Learning Objectives:

Within this domain, the AI auditor should be able to:

• Evaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.
• Evaluate AI solutions to advise on impact, opportunities, and risk to organization.
• Evaluate the impact of AI solutions on system interactions, environment, and humans.
• Evaluate the role and impact of AI decision-making systems on the organization and stakeholders.
• Evaluate the organization’s AI policies and procedures, including compliance with legal and regulatory requirements.
• Evaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.
• Evaluate whether the organization has defined ownership of AI-related risk, controls, procedures, decisions, and standards.
• Evaluate the organization’s data governance program specific to AI.
• Evaluate the organization’s privacy program specific to AI.
• Evaluate the organization’s problem and incident management programs specific to AI.
• Evaluate the organization’s change management program specific to AI.
• Evaluate the organization’s configuration management program specific to AI.
• Evaluate the organization’s threat and vulnerability management programs specific to AI.
• Evaluate the organization’s identity and access management program specific to AI.
• Evaluate vendors and supply chain management program specific to AI solutions.
• Evaluate the design and effectiveness of controls specific to AI.
• Evaluate data inputs requirements for AI models (e.g., data appropriateness, bias, and privacy).
• Evaluate system/business requirements for AI solutions to ensure alignment with enterprise architecture.
• Evaluate AI solution life cycle (e.g., design, development, deployment, monitoring, and decommissioning) and inputs/outputs for compliance and risk.
• Evaluate algorithms and models to ensure AI solutions are aligned to business objectives, policies, and procedures.
• Analyze the impact of AI on the workforce to advise stakeholders on how to address AI related workforce impacts, training, and education.
• Evaluate that awareness programs align to the organization’s AI-related policies and procedures.

Section A. AI Models, Considerations, and Requirements

1. Types of AI

• Generative • Predictive • Narrow • General

2. Machine learning/AI Models

• Basic models • Neural networks

3. Algorithms

• Classes of Algorithms • Additional AI Considerations (technical terms and concepts relevant to the IS auditor)

4. AI Lifecycle Overview

• Plan and Design • Collect and Process Data • Build and/or Adapt Model(s) • Test, Evaluate, Verify, and Validate • Make Available for Use/Deploy • Operate and Monitor • Retire/Decommission

5. Business Considerations

• Business Use Cases, Needs, Scope, and Objectives • Cost-Benefit Analysis • Return on Investment • Internal vs. Cloud Hosting • Vendors • Shared Responsibility

Section B. AI Governance and Program Management

1. AI Strategy

• Strategies • Opportunities • Vision and Mission • Value Alignment

2. AI-related Roles and Responsibilities

• Categories, Focuses, and Common Examples

3. AI-related Policies and Procedures

• Usage Policies

4. AI Training and Awareness

• Skills, Knowledge, and Competencies

5. Program metrics

• Examples of Metrics with Objectives and Definitions

Section C. AI Risk Management

1. AI-related Risk Identification

• AI Threat Landscape • AI Risks • Challenges for AI Risk Management

2. Risk Assessment

• Risk Assessment • Risk Appetite and Tolerance • Risk Mitigation and Prioritization • Remediation Plans/Best Practices

3. Risk Monitoring

• Continuous Improvement • Risk and Performance Metrics

Section D. Privacy and Data Governance Programs

1. Data Governance

• Data Classification • Data Clustering • Data Licensing • Data Cleansing and Retention

2. Privacy Considerations

• Data Privacy • Data Ownership (Governance and Privacy)

3. Privacy Regulatory Considerations

• Data Consent • Collection, Use, and Disclosure

Section E. Leading Practices, Ethics, Regulations, and Standards for AI

1. Standards, Frameworks, and Regulations Related to AI

• Best Practices • Industry Standards and Frameworks • Laws and Regulations

2. Ethical Considerations

• Ethical Use • Bias and Fairness • Transparency and Explainability • Trust and Safety • IP Considerations • Human Rights

Learning Objectives:

Within this domain, the AI auditor should be able to:

• Evaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.
• Evaluate AI solutions to advise on impact, opportunities, and risk to organization.
• Evaluate the impact of AI solutions on system interactions, environment, and humans.
• Evaluate the role and impact of AI decision-making systems on the organization and stakeholders.
• Evaluate the organization’s AI policies and procedures, including compliance with legal and regulatory requirements.
• Evaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.
• Evaluate whether the organization has defined ownership of AI-related risk, controls, procedures, decisions, and standards.
• Evaluate the organization’s data governance program specific to AI.
• Evaluate the organization’s privacy program specific to AI.
• Evaluate the organization’s problem and incident management programs specific to AI.
• Evaluate the organization’s change management program specific to AI.
• Evaluate the organization’s configuration management program specific to AI.
• Evaluate the organization’s threat and vulnerability management programs specific to AI.
• Evaluate the organization’s identity and access management program specific to AI.
• Evaluate vendors and supply chain management program specific to AI solutions.
• Evaluate the design and effectiveness of controls specific to AI.
• Evaluate data inputs requirements for AI models (e.g., data appropriateness, bias, and privacy).
• Evaluate system/business requirements for AI solutions to ensure alignment with enterprise architecture.
• Evaluate AI solution life cycle (e.g., design, development, deployment, monitoring, and decommissioning) and inputs/outputs for compliance and risk.
• Evaluate algorithms and models to ensure AI solutions are aligned to business objectives, policies, and procedures.
• Analyze the impact of AI on workforce to advise stakeholders to address AI-related workforce impacts, training, and education.
• Evaluate that awareness programs align to the organization’s AI-related policies and procedures.

Section A. Data Management Specific to AI

1. Data Collection

• Consent • Fit for Purpose • Data Lag

2. Data Classification

3. Data Confidentiality

4. Data Quality

5. Data Balancing

6. Data Scarcity

7. Data Security

• Data Encoding • Data Access • Data Secrecy • Data Replication • Data Backup

Section B. AI Solution Development Methodologies and Lifecycle

1. AI Solution Development Life Cycle

• Use Case Development • Design • Development • Deployment • Monitoring and Maintenance • Decommission

2. Privacy and Security by Design

• Explainability • Robustness

Section C. Change Management Specific to AI

1. Change Management Considerations

• Data Dependency • AI Model • Regulatory and Societal Impact • Emergency Changes • Configuration Management

Section D. Supervision of AI Solutions

1. AI Agency

• Logging and Monitoring • AI Observability • Human in the Loop (HITL) • Hallucination Section E. Testing Techniques for AI Solutions 1. Conventional Software Testing Techniques • A/B Testing • Unit and Integration Testing • Objective Verification • Code Reviews • Black Box Testing

2. AI-Specific Testing Techniques

• Model Cards • Bias Testing • Adversarial Testing

Section F. Threats and Vulnerabilities Specific to AI

1. Types of AI-related Threats

• Training Data Leakage • Data Poisoning • Model Poisoning • Model Theft • Prompt Injections • Model Evasion • Model Inversion • Threats for Using Vendor Supplied AI • AI Solution Disruption

2. Controls for AI-related Threats

• Threat and Vulnerability Identification • Prompt Templates 6 • Defensive Distillation • Regularization

Section G. Incident Response Management Specific to AI

1. Prepare

• Policies, Procedures, and Model Documentation • Incident Response Team • Tabletop Exercises

2. Identify and Report

3. Assess

4. Respond

• Containment • Eradication • Recovery

5. Post-Incident Review

Learning Objectives:

Within this domain, the AI auditor should be able to:

• Evaluate impacts, opportunities, and risk when integrating AI solutions within the audit process.
• Utilize AI solutions to enhance audit processes, including planning, execution, and reporting.
• Evaluate the monitoring and reporting of metrics (e.g., KPIs, KRIs) specific to AI.
• Evaluate data input requirements for AI models (e.g., data appropriateness, bias, and privacy).

Section A. Audit Planning and Design

1. Identification of AI Assets and Controls

• Inventory Objective and Procedure • Inventory and Data Gathering Methods • Documentation • Surveys • Interviews

2. Types of AI Controls

• Examples including Control Categories, Controls, and Explanations

3. Audit Use Cases

• Large Language Models • Audit Process Improvement • Generative AI • Audit-Specific AI Applications

4. Internal Training for AI Use

• Key Components for Auditor Knowledge • Practical Skills Development

Section B. Audit Testing and Sampling Methodologies

1. Designing an AI Audit

• AI Audit Objectives • Audit Scoping and Resources

2. AI Audit Testing Methodologies

• AI Systems Overall Testing • Financial Models

3. AI Sampling

• Judgmental sampling • AI sampling

4. Outcomes of AI testing

• Reduce false positives • Reduce workforce needs • Outliers

Section C. Audit Evidence Collection Techniques

1. Data Collection

• Training and Testing Data • Unstructured and Structured Data Collection • Extract, Transform, and Load • Data Manipulation • Scraping

2. Walkthroughs and interviews

• Design Interview Questions

3. AI Collection Tools

• Using AI to Collect Logs • AI agents to create outputs • Voice to Speech • Optimal Character Recognition

Section D. Audit Data Quality and Data Analytics

1. Data Quality

• Optimization

2. Data Analytics

• Sentiment Analysis • Run Data Analytics

3. Data Reporting

• Reports • Dashboards

Section E. AI Audit Outputs and Reports

1. Reports

• Report Types (examples and details) • Advisory Reports • Charts and Visualizations

2. Audit Follow-up

• Automated follow-up

3. Quality Assurance