Network Defense
Specification
Define, implement and evaluate processes, procedures and defense-in-depth techniques for protection, detection, and timely response to network-based attacks.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Training
Validation/Red Teaming
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
Data deletion
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
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. Verify the Cloud Service Provider (CSP) documented procedures clearly define network defense mechanisms. 2. Confirm regular implementation and evaluation of defense strategies (e.g., Zero Trust, honey pots, Microsoft Sentinel, AWS GuardDuty). 3. Check routine testing of defense mechanisms for effectiveness against current threats. 4. Ensure monitoring and logging effectively capture events relevant to network defense. 5. Validate timely response and mitigation processes for detected threats. 6. Confirm clear accountability and documented roles for network defense management. 7. Verify regular training sessions on network defense practices provided to security teams.
Standards mappings
No Mapping for ISO 42001 ISO/IEC 27001:2022 - A.8.16 A.8.20
Addendum
Contrarily to the AICM control that is characterized by an increased level of specificity and technicality, the ISO/IEC 42001 framework establishes more general requirements that may be interpreted as encompassing the aspects tackled in the control (e.g., no specific mention to the definition, implementation and assessment of processes, procedures and techniques of advanced network defense, no mention to network-based attacks, but inclusion of provisions to secure, manage and control networks and network devices).
Article 15
Addendum
Full control would have to be added because the EU AI Act does not address these concerns. Add, "Define, implement, and evaluate processes, procedures, and defense-in-depth techniques for protection, detection, and timely response to network-based attacks."
MP-2.3-005 MP-2.2-002
Addendum
The AICM control addresses security controls sufficient to protect, detect, and respond to network attacks, while NIST AI 600-1 controls are concerned with testing to prevent data manipulation and misuse.
C4 SR-06 C4 SR-07 C5 COS-01
Addendum
N/A
AI-CAIQ questions (1)
Are processes, procedures, and defense-in-depth techniques for the protection, detection, and timely response to network-based attacks, defined, implemented and evaluated?