AI Application Audit Test Plan

1. Introduction

1.1 Purpose

This test plan provides auditors, compliance practitioners, and security professionals with a structured framework to audit AI applications that use large language models (LLMs) and are built on modern MLOps platforms. It is intended to ensure that organizations meet rigorous security, governance, and vulnerability management standards—supporting regulatory and industry requirements (e.g., NIST SP 800-53, ISO/IEC 27001, OWASP, FedRAMP, and SOC 2).

1.2 Scope

This document covers:

• LLM Application Layer: Evaluation of both closed (proprietary) and open LLMs and how they are integrated within the application.
• MLOps Infrastructure: Controls related to continuous integration/continuous deployment (CI/CD), container orchestration, and secure pipeline management.
• Data Management: Processes for managing training data, ensuring data quality, mitigating bias, and supporting RAG architectures.
• Inference Engine Security: Safeguards for API endpoints, authentication, rate limiting, and monitoring of inference services.
• Vulnerability Management: Procedures for the identification, remediation, and resolution of vulnerabilities at the dependency, source code, and infrastructure layers.

1.3 Audience

This plan is intended for ISACA auditors, internal audit teams, IT security and compliance professionals, and AI/ML governance practitioners.

2. Audit Objectives

• Evaluate Control Design & Implementation: Assess whether the AI application controls are appropriately designed and implemented across the LLM, MLOps, data, and inference layers.
• Test Operational Effectiveness: Verify that the controls operate consistently over the defined observation period.
• Review Vulnerability Management Processes: Ensure there is a systematic process for identifying, tracking, and remediating vulnerabilities across dependencies, source code, and underlying infrastructure.
• Validate Data Governance: Confirm that data used for training and RAG is managed securely, with appropriate access controls, privacy measures, and bias mitigation.
• Ensure Secure Inference Operations: Evaluate that inference endpoints are protected from unauthorized access and abuse.

3. Testing Framework and Methodology

The audit approach follows these phases:

1. Documentation Review: Gather policies, procedures, configuration settings, and architectural diagrams related to the AI application environment.
2. Interviews and Walkthroughs: Interview stakeholders (data scientists, MLOps engineers, security teams) to understand operational practices.
3. Control Testing: Execute tests on individual controls using sample reviews, automated scanning tools, and manual inspections.
4. Vulnerability Scanning & Remediation Verification: Use automated tools and manual review to confirm vulnerabilities are identified and resolved promptly.
5. Reporting & Follow-Up: Document findings and recommendations, ensuring traceability to relevant frameworks and standards.

4. Test Plan: LLM Application Layer

4.1 Overview

Focus on the integration of large language models (closed and open) into the application. This includes:

• Secure integration and configuration
• Access controls around the LLM API
• Monitoring and logging of LLM usage

4.2 Control Testing Table

5. Test Plan: MLOps Infrastructure

5.1 Overview

Assess the security of the MLOps pipeline, including CI/CD processes, container security, and deployment practices.

5.2 Control Testing Table

6. Test Plan: Data Management (Training, RAG)

6.1 Overview

Focus on the governance of data used for training models and retrieval augmented generation. This includes data privacy, integrity, and bias mitigation.

6.2 Control Testing Table

7. Test Plan: Inference Engine Security

7.1 Overview

Evaluate the security controls for the inference layer, including API endpoints, throttling, and response monitoring.

7.2 Control Testing Table

8. Vulnerability Management & Remediation

8.1 Overview

This section focuses on how vulnerabilities in the AI application are identified, tracked, and resolved. It includes controls over dependencies, source code, and underlying infrastructure.

8.2 Control Testing Table

9. Reporting, Remediation, and Continuous Monitoring

• Audit Reporting:
  
  • Compile findings into a comprehensive audit report with actionable remediation recommendations.
  • Map each finding to relevant controls and regulatory frameworks.
• Remediation Planning:
  
  • Ensure that remediation timelines are defined and tracked.
  • Verify that patch management, configuration changes, and process updates are completed and re-tested.
• Continuous Monitoring:
  
  • Integrate continuous monitoring solutions to detect deviations in real time.
  • Establish regular review cycles to assess the ongoing effectiveness of controls.

10. Conclusion

A robust AI application audit test plan is essential for organizations leveraging LLMs and MLOps to ensure security, compliance, and operational excellence. By systematically evaluating the LLM application layer, MLOps infrastructure, data management practices, inference security, and vulnerability management processes, organizations can reduce risk and ensure they meet or exceed regulatory and industry standards. This test plan provides a structured approach for ISACA auditors and security professionals to evaluate and validate AI application security controls, ultimately enabling continuous improvement and robust compliance postures.

11. Consolidated Table

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