A&A-02Cloud & AI Related

Independent Assessments

Specification

Conduct independent audit and assurance assessments according to relevant standards at least annually.

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

Data collection, Data curation, Data storage

Development

Design, Training, Guardrails

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

AI Services supply chain, AI applications, Orchestration

Delivery

Continuous monitoring, Continuous improvement, Operations, Maintenance

Retirement

Data deletion, Model disposal, Archiving

Ownership / SSRM

Shared across the supply chain

Shared control ownership refers to responsibilities and activities related to LLM security that are distributed across multiple stakeholders within the AI supply chain, including the Cloud Service Provider (CSP), Model Provider (MP), Orchestrated Service Provider (OSP), Application Provider (AP), and Customer (AIC). These controls require coordinated actions, communication, and governance across all involved parties to ensure their effectiveness.

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

Independent assessments are essential to ensure AI systems are credible, reliable, and compliant. Annual assessments identify vulnerabilities and risks, allowing for proactive mitigation and continuous improvement.

[All Providers: MP, AP, OSP]
1. Assessment Planning and Execution: Engage qualified independent assessors with AI expertise, establish clear assessment objectives and scope, and develop comprehensive testing methodologies including adversarial testing where applicable. Ensure assessors maintain independence from the teams responsible for system development and operations.

2. Assessment Standards Alignment: Incorporate relevant standards and frameworks including CSA AI Security Framework, ISO/IEC 42001 for AI Management Systems, ISO/IEC 27001 for information security, and industry-specific requirements. Ensure alignment with organizational policies and regulatory requirements.

3. Documentation and Reporting: Maintain detailed assessment records, document methodology and findings, create clear actionable reports, and establish evidence preservation procedures. Share findings with relevant stakeholders including senior management and compliance teams.

4. Assessment Frequency and Updates: Conduct assessments at least annually and after significant changes. Update assessment criteria based on evolving threats, technologies, and standards. Maintain a schedule of planned assessments.

5. Quality Assurance: Verify assessor qualifications and independence, validate assessment methodologies, ensure comprehensive coverage of critical areas, and review assessment quality and effectiveness.

6. Relevant Assessment Scope: Ensure independent assessments appropriately cover controls relevant to their role in the AI supply chain, focusing on the specific components, processes, and data under their responsibility as defined in their scope of services and products.

Auditing guidelines

1. Examine the process to determine standards and regulations applicable to the organization’s systems and environments.

2. Determine if the organization maintains and reviews a list of such standards and regulations.

3. Determine if senior management exercises oversight over the independence of the assessment process.

4. Determine if the audit plan is informed by previous assessments and if it is scheduled on an annual basis.

Standards mappings

ISO 42001No Gap

42001: 9.2.1 General  -  Internal audit
42001: 9.2.2 Internal audit program
27001: A.5.35 Independent review of information security
27001: A.5.36 Compliance with policies
rules and standards for information security
27002: 5.35 Independent review of information security
27002: 5.36 Compliance with policies
rules and standards for information security

Addendum

N/A

EU AI ActPartial Gap

Article 21 (1)
Article 43 (2)
Article 43 (3)
Article 93 (1)

Addendum

Require annual audits, independent post-market monitoring, reference to recognized audit or assurance standards.

NIST AI 600-1No Gap

MP-5.1-005
GV-3.2-001

Addendum

N/A

BSI AIC4No Gap

COM-03
OIS-01 Additional Criteria

Addendum

N/A

AI-CAIQ questions (1)

A&A-02.1

Are independent audit and assurance assessments conducted according to relevant standards at least annually?