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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

A practical, implementation-grade framework for managing AI vendor risk 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.
AI adoption is accelerating, but vendor risk oversight remains reactive, fragmented, or overly theoretical, especially in mid-market regulated firms.

The situation this course is for

Teams are expected to validate AI vendors against compliance, security, and operational standards, but lack a repeatable method. Frameworks are too enterprise-heavy or academic. The result: delayed deployments, inconsistent assessments, and reliance on consultants or one-off checklists.

Who this is for

Compliance officers, risk analysts, technology leads, and operations managers in mid-market firms (200, 2,000 employees) operating in regulated sectors (financial services, healthcare, insurance, legal tech, govtech).

Who this is not for

Enterprise risk teams with dedicated AI governance units or startups using only off-the-shelf consumer AI tools.

What you walk away with

  • Apply a standardized assessment framework to any AI vendor engagement
  • Map regulatory requirements to technical and contractual controls
  • Build cross-functional alignment between legal, tech, and compliance teams
  • Reduce vendor onboarding time with reusable templates and scorecards
  • Demonstrate audit-ready documentation for internal and external review

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Regulated Contexts
Establish core definitions, regulatory touchpoints, and the unique challenges of mid-market scale.
12 chapters in this module
  1. Defining AI vendor risk for non-enterprise environments
  2. Regulatory landscape: where AI oversight is converging
  3. The cost of poor vendor assessment: real-world case studies
  4. Balancing innovation velocity with compliance rigor
  5. Key stakeholders in AI vendor decision-making
  6. Common pitfalls in early-stage vendor evaluation
  7. How mid-market constraints shape risk tolerance
  8. Emerging expectations from boards and auditors
  9. Differentiating AI from traditional software risk
  10. The lifecycle of an AI vendor relationship
  11. Risk escalation pathways and thresholds
  12. Building your internal risk taxonomy
Module 2. Regulatory Alignment and Compliance Mapping
Translate broad regulations into specific AI vendor assessment criteria.
12 chapters in this module
  1. Mapping GDPR, HIPAA, and SOC 2 to AI vendor controls
  2. Sector-specific requirements for financial and health tech
  3. Using NIST AI RMF as an assessment backbone
  4. Interpreting FTC and EU AI Act guidance for procurement
  5. Aligning with internal audit and compliance calendars
  6. Documenting compliance intent for vendor review
  7. Handling cross-border data and model inference
  8. Third-party assurance standards (ISO, SOC, CSA)
  9. Creating a compliance scorecard for vendors
  10. Managing regulatory change over time
  11. Demonstrating due diligence in vendor selection
  12. Integrating compliance mapping into RFPs
Module 3. Vendor Due Diligence and Pre-Engagement Assessment
Structure a rigorous pre-contract evaluation process for AI vendors.
12 chapters in this module
  1. Designing a vendor intake questionnaire
  2. Evaluating model transparency and documentation
  3. Assessing training data provenance and bias mitigation
  4. Reviewing vendor security and access controls
  5. Validating model performance claims
  6. Checking for third-party dependencies and sub-vendors
  7. Auditing vendor incident response and breach protocols
  8. Scoring vendor maturity across risk domains
  9. Using weighted scoring for comparative assessment
  10. Conducting technical discovery calls
  11. Identifying red flags in vendor responses
  12. Documenting assessment rationale
Module 4. Contractual Safeguards and Legal Alignment
Negotiate contracts that enforce risk management and accountability.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Data ownership and usage rights
  3. Model IP and derivative work protections
  4. Warranties for model accuracy and fairness
  5. Indemnification for regulatory penalties
  6. Right-to-audit provisions and access scope
  7. Termination triggers and exit rights
  8. Service level agreements for AI performance
  9. Change control and model update notifications
  10. Liability caps and insurance requirements
  11. Subcontractor oversight and approval
  12. Dispute resolution for AI-specific failures
Module 5. Model Risk Management and Technical Validation
Apply model risk principles to third-party AI systems.
12 chapters in this module
  1. Adapting FRB SR 11-7 for third-party AI
  2. Validating model inputs, outputs, and logic
  3. Assessing drift detection and retraining protocols
  4. Reviewing model documentation (data sheets, model cards)
  5. Evaluating explainability and interpretability features
  6. Testing for bias, fairness, and disparate impact
  7. Conducting adversarial testing and red teaming
  8. Reviewing version control and model lineage
  9. Monitoring for concept drift and data decay
  10. Validating model performance in production
  11. Assessing fallback and human-in-the-loop mechanisms
  12. Documenting validation findings
Module 6. Data Governance and Privacy Integration
Ensure AI vendors comply with data handling and privacy standards.
12 chapters in this module
  1. Data classification and sensitivity mapping
  2. Mapping data flows in AI vendor systems
  3. Ensuring data minimization and purpose limitation
  4. Validating anonymization and pseudonymization
  5. Assessing cross-border data transfer mechanisms
  6. Reviewing data retention and deletion policies
  7. Auditing access logs and user permissions
  8. Handling subject access requests through vendors
  9. Evaluating data breach notification timelines
  10. Integrating vendor data practices into DPIAs
  11. Managing consent and opt-out mechanisms
  12. Documenting data governance compliance
Module 7. Operational Resilience and Continuity Planning
Evaluate vendor reliability and business continuity practices.
12 chapters in this module
  1. Assessing vendor uptime and SLA reliability
  2. Reviewing disaster recovery and failover plans
  3. Evaluating redundancy and geographic distribution
  4. Testing incident response communication
  5. Validating backup and restoration procedures
  6. Assessing vendor financial and operational stability
  7. Monitoring service degradation and performance drops
  8. Planning for vendor lock-in and exit strategies
  9. Documenting business continuity requirements
  10. Conducting tabletop exercises with vendors
  11. Reviewing third-party dependencies
  12. Building internal fallback capabilities
Module 8. Monitoring, Audit, and Ongoing Oversight
Implement continuous monitoring and audit readiness for AI vendors.
12 chapters in this module
  1. Designing an ongoing monitoring calendar
  2. Tracking key risk indicators (KRIs) for vendors
  3. Conducting periodic reassessments
  4. Reviewing vendor audit reports (SOC 2, ISO)
  5. Performing internal spot checks and sampling
  6. Using dashboards for vendor risk visibility
  7. Escalating issues to vendor management
  8. Managing vendor corrective action plans
  9. Documenting oversight activities
  10. Preparing for internal and external audits
  11. Integrating vendor risk into enterprise risk reports
  12. Automating monitoring where possible
Module 9. Cross-Functional Alignment and Stakeholder Management
Align legal, compliance, IT, and business teams on vendor risk practices.
12 chapters in this module
  1. Identifying key stakeholders in vendor risk
  2. Building a cross-functional review committee
  3. Creating shared risk language and definitions
  4. Facilitating joint assessment sessions
  5. Managing conflicting priorities across teams
  6. Communicating risk decisions to leadership
  7. Training teams on vendor risk expectations
  8. Integrating risk into procurement workflows
  9. Documenting stakeholder input and approvals
  10. Running vendor risk workshops
  11. Aligning with enterprise architecture
  12. Scaling practices across business units
Module 10. Implementation Playbook and Tooling
Deploy the framework using templates, scorecards, and workflows.
12 chapters in this module
  1. Customizing the assessment framework
  2. Using the vendor intake template
  3. Applying the risk scoring matrix
  4. Populating the compliance mapping grid
  5. Generating RFP language
  6. Negotiating contract clauses
  7. Running technical validation tests
  8. Conducting stakeholder alignment sessions
  9. Documenting decisions in the risk ledger
  10. Using the audit readiness checklist
  11. Updating the playbook over time
  12. Training new team members
Module 11. Scaling and Institutionalizing the Practice
Embed AI vendor risk assessment into organizational muscle memory.
12 chapters in this module
  1. Building a vendor risk policy
  2. Integrating with existing GRC platforms
  3. Creating a vendor risk training program
  4. Measuring program effectiveness
  5. Reporting to board and audit committee
  6. Benchmarking against peers
  7. Iterating on the framework
  8. Managing resource constraints
  9. Automating assessments and monitoring
  10. Expanding to other third-party risks
  11. Recognizing team contributions
  12. Sustaining momentum over time
Module 12. Future-Proofing and Emerging Trends
Anticipate next-generation risks and regulatory shifts.
12 chapters in this module
  1. Tracking AI regulation in flight
  2. Preparing for mandatory AI registries
  3. Evaluating open-weight vs. closed models
  4. Assessing generative AI-specific risks
  5. Monitoring compute and energy use disclosures
  6. Reviewing AI ethics and human rights frameworks
  7. Evaluating vendor ESG commitments
  8. Handling AI-generated content provenance
  9. Anticipating liability for AI outputs
  10. Assessing vendor alignment with AI standards
  11. Planning for AI incident disclosure rules
  12. Staying ahead of enforcement trends

How this maps to your situation

  • Assessing a new AI vendor for procurement
  • Responding to an auditor’s request for vendor documentation
  • Negotiating contract terms with a high-risk AI provider
  • Scaling vendor risk practices across multiple departments

Before vs. after

Before
Reactive, inconsistent, and resource-intensive vendor assessments that delay deployments and increase compliance exposure.
After
A repeatable, audit-ready process for evaluating and managing AI vendors, aligned with regulations, stakeholders, and business goals.

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 just-in-time learning and immediate application.

If nothing changes
Without a structured approach, organizations risk delayed AI adoption, compliance findings, reputational damage, and unmanaged exposure to vendor failures or regulatory penalties.

How this compares to the alternatives

Unlike generic AI ethics courses or enterprise-heavy frameworks, this program is tailored to mid-market realities, practical, actionable, and focused on vendor assessment, not theoretical AI governance.

Frequently asked

Who is this course designed for?
Compliance, risk, and technology professionals in mid-market firms operating in regulated industries who are responsible for assessing or managing third-party AI vendors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for non-technical leaders?
Yes, while technical depth is included, the framework is designed for cross-functional use, with clear guidance for legal, compliance, and business stakeholders.
$199 one-time. Approximately 3, 4 hours per module, designed for just-in-time learning and immediate application..

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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