Application Vulnerability Remediation
Specification
Define and implement a process to remediate application security vulnerabilities, automating remediation when possible.
Threat coverage
Architectural relevance
Lifecycle
Resource provisioning, Team and expertise
Guardrails, Supply Chain, Design
Validation/Red Teaming
AI applications, AI Services supply chain, Orchestration
Continuous improvement, Maintenance
Not applicable
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 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.
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. Review Vulnerability Scanning for Hosted Services: Confirm use of CSP-native scanners and third-party testing. 2. Validate Customer Notification and Advisory Process as customers need timely info about vulnerabilities in shared services. Check how CSPs disseminate CVEs and issue advisory reports or mitigation scripts. 3. Assess Remediation Timeframes and Automation: Examine auto-remediation engines (e.g., AWS Inspector + Systems Manager) and patch windows. 4. Inspect Secure Image and Runtime Lifecycle: Audit image repositories, attestations, and runtime hardening timelines. 5. Confirm Enforcement of Security Baselines: Test Policy-as-Code rules that detect and fix known configuration vulnerabilities. From CCM: 1. Examine the policy and procedures to remediate application security vulnerabilities and automating remediation. 2. Evaluate whether roles and responsibilities, including escalation paths for application security incident response and remediation, are defined and effective. 3. Determine if the organization leverages automation when possible and if this automation increases remediation efficiency.
Standards mappings
42001: B.6.2.4 42001: A.6.2.6 42001: A.8.4 27001: A.8.8
Addendum
A dedicated AI-app vulnerability management control.
Article 15 (4) Article 15 (5) Article 53 Article 55 (1)
Addendum
N/A
MG-2.4-003 MS-2.7-001
Addendum
N/A
PSS-02
Addendum
N/A
AI-CAIQ questions (1)
Are processes defined and implemented to remediate application security vulnerabilities, automating remediation when possible?