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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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1. Introduction

1.1 Purpose

This document provides a structured test plan to assess the application security controls required for Federal Risk and Authorization Management Program (FedRAMP) compliance. The objective is to ensure that organizations implementing secure software development practices, API security controls, vulnerability management, and third-party component security can effectively demonstrate compliance with FedRAMP Moderate and High Baselines, as defined in NIST SP 800-53 Rev. 5 and the FedRAMP Security Control Baselines.

1.2 Scope

This test plan is specifically focused on application security controls relevant to FedRAMP Moderate and High Baselines, mapped to NIST 800-53 Rev. 5 security control families:

• System and Communications Protection (SC-xx) – Required
• Access Control (AC-xx) – Required
• Risk Assessment (RA-xx) – Required
• Security Assessment and Authorization (CA-xx) – Required
• Configuration Management (CM-xx) – Required
• Contingency Planning (CP-xx) – If in scope

This document excludes broader infrastructure and operational security controls such as physical security, general IT operations, and non-application-specific monitoring, unless explicitly required for FedRAMP compliance.

2. Testing Framework and Methodology

FedRAMP assessments require that security controls be:

1. Implemented and Operational – The organization must have fully implemented controls that align with FedRAMP's required security baselines.
2. Continuously Monitored – Controls must be continuously assessed to ensure operational effectiveness.
3. Reviewed by an Accredited Third-Party Assessment Organization (3PAO) – Independent validation of controls is required before an agency can authorize the system.

The following test plan evaluates application security controls across key FedRAMP security control families to ensure compliance with FedRAMP Moderate and High Baselines.

3. Test Plan: FedRAMP Application Security Controls

The following table provides a detailed test plan for evaluating FedRAMP application security controls, including control descriptions, testing procedures, success criteria, and evidence requirements.

Table 1: FedRAMP Application Security Test Plan

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Purpose

This document provides a structured test plan for evaluating application security controls as part of a SOC 2 Type 2 readiness assessment. It covers key domains including secure development, application access controls, vulnerability management, API security, and third-party component security.

Scope

• In-Scope: Controls relevant to application security, including SDLC, authentication, API security, patching, logging, and vulnerability management.
• Out-of-Scope: General infrastructure/network security, physical security, and non-application-related operational security.

Test Plan

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How to Use This Test Plan

1. Assign Test Owners: Designate a responsible tester for each control.
2. Collect Evidence: Gather relevant logs, reports, and documentation.
3. Execute Tests: Follow the steps for each control.
4. Document Results: Mark controls as Pass/Fail and remediate gaps before the SOC 2 audit.
5. Reassess Periodically: Application security is ongoing—schedule periodic internal reviews.

Abstract

The proliferation of artificial intelligence (AI) in enterprise applications has introduced novel security challenges that extend beyond traditional software security paradigms. ISO 42001, the first international standard for AI management systems (AIMS), establishes a governance framework for AI risk management, emphasizing security, transparency, and accountability. While the standard primarily addresses organizational and ethical considerations, it has direct implications for application security, particularly in mitigating adversarial threats, securing AI supply chains, and ensuring model robustness.

This paper examines the security dimensions of ISO 42001, analyzing its impact on software security practices, AI-specific vulnerabilities, and the evolving requirements for AI-driven application security architectures. Additionally, we present a comprehensive ISO 42001 audit test plan, providing security practitioners with a reusable framework for assessing both organizational AI governance and AI product security against ISO 42001 requirements. The audit framework includes technical and procedural evaluations of AI-specific risk management, adversarial robustness, model integrity, and runtime security.

We argue that ISO 42001 necessitates a paradigm shift in software security methodologies, requiring the integration of AI-aware security controls within the secure software development lifecycle (SSDLC). By systematically aligning AI governance with application security practices, organizations can enhance compliance while proactively mitigating AI-driven security risks.

1. Introduction

The integration of AI into modern software architectures has redefined security considerations in application security. Unlike traditional software vulnerabilities, AI systems introduce unique attack surfaces, including adversarial manipulations, model inversion, and data poisoning. Existing application security methodologies—such as static application security testing (SAST), dynamic application security testing (DAST), and software composition analysis (SCA)—fail to comprehensively address these emerging risks.

ISO 42001 provides a structured approach for AI governance, yet its security implications remain underexplored in the context of application security. This paper examines the security dimensions of ISO 42001, highlighting the necessity for security practitioners to adapt AI-specific threat modeling, supply chain security mechanisms, and runtime security controls. Furthermore, a detailed audit framework is proposed to enable organizations to evaluate their security posture against ISO 42001 requirements.

2. Background: ISO 42001 and AI Security

2.1 Overview of ISO 42001

ISO 42001 is a risk-based AI governance framework analogous to ISO 27001, which defines information security management systems (ISMS). The standard mandates the implementation of structured policies to ensure AI transparency, bias mitigation, and compliance with emerging regulatory requirements such as the European Union AI Act and the U.S. Executive Order on AI Safety.

While the primary intent of ISO 42001 is governance, the standard implicitly introduces several security requirements, including:

• Threat modeling for AI systems to identify potential adversarial attack vectors.

• Security controls for AI supply chains to ensure provenance and integrity.

• Monitoring of AI model drift and adversarial robustness to mitigate security degradation over time.

2.2 AI-Specific Security Risks

Traditional application security controls fail to account for AI-specific threats, necessitating a re-evaluation of existing methodologies. Key risks include:

• Adversarial ML Attacks: Attackers can introduce imperceptible perturbations to input data, leading to misclassification or model manipulation.

• Model Inference Attacks: Model inversion techniques can reconstruct training data, exposing sensitive information.

• Supply Chain Risks in AI Models: Organizations often integrate pre-trained models from third-party sources without visibility into their security posture, introducing risks analogous to software supply chain vulnerabilities.

• Prompt Injection Attacks: In large language models (LLMs), untrusted input manipulation can override system constraints, leading to unauthorized behaviors.

ISO 42001, by defining AI governance and risk management requirements, provides an opportunity to systematically address these security concerns within an enterprise framework.

3. Security Implications of ISO 42001 for Application Security

3.1 AI-Specific Threat Modeling

ISO 42001 requires organizations to conduct AI-specific threat modeling. Existing frameworks such as STRIDE and PASTA must be extended to include:

• Data Integrity Risks (e.g., training data poisoning).

• Model Integrity Risks (e.g., adversarial example attacks).

• Inference Leakage Risks (e.g., model extraction and membership inference).

3.2 AI Supply Chain Security

AI model supply chains introduce dependencies that require security validation:

• Model Provenance Verification (e.g., cryptographic signatures for AI models).

• Dataset Integrity Checks (e.g., adversarial data filtering techniques).

• Dependency Management for AI Pipelines (e.g., SBOM for AI models).

3.3 AI Runtime Security and Continuous Monitoring

ISO 42001’s risk management principles necessitate:

• Real-Time Monitoring for Adversarial Attacks (e.g., automated detection of adversarial inputs).

• Anomaly Detection in AI Decision-Making (e.g., memory and execution trace analysis).

• Policy Enforcement for AI-Generated Outputs (e.g., LLM safety filters).

4. ISO 42001 Audit Test Plan: Evaluating AI Security Posture

This audit test plan provides a structured approach for evaluating an organization’s adherence to ISO 42001 AI security requirements, with a focus on governance and technical controls.

4.1 Organizational AI Governance Assessment

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5. Conclusion

ISO 42001 introduces a structured governance framework for AI systems, yet its implications for application security remain underexplored. This paper has demonstrated that ISO 42001 necessitates a re-evaluation of traditional AppSec methodologies to address AI-specific risks, including adversarial manipulation, supply chain security, and runtime model integrity.

By systematically integrating AI risk assessment methodologies into the SSDLC, organizations can align with ISO 42001 while enhancing security resilience. The audit test plan presented here serves as a practical framework for evaluating compliance and security posture in AI-driven environments.