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Compliance-Ready AI Vendor Risk Assessment for Compliance Officers

$199.00
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A tailored course, built for your situation

Compliance-Ready AI Vendor Risk Assessment for Compliance Officers

Master implementation-grade frameworks to lead AI vendor oversight with confidence and precision

$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 assessing AI vendors without standardized, compliance-aligned frameworks

The situation this course is for

Compliance officers are increasingly asked to evaluate AI-powered vendors without clear assessment criteria, consistent documentation standards, or integration pathways into existing risk management programs. This creates delays, inconsistent evaluations, and gaps in audit readiness.

Who this is for

Compliance Officers, Risk Managers, and Governance Professionals in mid-to-large organizations adopting AI technologies through third-party vendors

Who this is not for

Individuals seeking introductory AI concepts or general cybersecurity training; this course assumes foundational knowledge of compliance frameworks and focuses on applied AI vendor risk execution

What you walk away with

  • Apply a standardized assessment framework to any AI vendor engagement
  • Build defensible documentation aligned with regulatory expectations
  • Integrate AI vendor reviews into existing compliance workflows
  • Lead cross-functional evaluations with legal, security, and procurement teams
  • Reduce time-to-approval for AI vendor onboarding by up to 50%

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Compliance
Establish core principles and compliance-specific risk dimensions for AI vendors
12 chapters in this module
  1. Defining AI vendor risk in regulated environments
  2. Mapping AI capabilities to compliance obligations
  3. Key differences from traditional IT vendor assessments
  4. Regulatory trends shaping vendor oversight
  5. Core responsibilities of the compliance officer
  6. Aligning with NIST AI Risk Management Framework
  7. Understanding model lifecycle implications
  8. Data provenance and lineage requirements
  9. Transparency obligations for third-party AI
  10. Documentation standards for audit readiness
  11. Common pitfalls in early-stage evaluations
  12. Building a compliance-first assessment mindset
Module 2. Regulatory Alignment and Expectations
Navigate evolving standards across jurisdictions and sectors
12 chapters in this module
  1. Overview of global AI governance initiatives
  2. Mapping compliance requirements to vendor contracts
  3. Sector-specific rules for education, finance, and healthcare
  4. Interpreting FTC, EU AI Act, and state-level guidance
  5. Demonstrating due diligence in vendor selection
  6. Handling cross-border data flows in AI systems
  7. Compliance with student privacy laws in AI tools
  8. Preparing for regulatory inquiries on AI use
  9. Documenting risk acceptance decisions
  10. Working with legal teams on liability clauses
  11. Reporting obligations for AI incidents
  12. Maintaining up-to-date compliance posture
Module 3. Vendor Assessment Framework Design
Create a repeatable, scalable assessment methodology
12 chapters in this module
  1. Structuring a tiered risk classification system
  2. Developing AI-specific risk criteria
  3. Weighting factors for high-impact vendors
  4. Designing standardized evaluation scorecards
  5. Incorporating fairness and bias detection
  6. Assessing model explainability commitments
  7. Evaluating vendor change management practices
  8. Reviewing third-party audit certifications
  9. Validating claims of 'ethical AI' or 'responsible AI'
  10. Benchmarking against industry peers
  11. Integrating with existing GRC platforms
  12. Version control for assessment templates
Module 4. Due Diligence Questionnaire Development
Build comprehensive, targeted question sets for vendor engagement
12 chapters in this module
  1. Core domains of inquiry for AI vendors
  2. Crafting effective follow-up questions
  3. Handling incomplete or evasive responses
  4. Validating technical documentation
  5. Assessing model training data practices
  6. Evaluating bias testing and mitigation
  7. Understanding model drift detection
  8. Reviewing human-in-the-loop protocols
  9. Auditing model performance monitoring
  10. Assessing incident response readiness
  11. Verifying security controls for AI systems
  12. Documenting vendor subcontractor relationships
Module 5. Risk Scoring and Prioritization Models
Implement quantitative and qualitative scoring systems
12 chapters in this module
  1. Designing risk matrices for AI vendors
  2. Assigning impact and likelihood ratings
  3. Incorporating organizational sensitivity factors
  4. Adjusting for student data exposure levels
  5. Weighting for regulatory scrutiny potential
  6. Creating dynamic risk dashboards
  7. Setting thresholds for escalation
  8. Handling borderline risk determinations
  9. Documenting scoring rationale
  10. Reviewing scores over time
  11. Aligning with enterprise risk appetite
  12. Reporting risk profiles to leadership
Module 6. Third-Party Audit Evaluation
Assess external audit reports and certifications
12 chapters in this module
  1. Interpreting SOC 2 reports for AI systems
  2. Evaluating ISO 27001 compliance in AI vendors
  3. Reviewing algorithmic impact assessments
  4. Assessing penetration test results
  5. Validating model validation reports
  6. Understanding red team findings
  7. Reviewing bias audit documentation
  8. Assessing data governance certifications
  9. Handling conflicting audit opinions
  10. Requesting supplemental evidence
  11. Benchmarking against peer audit outcomes
  12. Documenting audit follow-up actions
Module 7. Contractual Risk Mitigation
Embed compliance requirements into vendor agreements
12 chapters in this module
  1. Negotiating AI-specific contract clauses
  2. Defining model performance guarantees
  3. Establishing transparency rights
  4. Including audit and inspection rights
  5. Setting data retention and deletion terms
  6. Addressing model retraining obligations
  7. Handling intellectual property disclosures
  8. Requiring incident notification timelines
  9. Including exit strategy and data portability
  10. Ensuring right-to-explain decisions
  11. Managing subcontractor oversight
  12. Documenting compliance commitments
Module 8. Ongoing Monitoring and Review
Implement continuous oversight mechanisms
12 chapters in this module
  1. Designing periodic review schedules
  2. Tracking model performance changes
  3. Monitoring for concept drift
  4. Reviewing updated training data disclosures
  5. Assessing incident reporting quality
  6. Evaluating vendor communication practices
  7. Updating risk assessments after changes
  8. Handling model version updates
  9. Reviewing new feature introductions
  10. Conducting surprise audits
  11. Maintaining documentation trails
  12. Reporting ongoing risks to leadership
Module 9. Cross-Functional Collaboration
Lead effective coordination across teams
12 chapters in this module
  1. Engaging legal teams in vendor reviews
  2. Collaborating with IT security professionals
  3. Working with procurement specialists
  4. Involving data privacy officers
  5. Coordinating with instructional technology teams
  6. Facilitating leadership briefings
  7. Managing escalation paths
  8. Resolving conflicting stakeholder priorities
  9. Documenting cross-functional decisions
  10. Creating shared risk language
  11. Running joint evaluation sessions
  12. Building organizational memory
Module 10. Incident Response and Remediation
Prepare for and respond to AI-related incidents
12 chapters in this module
  1. Identifying AI-specific incident types
  2. Establishing detection thresholds
  3. Activating response protocols
  4. Assessing bias or fairness failures
  5. Evaluating model accuracy degradation
  6. Handling unintended outputs
  7. Coordinating with vendor support
  8. Documenting root cause analysis
  9. Implementing corrective actions
  10. Reporting to regulators when required
  11. Updating risk assessments post-incident
  12. Preventing recurrence through process changes
Module 11. Training and Knowledge Transfer
Scale compliance practices across teams
12 chapters in this module
  1. Developing internal training materials
  2. Creating vendor assessment playbooks
  3. Onboarding new team members
  4. Conducting mock evaluations
  5. Sharing lessons learned
  6. Building internal expertise
  7. Creating FAQ documents
  8. Standardizing terminology
  9. Establishing peer review processes
  10. Maintaining knowledge repositories
  11. Updating teams on regulatory changes
  12. Measuring training effectiveness
Module 12. Maturity Assessment and Continuous Improvement
Evaluate and enhance your AI vendor risk program
12 chapters in this module
  1. Assessing program maturity levels
  2. Benchmarking against industry standards
  3. Identifying improvement opportunities
  4. Setting strategic goals
  5. Measuring efficiency gains
  6. Tracking risk reduction outcomes
  7. Demonstrating value to leadership
  8. Planning resource allocation
  9. Integrating lessons from audits
  10. Adapting to new AI capabilities
  11. Future-proofing assessment frameworks
  12. Contributing to industry best practices

How this maps to your situation

  • Evaluating AI-powered educational tools
  • Assessing third-party analytics platforms
  • Onboarding AI-driven communication systems
  • Reviewing automated decision-making vendors

Before vs. after

Before
Uncertainty in evaluating AI vendors, inconsistent documentation, and reactive compliance approaches
After
Confidence in leading assessments, standardized processes, and proactive risk management

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 flexible, self-paced learning

If nothing changes
Organizations that delay structured AI vendor risk practices may face increased compliance exposure, inefficient evaluations, and diminished oversight capacity as AI adoption accelerates.

How this compares to the alternatives

Unlike generic cybersecurity or IT vendor courses, this program focuses exclusively on AI-specific compliance challenges with implementation-grade tools and real-world templates tailored for regulated environments.

Frequently asked

Who is this course designed for?
Compliance Officers, Risk Managers, and Governance Professionals responsible for overseeing AI vendor relationships in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or policy-focused?
It bridges both domains, offering policy-aligned frameworks with technical depth to enable effective oversight without requiring data science expertise.
$199 one-time. Approximately 3 hours per module, designed for flexible, self-paced learning.

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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