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Compliance-Ready AI Vendor Risk Assessment for Multi-Site Programs

$199.00
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A tailored course, built for your situation

Compliance-Ready AI Vendor Risk Assessment for Multi-Site Programs

A structured, implementation-grade path for managing AI vendor risk across distributed 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.
Fragmented AI vendor assessments slow down deployment and increase compliance exposure across sites.

The situation this course is for

As AI adoption grows across multiple locations, teams face inconsistent evaluation methods, duplicated efforts, and misaligned compliance thresholds, leading to delays, audit findings, and operational friction.

Who this is for

Business and technology professionals in compliance, risk, IT, or operations managing AI vendor programs across multiple sites.

Who this is not for

Individuals seeking introductory AI awareness content or single-site risk checklists.

What you walk away with

  • Apply a standardized framework for AI vendor risk assessment across all sites
  • Align control expectations between legal, IT, and operational teams
  • Reduce time to audit readiness using pre-built compliance mappings
  • Implement consistent vendor evaluation workflows across regions
  • Deploy a scalable playbook for onboarding and monitoring AI vendors

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Multi-Site Contexts
Establish core definitions, regulatory touchpoints, and operational scope for distributed AI risk programs.
12 chapters in this module
  1. Defining AI vendor risk in enterprise settings
  2. Regulatory drivers across jurisdictions
  3. Key differences: single-site vs. multi-site risk
  4. Stakeholder roles in AI governance
  5. Risk tolerance and organizational appetite
  6. Common control frameworks (NIST, ISO, SOC2)
  7. Mapping AI use cases to risk tiers
  8. Vendor lifecycle stages and risk touchpoints
  9. Baseline requirements for audit readiness
  10. Documentation standards for compliance
  11. Cross-functional alignment strategies
  12. Building a risk-aware culture
Module 2. Regulatory Alignment Across Jurisdictions
Navigate compliance expectations from major standards bodies and regional regulators.
12 chapters in this module
  1. Overview of NIST AI Risk Management Framework
  2. Integrating EU AI Act requirements
  3. U.S. sector-specific guidance (FTC, FDA, etc.)
  4. State-level AI regulations and implications
  5. FERPA, HIPAA, and data protection intersections
  6. Cross-border data transfer considerations
  7. Documentation for regulatory examination
  8. Managing evolving compliance timelines
  9. Benchmarking against industry peers
  10. Engaging legal counsel on risk thresholds
  11. Public reporting obligations
  12. Preparing for regulatory inquiries
Module 3. Risk Tiering and Vendor Classification
Develop a consistent method to categorize AI vendors by impact and exposure.
12 chapters in this module
  1. Criteria for high, medium, and low-risk vendors
  2. Functional impact scoring models
  3. Data sensitivity and processing volume
  4. Autonomy level and decision-making authority
  5. Vendor transparency and documentation
  6. Third-party dependencies and subprocessing
  7. Historical performance and incident tracking
  8. Financial stability and business continuity
  9. Geographic footprint and jurisdictional risk
  10. Scoring system calibration and review
  11. Automated classification workflows
  12. Maintaining classification records
Module 4. Control Mapping and Gap Analysis
Translate compliance requirements into actionable technical and operational controls.
12 chapters in this module
  1. Control identification from regulatory sources
  2. Mapping controls to vendor capabilities
  3. Technical vs. procedural control types
  4. Control ownership and accountability
  5. Gap analysis methodology and templates
  6. Prioritizing remediation efforts
  7. Evidence collection strategies
  8. Control validation techniques
  9. Third-party attestation review
  10. Penetration testing and AI red teaming
  11. Monitoring control effectiveness over time
  12. Updating controls with vendor changes
Module 5. Assessment Workflow Design
Build repeatable, scalable processes for evaluating AI vendors across sites.
12 chapters in this module
  1. Designing assessment intake procedures
  2. Standardizing questionnaire development
  3. Automating response collection and scoring
  4. Integrating with procurement systems
  5. Routing workflows by risk tier
  6. Cross-site coordination protocols
  7. Centralized vs. decentralized assessment models
  8. Version control for assessment tools
  9. Training assessors and reviewers
  10. Managing vendor response timelines
  11. Handling incomplete or delayed responses
  12. Audit trail and decision logging
Module 6. Cross-Site Coordination and Governance
Establish governance structures that ensure consistency without sacrificing local adaptability.
12 chapters in this module
  1. Central governance vs. local autonomy models
  2. Establishing a cross-site AI risk council
  3. Shared documentation repositories
  4. Conflict resolution for site-specific needs
  5. Change management across locations
  6. Communication protocols for updates
  7. Training consistency across teams
  8. Performance metrics for site teams
  9. Escalation paths for high-risk findings
  10. Vendor change notifications to all sites
  11. Synchronizing audit schedules
  12. Lessons learned sharing mechanisms
Module 7. Third-Party Audit and Attestation Review
Evaluate external audit reports and certifications for relevance and reliability.
12 chapters in this module
  1. Understanding SOC 2 Type II reports
  2. Reviewing ISO 27001 certification validity
  3. Penetration test report interpretation
  4. AI-specific audit frameworks emerging
  5. Assessing vendor self-attestation
  6. Independent verification strategies
  7. Scope limitations in third-party audits
  8. Timeline alignment with your audit cycle
  9. Engaging external experts when needed
  10. Summarizing findings for leadership
  11. Tracking audit follow-up actions
  12. Maintaining audit history archives
Module 8. Data Protection and Privacy Integration
Ensure AI vendor practices align with organizational data governance and privacy obligations.
12 chapters in this module
  1. Data mapping for AI vendor interactions
  2. Purpose limitation and data minimization
  3. Consent management with third-party AI
  4. Anonymization and pseudonymization techniques
  5. Data retention and deletion policies
  6. Breach notification requirements
  7. DPIA integration for high-risk vendors
  8. Vendor data processing agreements
  9. Cross-border transfer mechanisms
  10. Monitoring data access patterns
  11. Logging and alerting for anomalies
  12. Privacy-by-design principles in vendor selection
Module 9. Incident Response and Vendor Escalation
Prepare for and respond to AI-related incidents involving third-party providers.
12 chapters in this module
  1. Defining AI incident types and severity levels
  2. Vendor notification requirements
  3. Internal escalation protocols
  4. Cross-site incident communication
  5. Forensic data collection from vendors
  6. Regulatory reporting triggers
  7. Public relations coordination
  8. Post-incident review and remediation
  9. Updating risk profiles after incidents
  10. Vendor performance penalties
  11. Termination triggers and exit planning
  12. Lessons learned documentation
Module 10. Continuous Monitoring and Reassessment
Maintain oversight of AI vendors beyond initial assessment.
12 chapters in this module
  1. Designing ongoing monitoring workflows
  2. Key risk indicators for vendor stability
  3. Automated alerting from external sources
  4. Scheduled reassessment cadence
  5. Trigger-based reassessments (e.g., M&A)
  6. Vendor performance dashboards
  7. Integration with GRC platforms
  8. Tracking regulatory changes affecting vendors
  9. Monitoring open-source component risks
  10. Reviewing vendor update logs
  11. Handling vendor non-compliance
  12. Documentation for continuous oversight
Module 11. Stakeholder Communication and Reporting
Develop clear, actionable reporting for leadership, legal, and operational teams.
12 chapters in this module
  1. Executive summary creation
  2. Risk heat maps for leadership
  3. Technical reports for IT teams
  4. Compliance evidence packs for auditors
  5. Board-level AI risk updates
  6. Regulatory submission preparation
  7. Cross-departmental briefing templates
  8. Vendor scorecard distribution
  9. Change impact communication
  10. Training materials for non-experts
  11. Feedback loops from stakeholders
  12. Archiving and retrieval of reports
Module 12. Implementation Planning and Scaling
Deploy and scale the AI vendor risk program across the organization.
12 chapters in this module
  1. Phased rollout strategy
  2. Pilot program design and evaluation
  3. Resource allocation and staffing
  4. Tooling and platform selection
  5. Integration with existing GRC systems
  6. Change management for adoption
  7. Training curriculum development
  8. KPIs for program success
  9. Continuous improvement cycles
  10. Scaling to new business units
  11. External validation and benchmarking
  12. Sustaining leadership support

How this maps to your situation

  • You're launching AI tools across multiple locations and need consistent risk evaluation.
  • Your organization is standardizing compliance practices and expanding vendor oversight.
  • You're preparing for audits and need documented, repeatable assessment workflows.
  • Leadership is asking for clearer visibility into third-party AI risks.

Before vs. after

Before
Disjointed assessments, inconsistent controls, and reactive responses to vendor issues across sites.
After
A unified, audit-ready AI vendor risk program with clear workflows, documentation, and stakeholder alignment.

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 60 hours of focused learning, designed for self-paced completion over 8, 10 weeks.

If nothing changes
Without a structured approach, organizations face increased compliance exposure, duplicated efforts, audit findings, and delayed AI adoption across sites.

How this compares to the alternatives

Unlike generic AI ethics courses or one-size-fits-all checklists, this program delivers implementation-grade structure for multi-site environments with regulated compliance needs.

Frequently asked

Who is this course designed for?
Compliance, risk, IT, and operations professionals managing AI vendor programs across multiple locations with varying regulatory requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there video content?
No, the course is entirely text-based with downloadable templates and a hand-built implementation playbook.
$199 one-time. Approximately 60 hours of focused learning, designed for self-paced completion over 8, 10 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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