AICM AtlasCSA AI Controls Matrix
LOG · Logging and Monitoring
LOG-10Cloud & AI Related

Encryption Monitoring and Reporting

Specification

Establish and maintain a monitoring and internal reporting capability over the operations of cryptographic, encryption and key management policies, processes, procedures, and controls.

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

Development

Training

Evaluation

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 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. AI Data Encryption Compliance: Collaborate with relevant stakeholders to ensure that AI datasets (training data, inference inputs, outputs) are encrypted in transit and at rest.

2. Policy Alignment and Key Ownership: Document cryptographic policies dictating AI data handling, including data classification, key ownership, and approved encryption methods. Specify reporting channels for incidents or deviations from established cryptographic standards.

3. Access Control and Key Management: Enforce strict role-based permissions to limit access to cryptographic keys for AI workloads (e.g., training, testing, production). Integrate automated key lifecycle management (generation, rotation, retirement) aligned with AI model stages, logging all key-related events.

4. Monitoring and Reporting: Log and audit all cryptographic operations associated with AI data (encryption/decryption events, certificate usage, key access). Implement real-time dashboards or alerts to visualize encryption status and flag anomalies (e.g., unauthorized key usage, suspicious access patterns).

5. Model Integrity and Output Verification: Incorporate checks during AI inference to confirm model outputs have not been tampered with. Automate alerts that notify security teams when cryptographic inconsistencies or anomalies are detected in AI pipelines.

6. Secure Logging and Retention: Store cryptographic logs (key usage, encryption events) in encrypted form with defined retention policies suitable for AI data and model parameters. Use role-based access to restrict who can view or modify these logs, preventing unauthorized disclosure or tampering.

Auditing guidelines

1. Inquiring with Control Owners

1.1 Conduct interviews with personnel responsible for establishing and maintaining monitoring and internal reporting capabilities over cryptographic, encryption, and key management operations for cloud infrastructure and AI processing resources to understand their oversight processes for customer data protection and infrastructure security. Verify their understanding of monitoring controls for encryption of customer workloads, internal reporting mechanisms for infrastructure cryptographic operations, and procedures for maintaining ongoing oversight of key management for customer isolation and service security.

2. Inspecting Records and Documents

2.1 Confirm monitoring mechanisms are in place to detect encryption failures or unauthorized decryption attempts for cloud infrastructure data, customer workloads, and inter-service communications.

2.2 Verify reports are generated on the use of encryption in cloud infrastructure data transmission, customer data storage, and compute resource protection.

2.3 Review documentation on how cryptographic keys are handled, rotated, and monitored for cloud infrastructure security, customer data protection, and tenant isolation.

2.4 Validate that cloud infrastructure teams receive alerts for deviations in encryption policy adherence affecting customer security and infrastructure integrity.

2.5 Check integration with central SIEM tools for real-time visibility into cloud infrastructure cryptographic operations and customer protection events.

2.6 Ensure audit logs capture cloud infrastructure encryption-related events like certificate expiration, invalid key use, or customer data encryption failures.

2.7 Confirm documentation of encryption algorithms and configurations in use for cloud infrastructure operations, customer data protection, and hardware security.

2.8 Examine internal reporting processes for communicating cloud infrastructure cryptographic and key management findings to infrastructure operations and customer security teams.

2.9 Review periodic assessment and reporting schedules for cloud infrastructure cryptographic policy compliance and customer protection effectiveness.

2.10 Validate that monitoring and reporting capabilities cover all aspects of cloud infrastructure cryptographic operations including customer isolation, infrastructure security, and regulatory compliance.

2.11 Confirm centralized monitoring of encryption operations across all cloud services.

2.12 Validate reporting systems track usage of KMS, HSMs, and customer-managed keys.

2.13 Review incident handling procedures for failed or suspicious cryptographic operations.

2.14 Verify customer access to audit logs involving key lifecycle events.

2.15 Ensure tools are in place to alert on anomalies in key access patterns.

2.16 Confirm monitoring of compliance with configured encryption policies across multi-tenant platforms.

2.17 Check evidence of periodic reporting to external auditors or customers.

Standards mappings

ISO 42001Partial Gap
No Mapping for ISO 42001
ISO 27001 8.24
ISO 27001 9.1
ISO 27001 9.2
Addendum

No ISO 42001 control maps to LOG-10 topic.

EU AI ActFull Gap
No Mapping
Addendum

Monitoring of the use of encryption keys and/or cryptographic operations.

NIST AI 600-1Full Gap
MG-3.2-002
Addendum

Need to provide guidance that covers this AICM control, "Establish and maintain a monitoring and internal reporting capability over the operations of cryptographic, encryption and key management policies, processes, procedures, and controls."

BSI AIC4No Gap
C4 PC-02
C5 CRY-01
Addendum

N/A

AI-CAIQ questions (1)

LOG-10.1

Are monitoring and internal reporting capabilities established to report on cryptographic operations, encryption, and key management policies, processes, procedures, and controls?