facilitation
  6. Risk scoring and tiering
  7. Mitigation plan development
  8. Contract negotiation support
  9. Approval workflow design
  10. Onboarding and integration
  11. Post-implementation review
  12. Continuous monitoring setup
Module 11. Sector-Specific Playbooks: Finance, Healthcare, Government
Tailored workflows and documentation for high-regulation domains.
12 chapters in this module
  1. Financial services: credit scoring and fraud detection
  2. Healthcare: clinical decision support systems
  3. Government: benefits eligibility and case management
  4. Insurance: underwriting automation
  5. Pharma: AI in clinical trials
  6. Legal tech: contract review and discovery
  7. Regulatory reporting automation
  8. Patient-facing chatbots and triage
  9. Public safety and law enforcement tools
  10. Procurement automation in public sector
  11. Compliance monitoring AI
  12. Sector-specific playbook customization
Module 12. Scaling AI Risk Governance Across the Portfolio
Extend the framework to manage multiple vendors and evolving technologies.
12 chapters in this module
  1. Centralized vs. decentralized governance models
  2. Vendor lifecycle management platforms
  3. Automated risk scoring tools
  4. Benchmarking across peer organizations
  5. Continuous improvement cycles
  6. Training programs for assessors
  7. External auditor coordination
  8. Regulatory change tracking
  9. AI innovation sandbox governance
  10. Third-party ecosystem risk aggregation
  11. M&A due diligence for AI assets
  12. Future-proofing for emerging regulations

How this maps to your situation

  • Assessing a new AI vendor for a regulated workflow
  • Preparing for regulatory audit of existing AI systems
  • Designing internal AI governance policies
  • Onboarding a third-party AI solution under tight timeline

Before vs. after

Before
Uncertainty in vendor evaluation, inconsistent documentation, and cross-functional misalignment delay AI adoption and increase compliance exposure.
After
Confident, structured assessments with audit-ready outputs, aligned stakeholders, and scalable governance for future AI initiatives.

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 4 hours per module, designed for busy professionals to complete at their own pace over 8, 12 weeks.

If nothing changes
Organizations that delay structured AI vendor risk practices face longer procurement cycles, higher rework costs, and increased exposure during regulatory reviews.

How this compares to the alternatives

Unlike generic risk frameworks or enterprise-focused playbooks, this course is tailored to mid-market realities, practical, implementation-grade, and aligned with current regulatory expectations without requiring a large governance team.

Frequently asked

Who is this course designed for?
Risk, compliance, and technology leaders in mid-sized organizations operating under financial, healthcare, or public-sector regulations.
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 assessments.
$199 one-time. Approximately 4 hours per module, designed for busy professionals to complete at their own pace 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
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