Security Monitoring and Alerting
Specification
Identify and monitor security-related events within applications, the underlying infrastructure, supply chain, and consider logging other events based on risk evaluation. Define and implement a system to generate alerts to responsible stakeholders based on such events and corresponding metrics.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Guardrails
Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
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
Auditing guidelines
1. Inquiring with Control Owners 1.1 Conduct interviews with personnel responsible for identifying, monitoring, and alerting on security-related events within cloud infrastructure, AI processing resources, and multi-tenant compute environments to understand their event classification and risk evaluation processes for infrastructure-level AI security. Verify their understanding of alert generation systems and stakeholder notification procedures for security incidents involving compute resource compromise, data center breaches, or customer workload isolation failures. 2. Inspecting Records and Documents 2.1 Verify documented procedures exist for configuring cloud infrastructure security monitoring including hypervisor protection, GPU/TPU security, and customer workload isolation. 2.2 Confirm alerts are generated for anomalous behaviors in compute resource usage, unauthorized infrastructure access, or customer workload boundary violations. 2.3 Check for integration with centralized SIEM tools or log aggregation systems that capture infrastructure events, resource utilization patterns, and customer access activities. 2.4 Review logs to validate that high-risk infrastructure activities such as hypervisor escape attempts, unauthorized hardware access, or customer data exfiltration are appropriately flagged. 2.5 Validate roles responsible for triaging and responding to cloud infrastructure-specific security alerts including cloud operations teams, security incident response, and customer liaison teams. 2.6 Ensure monitoring includes infrastructure-specific threats such as side-channel attacks, hardware tampering, VM escape, container breakouts, and supply chain compromise of hardware components. 2.7 Verify alert thresholds are tuned for cloud infrastructure contexts to detect sophisticated infrastructure attacks while minimizing impact on customer AI workloads. 2.8 Review cloud infrastructure security monitoring including physical data center security, network infrastructure protection, and hardware security modules. 2.9 Examine cloud supply chain security event monitoring including hardware vendors, firmware updates, network equipment providers, and third-party data center services. 2.10 Validate risk evaluation frameworks used to determine additional infrastructure-specific security events requiring monitoring such as power anomalies, cooling system failures, or regulatory compliance violations. 2.11 Confirm alert notification procedures for infrastructure security incidents include appropriate stakeholders such as data center operations, customer security teams, and regulatory compliance officers. 2.12 Verify comprehensive monitoring and alerting policies are documented and enforced across cloud infrastructure. 2.13 Check native cloud security tools (e.g., AWS CloudTrail, Azure Sentinel) are configured for real-time alerts. 2.14 Ensure alerts are generated for unauthorized access attempts or privilege escalations. 2.15 Validate centralized aggregation of security logs across services and tenants. 2.16 Review responsibilities assigned for incident triage and response to cloud-native alerts. 2.17 Confirm SLAs define response times for security alerts across regions/zones. 2.18 Verify anomaly detection algorithms or ML-based alerting mechanisms are in place.
Standards mappings
ISO 42001 A.6.2.6 ISO 27001 A.8.16
Addendum
N/A
Article 15 Article 72 (1) Article 72 (2)
Addendum
N/A
MG-3.2-006 MG-4.1-002 GV-1.5-001 GV-6.2-004 MP-4.1-001
Addendum
N/A
C4 RE-01 C4 RE-02 C4 BC-04 C5 OPS-13 C5 OPS-15
Addendum
N/A
AI-CAIQ questions (2)
Are security-related events within applications, the underlying infrastructure, and the supply chain being identified and monitored, and are other events being logged based on risk evaluation?
Is a system to generate alerts, defined and implemented, to responsible stakeholders based on security-related events and corresponding metrics?