Vulnerability Management Reporting
Specification
Define and implement a process for tracking and reporting vulnerability identification and remediation activities that includes stakeholder notification.
Threat coverage
Architectural relevance
Lifecycle
Data storage, Resource provisioning
Training
Evaluation, Validation/Red Teaming, Re-evaluation
Orchestration, AI Services supply chain, AI applications
Operations, Maintenance, Continuous monitoring, Continuous improvement
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
Auditing guidelines
1. Verify that the Cloud Service Provider (CSP) has defined a process to systematically document both the vulnerabilities identified within the security perimeter and the activities implemented to remediate them. Ensure that the process is documented in detail, covering scope, objectives, roles and responsibilities. 2. Examine the above-mentioned process to verify that it includes a notification phase to relevant stakeholders. 3. Confirm that the above-mentioned process is communicated and thoroughly comprehended by relevant parties. 4. Confirm that the above-mentioned process is concretely and appropriately implemented by responsible parties. 5. Inspect whether the above-mentioned process is monitored against sets of efficacy and efficiency metrics / indicators. 6. Inspect whether the above-mentioned process is periodically reviewed and updated by responsible parties.
Standards mappings
42001: A.6.2.6 AI system operation and monitoring 42001: A.8.4 Reporting and stakeholder notification 42001: A.6.1 / A.6.3.2 Risk and control planning 27001: 5.26 Response to information sec. inc 27001: 8.8 Management of technical vulnerabilities 27002: 8.8 Technical vulnerability management 27002: 5.7: Threat intelligence
Addendum
The organization should define and implement a process for identifying, tracking, and remediating vulnerabilities within AI systems, components, and dependencies. This process should include: Logging of vulnerabilities and their statuses, Documentation of remediation actions, Periodic reporting to relevant stakeholders, Alignment with the organization’s vulnerability and incident management procedures.
Article 72 (2) (d) Article 73
Addendum
N/A
GV-2.1-001 GV-4.3-002 MG-2.4-003 MS-2.7-001
Addendum
NIST AI 600-1 does not specifically reference "tracking and reporting vulnerabilities."
C4 SR-02 C4 SR-03 C5 COM-04
Addendum
N/A
AI-CAIQ questions (1)
Are processes defined and implemented for tracking and reporting vulnerability identification and remediation activities that include stakeholder notification?