Skip to main content
Image coming soon

Compliance-Ready AI Vendor Risk Assessment for Audit Teams

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Compliance-Ready AI Vendor Risk Assessment for Audit Teams

Master the structured evaluation of AI vendors with audit-grade rigor and governance alignment

$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 outpacing risk controls, leaving audit teams scrambling to assess vendors without standardized methods

The situation this course is for

Audit and compliance professionals are increasingly asked to evaluate AI vendors but lack consistent, defensible frameworks. Existing guidance is too high-level or too technical, creating gaps in coverage, inconsistent findings, and delayed approvals. Without a unified approach, teams risk either over-relying on vendor claims or blocking innovation due to unmanageable uncertainty.

Who this is for

Business and technology professionals in audit, compliance, risk, or governance roles who engage with third-party AI solutions and need to deliver credible, standards-aligned assessments

Who this is not for

This is not for software developers building AI models or data scientists focused on algorithmic performance. It is not for executives seeking high-level overviews without implementation detail.

What you walk away with

  • Apply a standardized framework to assess AI vendor risk across technical, operational, and compliance dimensions
  • Align vendor evaluations with regulatory expectations including data protection, model transparency, and accountability
  • Generate audit-ready documentation using structured templates and checklists
  • Navigate vendor negotiations with confidence using predefined control thresholds and red-line criteria
  • Lead cross-functional AI governance discussions with a common language and methodology

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Audit Contexts
Establish the core principles of AI risk as they apply to third-party assessment and audit readiness
12 chapters in this module
  1. Defining AI vendor risk in regulated environments
  2. The evolving role of audit in AI governance
  3. Key regulatory touchpoints for third-party AI
  4. Distinguishing AI risk from traditional software risk
  5. Stakeholder mapping: aligning audit with legal, security, and procurement
  6. Risk tolerance and escalation pathways
  7. Overview of industry frameworks and standards
  8. Building a risk taxonomy for vendor assessment
  9. Common failure patterns in AI vendor engagements
  10. The audit lifecycle and AI vendor touchpoints
  11. Establishing governance boundaries
  12. Preparing for dynamic risk re-evaluation
Module 2. Scoping AI Vendor Engagements
Define assessment boundaries based on use case, data sensitivity, and impact level
12 chapters in this module
  1. Classifying AI use cases by risk tier
  2. Mapping data flows in vendor-hosted systems
  3. Determining criticality and business impact
  4. Identifying regulated data handling requirements
  5. Assessing autonomy and decision-making authority
  6. Defining scope boundaries with stakeholders
  7. Vendor ecosystem mapping: direct and indirect dependencies
  8. Using categorization to prioritize assessments
  9. Documenting assumptions and constraints
  10. Establishing change control for scope adjustments
  11. Integrating scoping with procurement timelines
  12. Outputting a scoping memorandum
Module 3. Control Frameworks for AI Vendor Assessment
Adapt established control standards to AI-specific risks and vendor delivery models
12 chapters in this module
  1. Mapping NIST AI RMF to vendor evaluation
  2. Applying ISO/IEC 42001 controls to third parties
  3. Integrating SOC 2 with AI-specific criteria
  4. Customizing control sets by risk tier
  5. Evaluating model development lifecycle controls
  6. Assessing data provenance and quality management
  7. Validating testing and validation practices
  8. Reviewing model monitoring and drift detection
  9. Auditing incident response and disclosure readiness
  10. Evaluating human oversight and intervention
  11. Assessing red teaming and adversarial testing
  12. Documenting control gaps and compensating measures
Module 4. Technical Risk Evaluation for Non-Engineers
Interpret technical documentation and findings without requiring data science expertise
12 chapters in this module
  1. Reading model cards for risk signals
  2. Understanding data sheets and provenance reports
  3. Interpreting bias and fairness assessments
  4. Evaluating explainability and interpretability claims
  5. Assessing model performance metrics in context
  6. Reviewing training data composition and sourcing
  7. Identifying overfitting and generalization risks
  8. Understanding deployment architecture implications
  9. Assessing API security and access controls
  10. Evaluating system resilience and failover design
  11. Reviewing logging and monitoring capabilities
  12. Translating technical findings into audit findings
Module 5. Compliance Alignment and Regulatory Mapping
Ensure vendor assessments meet current regulatory expectations across jurisdictions
12 chapters in this module
  1. Mapping AI risk to GDPR and data protection laws
  2. Aligning with sector-specific rules (finance, healthcare, etc)
  3. Addressing algorithmic accountability requirements
  4. Preparing for upcoming AI Act-style regulations
  5. Demonstrating due diligence in vendor selection
  6. Documenting compliance rationale for auditors
  7. Handling cross-border data transfer implications
  8. Evaluating vendor adherence to ethical AI principles
  9. Assessing transparency and disclosure obligations
  10. Reviewing recordkeeping and audit trail requirements
  11. Aligning with board-level governance expectations
  12. Updating assessments for regulatory changes
Module 6. Contractual Risk Mitigation
Incorporate audit rights, performance guarantees, and exit strategies into vendor agreements
12 chapters in this module
  1. Defining audit rights and access provisions
  2. Specifying model performance benchmarks
  3. Establishing update and retraining expectations
  4. Negotiating access to model documentation
  5. Requiring bias and fairness monitoring reports
  6. Including data deletion and portability clauses
  7. Defining incident notification timelines
  8. Securing right-to-explain for end users
  9. Building in third-party assessment rights
  10. Planning for vendor lock-in and exit strategies
  11. Addressing intellectual property and model ownership
  12. Documenting contractual risk mitigation outcomes
Module 7. Vendor Due Diligence Execution
Conduct structured assessments using questionnaires, interviews, and evidence review
12 chapters in this module
  1. Designing risk-based vendor questionnaires
  2. Tailoring questions by AI use case and tier
  3. Validating responses with evidence requests
  4. Conducting vendor interviews with audit focus
  5. Assessing organizational maturity and governance
  6. Evaluating vendor security and compliance posture
  7. Reviewing certifications and third-party reports
  8. Triangulating claims with technical artifacts
  9. Documenting due diligence activities
  10. Managing vendor response delays and omissions
  11. Assessing subcontractor and supply chain risk
  12. Finalizing due diligence findings
Module 8. Risk Scoring and Prioritization
Apply consistent scoring methods to compare vendors and focus audit attention
12 chapters in this module
  1. Designing a risk scoring matrix
  2. Weighting factors by impact and likelihood
  3. Scoring data sensitivity and exposure
  4. Evaluating model opacity and interpretability
  5. Assessing vendor transparency and cooperation
  6. Incorporating organizational and financial stability
  7. Scoring third-party dependencies and supply chain
  8. Aggregating scores across domains
  9. Using scoring to determine assessment depth
  10. Documenting risk scoring rationale
  11. Presenting risk scores to stakeholders
  12. Updating scores over time
Module 9. Audit Trail Design and Evidence Management
Create defensible, reproducible records of vendor assessments
12 chapters in this module
  1. Defining evidence requirements for each control
  2. Structuring documentation for audit readiness
  3. Versioning and change tracking for assessments
  4. Storing sensitive vendor information securely
  5. Linking findings to risk ratings and decisions
  6. Creating summary reports for leadership
  7. Maintaining independence and objectivity
  8. Documenting exceptions and compensating controls
  9. Using templates for consistency
  10. Preparing for internal and external audit review
  11. Archiving assessment records
  12. Ensuring retention compliance
Module 10. Cross-Functional Coordination
Lead alignment between audit, legal, security, procurement, and business units
12 chapters in this module
  1. Identifying key stakeholders and their concerns
  2. Establishing governance forums for AI vendor review
  3. Creating shared definitions and risk language
  4. Integrating assessment into procurement workflows
  5. Coordinating with data protection officers
  6. Aligning with enterprise risk management
  7. Facilitating decision-making on high-risk vendors
  8. Communicating findings across departments
  9. Managing conflicting priorities and incentives
  10. Documenting cross-functional agreements
  11. Building consensus on risk acceptance
  12. Scaling coordination across multiple vendors
Module 11. Ongoing Monitoring and Reassessment
Design continuous oversight practices for long-term vendor relationships
12 chapters in this module
  1. Defining reassessment frequency by risk tier
  2. Monitoring for model updates and retraining
  3. Tracking vendor incidents and disclosures
  4. Reviewing performance trends over time
  5. Updating risk assessments with new information
  6. Triggering reassessment based on events
  7. Using automated monitoring tools
  8. Conducting periodic control validation
  9. Engaging vendors for annual compliance updates
  10. Assessing changes in vendor ownership or structure
  11. Managing sunset and transition planning
  12. Documenting ongoing monitoring activities
Module 12. Implementation and Scaling Across the Enterprise
Deploy a standardized AI vendor risk program across multiple teams and use cases
12 chapters in this module
  1. Assessing organizational readiness for AI risk program
  2. Defining roles and responsibilities
  3. Training audit and procurement teams
  4. Integrating with existing risk and compliance platforms
  5. Creating centralized vendor risk registers
  6. Standardizing templates and workflows
  7. Measuring program effectiveness
  8. Reporting to executive leadership and board
  9. Iterating based on feedback and outcomes
  10. Scaling to new business units
  11. Benchmarking against industry peers
  12. Maintaining continuous improvement

How this maps to your situation

  • Audit teams entering AI vendor review for the first time
  • Compliance officers building AI governance frameworks
  • Risk managers expanding third-party risk programs to include AI
  • Procurement specialists needing structured evaluation criteria

Before vs. after

Before
Unstructured evaluations, inconsistent findings, and reactive responses to AI vendor risks
After
A repeatable, audit-ready process for assessing AI vendors with confidence and clarity

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 45, 60 hours total, designed for flexible, self-paced completion over 6, 8 weeks.

If nothing changes
Without a structured approach, organizations risk either blocking innovation due to unmanaged risk or exposing themselves to compliance gaps, reputational harm, and operational failures from poorly vetted AI vendors.

How this compares to the alternatives

Unlike generic AI ethics guides or technical model cards, this course delivers audit-specific, implementation-grade methodology tailored to compliance professionals who must produce defensible assessments under real-world constraints.

Frequently asked

Who is this course designed for?
Audit, compliance, risk, and governance professionals who assess third-party AI vendors and need to produce credible, standards-aligned evaluations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is technical AI knowledge required?
No. The course is designed for professionals without data science backgrounds, focusing on audit-relevant evaluation criteria and risk interpretation.
$199 one-time. Approximately 45, 60 hours total, designed for flexible, self-paced completion over 6, 8 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
!