AICM AtlasCSA AI Controls Matrix
LOG · Logging and Monitoring
LOG-06Cloud & AI Related

Clock Synchronization

Specification

Use a reliable time source across all relevant information processing systems.

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, Resource provisioning

Development

Design, Training

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Archiving, Data deletion, Model disposal

Ownership / SSRM

PI

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

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

Shared Orchestrated Service Provider-Application Provider (Shared OSP-AP)

The OSP and AP 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.

Implementation guidelines

[All Actors]
1. Synchronise every system, service, container and device with a trusted, authenticated time source (e.g., NTP, the cloud-provider time service, etc.).

2. Configure at least one fallback time source and generate alerts when drift or sync failure exceeds the defined threshold.

3. Continuously monitor time-sync health; log and raise incidents for repeated drift, spoofing attempts or source unreachability.

4. Record and review time-sync settings in configuration management; verify them at least annually or after architecture changes.

5. Enforce consistent timestamp zones and formats across all logs to support correlation, forensics and auditability.

Auditing guidelines

1. Inquiring with Control Owners

1.1 Conduct interviews with personnel responsible for managing time synchronization across cloud infrastructure and AI processing resources to understand their implementation of reliable time sources for compute resource management, customer workload tracking, and infrastructure monitoring. Verify their understanding of time synchronization requirements for multi-tenant environments, GPU/TPU clusters, and procedures for maintaining accurate timestamps across distributed cloud infrastructure.

2. Inspecting Records and Documents

2.1 Confirm cloud infrastructure systems handling compute resources and customer workloads use a centralized time source.

2.2 Verify implementation of Network Time Protocol (NTP) or equivalent time synchronization protocols across cloud infrastructure, hypervisors, and AI processing clusters.

2.3 Check synchronization logs to validate accurate timestamping across compute resource allocation, customer workload execution, and infrastructure monitoring activities.

2.4 Assess whether unsynchronized cloud infrastructure systems trigger alerts or errors that could affect customer workload scheduling or resource billing accuracy.

2.5 Verify clock drift thresholds are defined and monitored for cloud infrastructure components, GPU/TPU clusters, and customer tenant environments.

2.6 Confirm the accuracy of timestamps in cloud infrastructure logs critical for customer billing, resource utilization tracking, and security investigations.

2.7 Validate incident response records for infrastructure issues reference consistent timestamps across customer environments and infrastructure components.

2.8 Examine documentation of reliable time source configuration for cloud infrastructure and backup time synchronization mechanisms across data centers.

2.9 Review time synchronization policies covering cloud infrastructure, customer tenant isolation, and AI processing resource management systems.

2.10 Validate that time source reliability is monitored for cloud infrastructure and backup time sources are available to maintain service availability across multiple regions.

2.11 Confirm use of centralized, authenticated NTP servers across the cloud infrastructure.

2.12 Verify tenant isolation does not interfere with consistent time sync in multi-tenant setups.

2.13 Ensure that logs generated from different services share a common time reference.

2.14 Validate redundancy and fault-tolerance in time source configurations.

2.15 Check monitoring systems for alerting when time synchronization fails.

2.16 Review logs of past sync failures and documented remediation steps.

2.17 Confirm compliance with regulatory standards requiring timestamp precision.

Standards mappings

ISO 42001Partial Gap
No Mapping for ISO 42001
ISO 27001 A.8.17
Addendum

No ISO 42001 control maps to LOG-06 topic.

EU AI ActFull Gap
No Mapping
Addendum

No explicit mention to the use of a reliable time source across information processing systems is made in the European regulation, nor does it set broader requirements that may be interpreted as encompassing the one defined by the control.

NIST AI 600-1Full Gap
No Mapping
Addendum

Using a reliable time source.

BSI AIC4Partial Gap
C4 PC-02
C5 OPS-10
Addendum

No C4 control speaks to LOG-06 topic of time source across infrastructure.

AI-CAIQ questions (1)

LOG-06.1

Is a reliable time source being used across all relevant information processing systems?