A&A-05Cloud & AI Related

Audit Management Process

Specification

Define and implement an Audit Management process aligned with global auditing standards, to support audit planning, risk analysis, security control assessment, conclusion, remediation schedules, report generation, and review of past reports and supporting evidence.

Threat coverage

Model manipulation

Data poisoning

Sensitive data disclosure

Model theft

Model/Service Failure

Insecure supply chain

Insecure apps/plugins

Denial of Service

Loss of governance

Architectural relevance

Physical infrastructure

Network

Compute

Storage

Application

Data

Lifecycle

Preparation

Not applicable

Development

Guardrails

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

AI Services supply chain, AI applications

Delivery

Continuous improvement, Operations, Maintenance, Continuous monitoring

Retirement

Archiving, Data deletion, Model disposal

Ownership / SSRM

Shared Cloud Service Provider-Model Provider (Shared CSP-MP)

The CSP and MP are jointly responsible and accountable for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer.

Model

Owned by the Model Provider (MP)

The model provider (MP) designs, develops, and implements the control as part of their services or products to mitigate security, privacy, or compliance risks associated with the Large Language Model (LLM). Model Providers are entities that develop, train, and distribute foundational and fine-tuned AI models for various applications. They create the underlying AI capabilities that other actors build upon. Model Providers are responsible for model architecture, training methodologies, performance characteristics, and documentation of capabilities and limitations. They operate at the foundation layer of the AI stack and may provide direct API access to their models. Examples: OpenAI (GPT, DALL-E, Whisper), Anthropic(Claude), Google(Gemini), Meta(Llama), as well as any customized model.

Orchestrated

Owned by the Orchestrated Service Provider (OSP)

The Orchestrated Service Provider (OSP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The OSP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the OSP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The OSP is accountable for ensuring that its providers upstream (e.g MPs) implement the control as it relates to the service/product the develop and offered by the OSP. This refers to entities that create the technical building blocks and management tools that enable AI implementation. This can include platforms, frameworks, and tools that facilitate the integration, deployment, and management of AI models within enterprise workflows. These providers focus on model orchestration and offer services like API access, automated scaling, prompt management, workflow automation, monitoring, and governance rather than end-user functionality or raw infrastructure. They help businesses implement AI in a structured and efficient manner. Examples: AWS, Azure, GCP, OpenAI, Anthropic, LangChain (for AI workflow orchestration), Anyscale (Ray for distributed AI workloads), Databricks (MLflow), IBM Watson Orchestrate, and developer platforms like Google AI Studio.

Application

Owned by the Application Provider (AP)

The Application Provider (AP) is responsible for the design, development, implementation, and enforcement of the control to mitigate security, privacy, or compliance risks associated with Large Language Model (LLM)/GenAI technologies in the context of the services or products they develop and offer. The AP is responsible and accountable for the implementation of the control within its own infrastructure/environment. If the control has downstream implications on the users/customers, the AP is responsible for enabling the customer and/or upstream partner in the implementation/configuration of the control within their risk management approach. The AP is accountable for carrying out the due diligence on its upstream providers (e.g MPs, Orchestrated Services) to verify that they implement the control as it relates to the service/product develop and offered by the AP. These providers build and offer end-user applications that leverage generative AI models for specific tasks such as content creation, chatbots, code generation, and enterprise automation. These applications are often delivered as software-as-a-service (SaaS) solutions. These providers focus on user interfaces, application logic, domain-specific functionality, and overall user experience rather than underlying model development. Example: OpenAI (GPTs,Assistants), Zapier, CustomGPT, Microsoft Copilot (integrated into Office products), Jasper (AI-driven content generation), Notion AI (AI-enhanced productivity tools), Adobe Firefly (AI-generated media), and AI-powered customer service solutions like Amazon Rufus, as well as any organization that develops its AI-based application internally.

Implementation guidelines

AI service providers should implement an audit plan which covers the frequency and schedule of the audit. It should have both internal and external audit plans.Each provider must audit their systems according to their specific scope of responsibility

[AI Providers: MP, OSP, AP]
1. Audit Planning: Define objectives and scope of audits to include AI-specific components such as model development processes, training data quality, inference systems, and governance frameworks. Create a comprehensive audit calendar that specifies frequency based on risk assessment, responsible teams with AI expertise, and key milestones for each audit cycle.

2. Evidence Collection: Document evidence including model documentation, training datasets, inference logs, security configurations, and output validation tests. Ensure all evidence is securely stored with proper access controls and maintains traceability between model versions, training data, and deployed systems.

3. Reporting: Generate comprehensive reports summarizing findings, risk levels specific to AICM domains and compliance gaps with actionable recommendations. Use standardized templates that address unique AI concerns including bias, safety, and model robustness.

4. Remediation: Develop prioritized remediation schedules with clear deadlines and responsibilities assigned to relevant teams. Establish specific criteria for remediation of AI risks and findings across all AICM domains.

5. Stakeholder Communication: Share results with relevant stakeholders including senior management, AI governance boards, and compliance teams. Incorporate domain expert feedback to refine both technical and governance aspects of AI systems.

6. Continuous Improvement: Analyze past reports to identify recurring issues, emerging AI-specific risks, and performance patterns across multiple audit cycles. Use insights to drive improvements in model development processes, security controls, and governance frameworks.

Auditing guidelines

1. Examine policy related to the establishment and conduct of audits.

2. Determine if audit programs are established and aligned to the requirements of the organization, including the audit charter.

3. Determine if the organization upholds the independence of the audit program.

4. Determine if the conduct of audits is defined, approved at the appropriate level, and reviewed for effectiveness.

Standards mappings

ISO 42001No Gap

42001: 9.2.1 General
42001: 9.2.2 Internal Audit Programme
42001: 10.1 Non-Conformity and Corrective Action
42001: 10.2 Continual Improvement
27001: 9.2.1 General
27001: 9.2.2 Internal audit programme

Addendum

N/A

EU AI ActPartial Gap

Article 17 (1)
Annex VI

Addendum

Mandate that providers and deployers of high-risk AI systems establish a documented audit management process aligned with recognized standards. Require organizations to develop an annual (or more frequent) audit plan, aligned to risk priorities and operational needs. Require that audits are planned based on risk analysis outcomes, ensuring higher scrutiny where risk exposure is greatest. Require that the audit process includes evaluation of both security controls and compliance measures, covering both operational and technical aspects. Mandate that findings are addressed with corrective actions and follow-up audits or checks are performed to ensure remediation is effective. Require formal, documented audit reports and management reviews of past reports and evidence to ensure continual improvement. Clarify that the audit management process is part of the Quality Management System (Article 17) and that evidence is recorded in the technical documentation.

NIST AI 600-1Partial Gap

GV-4.3-002
MP-1.1-003

Addendum

The AICM requirement discusses an audit management process, while the two controls from NIST AI 600-1 focus specifically on incident reporting and risk measurement.

BSI AIC4No Gap

COM-02
COM-03

Addendum

N/A

AI-CAIQ questions (1)

A&A-05.1

Are Audit Management processes aligned with global auditing standards, defined and implemented to support audit planning, risk analysis, security control assessment, conclusion, remediation schedules, report generation, review of past reports and supporting evidence?