TVM-07Cloud & AI Related

Vulnerability Remediation Schedule

Specification

Define, implement and evaluate processes, procedures and technical measures based on identified risks to support scheduled and emergency responses to vulnerability identification.

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

Data collection, Data curation, Data storage, Resource provisioning

Development

Design, Training

Evaluation

Evaluation, Validation/Red Teaming, Re-evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Maintenance, Continuous monitoring, Continuous improvement

Retirement

Archiving, Data deletion, Model disposal

Ownership / SSRM

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

Shared Application Provider-AI Customer (Shared AP-AIC)

The AP and AIC both share responsibility and accountability 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 offer and consume.

Implementation guidelines

[All Actors]
1. Integrate threat intelligence feeds into the vulnerability management process to identify emerging threats and vulnerabilities across AI models, orchestrated services, applications and cloud platforms.

2. Use threat intelligence to assess the relevance of vulnerabilities, including zero-day exploits and vulnerabilities targeting specific industries.

3. Regularly update vulnerability management systems with the latest threat intelligence to keep pace with evolving risks.

4. Share threat intelligence across teams to enhance the organization’s ability to respond to new vulnerabilities quickly.

5. Monitor industry reports and security advisories to stay informed of emerging vulnerabilities and threats that may impact the organization.

Auditing guidelines

1. Verify that the Cloud Service Provider (CSP) has defined processes, procedures, and technical measures to periodically (at least monthly) detect vulnerabilities on assets managed by the organization. Ensure that the processes are documented in detail, covering scope, objectives, roles and responsibilities.

2. Examine the above-mentioned processes, procedures, and technical measures to confirm their compliance with relevant regulatory requirements and industry best practices.

3. Confirm that the above-mentioned processes, procedures, and technical measures are concretely and appropriately implemented.

4. Inspect whether the above-mentioned processes, procedures, and technical measures are monitored against sets of efficacy and efficiency metrics / indicators.

5. Inspect whether the above-mentioned processes, procedures, and technical measures are periodically reviewed and updated by responsible parties.

Standards mappings

ISO 42001No Gap

8.8 Management of technical vulnerabilities (27001)
A.6.2.6 AI system operation and monitoring (42001)

Addendum

N/A

EU AI ActNo Gap

Article 9 (2)
Article 72
Annex IV (3)

Addendum

Specify "at least monthly."

NIST AI 600-1No Gap

MP-5.1-005
MS-4.2-001

Addendum

N/A

BSI AIC4Partial Gap

C4 SR-01
C5 OPS-22

Addendum

BSI C4 SR-01 supports vulnerability scanning but with quarterly cadence, while TVM-07 recommends a monthly time period.

AI-CAIQ questions (1)

TVM-07.1

Are processes, procedures, and technical measures defined, implemented, and evaluated based on identified risks to support scheduled and emergency responses to vulnerability identification?