AICM AtlasCSA AI Controls Matrix
LOG · Logging and Monitoring
LOG-04Cloud & AI Related

Audit Logs Access and Accountability

Specification

Restrict access to audit logs and maintain records of access to logs.

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 storage, Team and expertise

Development

Guardrails

Evaluation

Re-evaluation, Validation/Red Teaming

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring

Retirement

Archiving, Data deletion

Ownership / SSRM

PI

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

Shared Model Provider-Orchestrated Service Provider (Shared MP-OSP)

The MP and OSP 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.

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

[All Actors]
1. Apply least-privilege access control, e.g., role- or attribute-based (RBAC/ABAC) to every audit-log store, service and API.

2. Require unique user identities and strong authentication (e.g., MFA) for all log-access requests.

3. Record every read, write, delete or configuration event on audit logs, capturing user ID, source, timestamp and action.

4. Store logs in tamper-evident or immutable locations and replicate for durability.

5. Monitor access patterns and generate real-time alerts for unauthorised attempts, privilege escalation or anomalous activity.

6. Review and reconcile log-access permissions regularly, on a risk-based schedule and revoke any unnecessary rights.

7. Retain log-access records for the policy-defined period and make them available for audit, forensics and compliance reporting.

Auditing guidelines

1. Inquiring with Control Owners

1.1 Conduct interviews with personnel responsible for managing cloud infrastructure audit log access controls and maintaining access records to understand their authorization processes for accessing infrastructure logs, customer workload logs, and system security events. Verify their understanding of access restriction mechanisms and record-keeping requirements for all cloud infrastructure audit log access activities.

2. Inspecting Records and Documents

2.1 Verify access to cloud infrastructure-generated audit logs (including compute resource usage, customer workload events, infrastructure security incidents, and system operations) is restricted to authorized personnel.

2.2 Ensure cloud infrastructure logging access is role-based and mapped to least privilege principles, maintaining customer workload isolation and data protection.

2.3 Confirm all cloud infrastructure log access events are themselves logged with timestamps, actor IDs, and specific customer environment data accessed.

2.4 Check for formal review processes of cloud infrastructure log access permissions, including customer data isolation requirements.

2.5 Validate cloud operations and infrastructure teams are not granted persistent access to customer-specific logs without approval and operational necessity.

2.6 Review incident records for unauthorized access to cloud infrastructure audit logs and follow-up actions taken.

2.7 Confirm procedures are in place to revoke cloud infrastructure log access upon role changes or terminations.

2.8 Examine documented access control policies and procedures for cloud infrastructure audit log systems, including customer tenant protections.

2.9 Validate that cloud infrastructure access records are retained according to customer SLAs and compliance requirements.

2.10 Review monitoring and alerting mechanisms for unauthorized or suspicious cloud infrastructure audit log access attempts.

2.11 Ensure CSP-native audit logs (e.g., CloudTrail, Stackdriver, Activity Logs) are accessible only to authorized roles.

2.12 Confirm administrative access to audit logs is gated through MFA and approval workflows.

2.13 Validate log access events are included in centralized security monitoring dashboards.

2.14 Review segregation of duties policies to prevent log tampering or unauthorized deletions.

2.15 Check that tenant-specific log access is isolated and auditable per customer.

2.16 Verify CSP performs and documents periodic access reviews of logging infrastructure.

Standards mappings

ISO 42001Partial Gap
No Mapping for ISO 42001
ISO 27001 A.5.33
Addendum

No ISO 42001 control maps to LOG-04 topic.

EU AI ActPartial Gap
Article 17 (1)
Addendum

Contrarily to the AICM control that provides an increased level of detail, the EU AI Act establishes more general security principles and requirements that encompass the aspects tackled in the control.

NIST AI 600-1Full Gap
GV-3.2-003
Addendum

Restricting privileged access to logs (access management).

BSI AIC4No Gap
C4 DM-02
C5 OPS-12
Addendum

N/A

AI-CAIQ questions (1)

LOG-04.1

Is access to audit logs restricted and are the records of access logs maintained?