Skip to main content
Image coming soon

Board-Level AI Audit Readiness for Audit Teams

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Board-Level AI Audit Readiness for Audit Teams

Implement AI governance frameworks that meet board expectations and audit standards

$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.
Audit teams are being asked to evaluate AI systems without clear frameworks, consistent terminology, or board-aligned benchmarks.

The situation this course is for

As AI adoption accelerates, audit functions face pressure to provide assurance on systems that are complex, dynamic, and often opaque. Without a structured approach, teams risk delivering inconsistent assessments, missing critical control gaps, or failing to communicate risk in terms decision-makers can act on.

Who this is for

Business and technology professionals in audit, risk, compliance, and governance roles who are tasked with evaluating AI systems and reporting to senior leadership or oversight bodies.

Who this is not for

This course is not for data scientists building AI models or executives seeking high-level overviews. It is specifically designed for audit practitioners who need to implement repeatable, defensible review processes.

What you walk away with

  • Apply a standardized framework to assess AI system risk and control maturity
  • Translate technical AI behaviors into audit-relevant findings and recommendations
  • Align AI audit practices with board-level governance expectations
  • Document reviews using templates that support traceability and accountability
  • Lead cross-functional discussions with data, legal, and compliance teams using shared language

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Audit in Governance
Establish the core principles linking AI systems to audit accountability and board oversight.
12 chapters in this module
  1. Defining AI in the context of audit scope
  2. Mapping AI risk to organizational governance frameworks
  3. Key roles in AI oversight: board, audit, compliance, and operations
  4. Regulatory signals shaping AI audit expectations
  5. Distinguishing AI audits from traditional IT audits
  6. The lifecycle approach to AI system review
  7. Common misconceptions about AI and auditability
  8. Building credibility in early-stage AI assessments
  9. Integrating AI into existing audit planning cycles
  10. Establishing audit terminology for AI systems
  11. Documenting assumptions in AI review scoping
  12. Preparing for evolving definitions of 'audit-ready' AI
Module 2. AI Risk Taxonomy for Auditors
Classify AI risks using a structured taxonomy aligned with audit control objectives.
12 chapters in this module
  1. Categorizing AI risks: fairness, robustness, transparency, and accountability
  2. Mapping model types to risk profiles
  3. Data provenance and its audit implications
  4. Versioning and drift as control concerns
  5. Third-party AI and vendor risk assessment
  6. Human-in-the-loop and oversight failure modes
  7. Bias detection at scale: what auditors should look for
  8. Security vulnerabilities unique to AI systems
  9. Emerging risks in generative AI applications
  10. Risk interaction: how AI amplifies existing control gaps
  11. Prioritizing risks for board-level reporting
  12. Risk communication frameworks for non-technical stakeholders
Module 3. Control Frameworks for AI Systems
Adapt established control models to the unique behaviors of AI-driven processes.
12 chapters in this module
  1. Applying COSO and COBIT to AI environments
  2. Designing controls for model training and retraining
  3. Monitoring inference behavior in production
  4. Input validation and adversarial attack resistance
  5. Output consistency and explainability requirements
  6. Logging and audit trail design for AI systems
  7. Access controls for model parameters and datasets
  8. Change management for AI components
  9. Fail-safe and fallback mechanism verification
  10. Control testing strategies for probabilistic systems
  11. Automated control monitoring with AI observability tools
  12. Integrating AI controls into SOX and other compliance regimes
Module 4. Assessment Methodology Development
Build a repeatable, evidence-based methodology for evaluating AI systems.
12 chapters in this module
  1. Scoping AI audits: defining boundaries and objectives
  2. Gathering documentation from model owners
  3. Interview techniques for data science and MLOps teams
  4. Reviewing model cards, data sheets, and system logs
  5. Validating training data representativeness
  6. Assessing model validation processes
  7. Evaluating model monitoring in production
  8. Testing for unintended behavior and edge cases
  9. Reviewing incident response plans for AI failures
  10. Assessing model retirement and decommissioning
  11. Documenting findings with audit trail integrity
  12. Creating assessment reports for technical and executive audiences
Module 5. Board Communication and Reporting
Structure findings and recommendations for board-level understanding and action.
12 chapters in this module
  1. Translating technical findings into business risk language
  2. Designing executive summaries for AI audit outcomes
  3. Visualizing AI risk and control maturity for leadership
  4. Aligning reports with enterprise risk appetite statements
  5. Highlighting strategic implications of audit findings
  6. Reporting on AI ethics and societal impact concerns
  7. Benchmarking against peer organization practices
  8. Presenting AI audit results in board packs
  9. Anticipating board questions and follow-ups
  10. Linking AI audit outcomes to investment decisions
  11. Documenting board engagement on AI oversight
  12. Creating ongoing reporting cadences for AI risk
Module 6. AI Audit Program Design
Scale individual assessments into a sustainable audit function capability.
12 chapters in this module
  1. Developing an AI audit charter and mandate
  2. Resourcing the AI audit function: skills and tools
  3. Integrating AI into annual audit planning
  4. Building relationships with data science and AI teams
  5. Creating an AI audit knowledge base
  6. Standardizing templates and workflows
  7. Training auditors on AI fundamentals
  8. Managing stakeholder expectations
  9. Measuring the effectiveness of AI audits
  10. Continuous improvement of the audit program
  11. Benchmarking program maturity
  12. Scaling across geographies and business units
Module 7. Compliance Alignment and Regulatory Readiness
Ensure AI audits meet current and emerging regulatory expectations.
12 chapters in this module
  1. Understanding EU AI Act requirements for audit
  2. Mapping to NIST AI Risk Management Framework
  3. Aligning with OECD AI Principles
  4. Preparing for SEC disclosure rules on AI
  5. Adapting to evolving FTC guidance on AI
  6. Meeting financial services regulatory expectations
  7. Healthcare AI compliance and audit considerations
  8. Privacy and data protection in AI systems
  9. Cross-border data and model deployment issues
  10. Third-party audit and certification options
  11. Preparing for regulatory examinations
  12. Maintaining audit independence in compliance reviews
Module 8. AI Incident Response and Forensics
Audit the organization's readiness to respond to AI failures.
12 chapters in this module
  1. Defining AI incidents: classification and severity levels
  2. Reviewing incident detection capabilities
  3. Assessing response playbooks for AI failures
  4. Auditing post-incident root cause analysis
  5. Evaluating model rollback and remediation processes
  6. Testing communication plans for AI incidents
  7. Reviewing stakeholder notification procedures
  8. Auditing lessons learned and process updates
  9. Monitoring recurrence of known issues
  10. Assessing legal and reputational risk management
  11. Documenting incident history for board review
  12. Stress-testing incident response plans
Module 9. Third-Party and Vendor AI Audits
Extend audit practices to externally sourced AI systems and services.
12 chapters in this module
  1. Assessing vendor AI maturity and transparency
  2. Reviewing third-party model documentation
  3. Auditing API-based AI services
  4. Evaluating vendor monitoring and support
  5. Managing model updates from external providers
  6. Assessing data handling in vendor environments
  7. Reviewing contract terms for audit rights
  8. Conducting remote or desktop audits of vendor AI
  9. Validating vendor risk assessments
  10. Handling proprietary model limitations
  11. Benchmarking vendor performance and reliability
  12. Exit strategies and model replacement planning
Module 10. Generative AI Audit Specialization
Address the unique challenges of auditing generative AI systems.
12 chapters in this module
  1. Understanding generative model architectures
  2. Assessing prompt engineering risks
  3. Reviewing content moderation and filtering
  4. Auditing training data for copyright and IP risks
  5. Evaluating hallucination and factual accuracy controls
  6. Monitoring output for harmful or biased content
  7. Assessing data leakage and privacy exposure
  8. Reviewing user interaction logging
  9. Auditing fine-tuning and customization processes
  10. Evaluating model watermarking and provenance
  11. Assessing supply chain risks in foundation models
  12. Reporting on generative AI innovation vs. risk trade-offs
Module 11. AI Audit Evidence and Documentation
Establish defensible, auditable records of AI system evaluations.
12 chapters in this module
  1. Defining sufficient and appropriate audit evidence for AI
  2. Capturing model metadata and configuration
  3. Archiving training data samples and preprocessing logic
  4. Documenting testing procedures and results
  5. Maintaining version control for audit artifacts
  6. Securing audit data and findings
  7. Ensuring reproducibility of audit steps
  8. Using timestamps and digital signatures
  9. Linking findings to control objectives
  10. Creating audit trails for AI decision support
  11. Storing evidence for regulatory retention periods
  12. Preparing documentation for external review
Module 12. Future-Proofing the AI Audit Function
Anticipate emerging trends and build long-term audit capability.
12 chapters in this module
  1. Tracking advancements in AI research and practice
  2. Adapting to new model types and architectures
  3. Incorporating AI audit into digital transformation
  4. Building partnerships with research and innovation teams
  5. Upskilling audit staff on emerging AI methods
  6. Leveraging AI to enhance audit processes
  7. Balancing innovation and risk in audit design
  8. Engaging with industry consortia and standards bodies
  9. Sharing best practices without compromising confidentiality
  10. Anticipating next-generation regulatory expectations
  11. Measuring the strategic value of AI audit
  12. Positioning the audit function as a trusted AI advisor

How this maps to your situation

  • Your team is beginning to assess AI systems but lacks a consistent framework.
  • You're preparing for board questions about AI risk and need credible responses.
  • Regulatory scrutiny is increasing, and you need to demonstrate audit readiness.
  • You're building an AI governance program and need audit integration.

Before vs. after

Before
Uncertainty about how to assess AI systems, inconsistent documentation, and difficulty communicating risk to leadership.
After
A structured, repeatable audit process that produces clear, board-ready findings and strengthens organizational AI governance.

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 flexible, self-paced learning.

If nothing changes
Without a formal approach, audit teams may deliver inconsistent assessments, miss critical risks, or fail to gain leadership trust, limiting their influence in shaping responsible AI adoption.

How this compares to the alternatives

Unlike high-level overviews or technical deep dives, this course is tailored specifically for audit professionals who need actionable, implementation-grade guidance, not theory or code.

Frequently asked

Who is this course designed for?
Audit, risk, and compliance professionals leading or contributing to AI system evaluations in regulated environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical?
It is implementation-focused, not technical. It equips auditors to evaluate AI systems without requiring data science expertise.
$199 one-time. Approximately 3-4 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
!