A&A-06Cloud & AI Related

Remediation

Specification

Establish, document, approve, communicate, apply, evaluate and maintain a risk-based corrective action plan to remediate audit findings, regularly review and report remediation status to relevant stakeholders.

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

Design, Training, Guardrails

Evaluation

Re-evaluation, Validation/Red Teaming, Evaluation

Deployment

AI applications, AI Services supply chain

Delivery

Continuous improvement

Retirement

Model disposal, Data deletion, Archiving

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 providers should maintain a Risk Management program to identify, mitigate, and manage risks specific to their systems. Management should establish processes to identify risks within their area of responsibility and implement appropriate measures to address them. Audit findings should receive appropriate attention at all organizational levels. When control deficiencies are identified, they should be evaluated for prioritization based on potential impact. Once prioritized, appropriate risk treatment options should be selected and implemented.

[AI Providers (MP, OSP, AP)]
1.Remediation Framework: Establish a documented framework outlining the process for creating, implementing, and monitoring corrective action plans specific to AI systems. Base the framework on a risk-based approach. Ensure the framework aligns with organizational policies, regulatory requirements, and industry standards

2. Communication and Coordination: Communicate remediation plans, including priorities and deadlines, to all responsible teams across AI development, operations, and security. Use collaborative platforms to coordinate remediation activities that may span across model development, orchestration, and application layers.

3. Roles and Responsibilities: Designate specific individuals or teams responsible for addressing each audit finding related to AI systems. Ensure clear ownership for remediating issues spanning the entire AI lifecycle handled by the provider.

4. Risk-Based Prioritization: Prioritize findings based on risk assessment that considers both traditional security factors and AI-specific concerns such as model robustness, output safety, and potential for misuse. Implement a tiered approach that addresses critical AI safety and security issues first. Establish escalation protocols for high-risk findings or delays in remediation of critical AI safety issues to ensure timely resolution.

5. Verification and Validation: Conduct follow-up assessments to confirm that remediation measures effectively address identified risks. For AI systems, this may include model testing, red-teaming exercises, or adversarial testing to validate that vulnerabilities have been properly mitigated.

6. Documentation and Reporting: Document remediation outcomes, including evidence of risk mitigation such as before/after security testing results, model performance metrics, and compliance improvements. Maintain comprehensive records linking audit findings to specific remediation actions and verification results. Provide regular status updates to relevant stakeholders.

7. Continuous Improvement: Regularly review and update corrective action processes to incorporate lessons learned from previous remediations and emerging AI risks. Adjust and implement policy and controls based on incident data, evolving threats, changing regulatory landscape for AI, and advances in AI safety research.

8. Metrics and Measurement: Include metrics and KPIs specific to AI systems that demonstrate risk reduction and compliance improvement. Track metrics such as successful model security test rates, reduction in false positives/negatives, or improvements in model robustness against adversarial inputs.

Auditing guidelines

1. Examine if the outputs of audits are defined by the policy.

2. Determine if the audit findings are reviewed and if appropriate reports are made available to users and senior management.

3. Determine if the identification of risks from audit findings, or changes to them, are made available to users.

4. Determine if corrective actions proposed are planned to align with the organization's risk profile.

5. Determine if a process exists to track changes in risk rating and is used to update risk registers, particularly with regard to residual risk.

6. Examine a sample of proposed corrective actions and determine if they were followed-up in a manner consistent with the organization's policy.

7. Examine audit programs to determine if they are subject to continuous improvement through feedback, review, and revisions.

8. Examine if a process exists to review the audit program in light of current and past audits.

Standards mappings

ISO 42001No Gap

42001: 9.2.1 General
42001: 9.2.2 Internal audit programme
42001: 9.3.2 Management Review Inputs
42001: 9.3.3 Management Review Results
42001: 10.2: Non-conformity and Corrective action
27001: 9.2.2 Internal audit programme
27001: 10.2 Nonconformity and corrective action

Addendum

N/A

EU AI ActNo Gap

Article 20 (1)
Article 20 (2)
Article 53
Article 55

Addendum

N/A

NIST AI 600-1Partial Gap

GV-1.3-007
MG-4.2-002
MG-1.3-001

Addendum

Extend the NIST AI 600-1 Governance function to include the requirements that corrective action plans be created based on audit findings and that stakeholders are notified of the status.

BSI AIC4No Gap

COM-03
COM-04

Addendum

N/A

AI-CAIQ questions (1)

A&A-06.1

Is a risk-based corrective action plan established, documented, approved, communicated, applied, evaluated, and maintained to remediate audit findings, regularly review, and report remediation status to relevant stakeholders?