AI Audit Methodology

Comprehensive methodology for auditing AI systems including planning, evidence collection, testing procedures, report templates, and integration with red team assessments.

An AI audit systematically evaluates whether an organization's AI systems meet defined standards, regulatory requirements, and internal policies. While distinct from 紅隊演練 -- which focuses on 對抗性 利用 -- auditing and 紅隊演練 are complementary activities. Red team findings provide critical evidence for auditors, and audit frameworks help red teamers 理解 which controls to 測試 and how to report findings.

Audit Planning

Pre-Audit Activities

Audit Team Competencies

AI audits require a mix of skills not typically found in traditional audit teams:

Scope Definition Framework

Evidence Collection

Types of Audit Evidence

Evidence Collection Procedures

1. 1

   Document request

   Issue a formal document request list to the organization covering all in-scope control areas. Allow adequate time for collection (typically 2-4 weeks).

   Standard document requests for AI audits:

   • AI governance policies and procedures
   • AI system inventory with risk classifications
   • Model development documentation (model cards, 訓練 procedures)
   • Data governance documentation (data lineage, quality procedures)
   • Risk assessments and impact assessments
   • Incident reports and response records
   • Change management records for AI systems
   • 監控 and alerting configurations
   • Red team and 安全 評估 reports
   • Training records for AI development and operations staff
2. 2

   Interview planning

   Schedule interviews with key personnel across AI governance, development, operations, and risk management functions.

   Key interview subjects:

   • AI governance lead / responsible AI officer
   • AI/ML engineering leads
   • Data engineering leads
   • 安全 and 紅隊 leads
   • Risk management and compliance
   • Business stakeholders using AI outputs
3. 3

   Technical 測試

   Plan and execute technical 測試 activities. 這是 where 紅隊 capabilities integrate directly.

   測試 areas:

   • Control effectiveness 測試 (does the control work as documented?)
   • Configuration review (are systems configured according to policy?)
   • 對抗性 測試 (do controls hold under attack?)
   • Performance validation (does the AI system perform as documented?)
4. 4

   Evidence preservation

   Maintain a structured evidence repository with clear chain of custody.

   Evidence management requirements:

   • Unique identifier 對每個 evidence item
   • Date and time of collection
   • Source of evidence
   • Collector name and role
   • Control objective the evidence supports
   • Integrity verification (hash for digital evidence)

Evidence Quality Criteria

測試 Procedures

Control 測試 Approaches

AI-Specific 測試 Procedures

Model governance 測試:

Data governance 測試:

安全 control 測試 (紅隊 integration):

Fairness and bias 測試:

Audit Reporting

Report Structure

Finding Classification

Finding Documentation Template

紅隊 Integration Points

Where 紅隊演練 Fits in the Audit

Translating 紅隊 Findings to Audit Language

Red teams and auditors use different terminology. This translation table helps:

Continuous Audit Considerations

Traditional audits are point-in-time assessments. For AI systems that change frequently, 考慮 continuous audit approaches:

Continuous Audit Architecture

AI System → 監控 Layer → Evidence Repository → Analysis Engine → Dashboard
    │              │                    │                    │              │
    ├── Logs       ├── Automated tests  ├── Evidence store   ├── Compliance  ├── Alerts
    ├── Metrics    ├── Drift 偵測  ├── Chain of custody ├── Trend       ├── Reports
    └── Events     └── Red team tests   └── Audit trail      └── Gap detect  └── KPIs

This architecture enables organizations to maintain audit readiness at all times rather than scrambling to prepare for periodic assessments. Red team 測試 results feed continuously into the evidence repository, providing ongoing assurance of control effectiveness.