AICM AtlasCSA AI Controls Matrix
AIS · Application & Interface Security
AIS-07Cloud & AI Related

Application Vulnerability Remediation

Specification

Define and implement a process to remediate application security vulnerabilities, automating remediation when possible.

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

Resource provisioning, Team and expertise

Development

Guardrails, Supply Chain, Design

Evaluation

Validation/Red Teaming

Deployment

AI applications, AI Services supply chain, Orchestration

Delivery

Continuous improvement, Maintenance

Retirement

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

[Applicable to all providers (CSP, MP, OSP, AP) excluding AIC unless otherwise specified]
1. Define Remediation Process Scope: Establish provider-specific processes for identifying, prioritizing, and remediating application security vulnerabilities as outlined in the provider-specific sections below. Ensure processes cover the entire vulnerability lifecycle, including detection, assessment, mitigation, and validation, across applications and interfaces.

2. Remediation Governance Structure: Establish clear roles and responsibilities for managing the remediation process, involving cross-functional teams (e.g., security, development, operations) and oversight by governance bodies. Define escalation workflows involving security leads, senior management, and compliance teams to prioritize and address critical vulnerabilities effectively.

3. Remediation Documentation Standards: Define structured documentation standards for the remediation process, including vulnerability reports, risk assessments, remediation plans, and validation records. Use consistent templates to document vulnerability details, impact analysis, mitigation steps, and post-remediation verification to ensure traceability and auditability.

4. Remediation Management Framework: Implement a structured process for managing remediation activities. Conduct regular vulnerability assessments and prioritize remediation based on risk severity (e.g., CVSS scores, exploitability). Review and update remediation processes at least annually or following significant changes (e.g., new vulnerabilities, system updates, or regulatory requirements). Ensure alignment with relevant standards (e.g., OWASP Top 10, NIST 800-53, ISO 27001) and AI-specific frameworks like NIST AI Risk Management Framework and OWASP LLM Top 10. Automate vulnerability tracking and reporting where possible using security tools.

5. Communication and Training Standards: Define requirements for communicating remediation processes, including formal distribution of vulnerability reports, mandatory training for developers and security teams on secure coding and remediation techniques, and awareness programs for stakeholders. Ensure remediation policies and tools are accessible via internal portals and that teams are trained on vulnerability management and compliance requirements.

6. Quality Control and Validation Standards: Define policies for quality assurance of remediation efforts, including requirements for re-testing mitigated vulnerabilities, conducting root cause analysis, and validating fixes through static/dynamic analysis or penetration testing. Establish automated monitoring and alerting to detect reintroduced vulnerabilities and ensure ongoing compliance with security standards.

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

ISO 42001Partial Gap
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.

EU AI ActNo Gap
Article 15 (4)
Article 15 (5)
Article 53
Article 55 (1)
Addendum

N/A

NIST AI 600-1No Gap
MG-2.4-003
MS-2.7-001
Addendum

N/A

BSI AIC4No Gap
PSS-02
Addendum

N/A

AI-CAIQ questions (1)

AIS-07.1

Are processes defined and implemented to remediate application security vulnerabilities, automating remediation when possible?