I&S-02Cloud & AI Related

Capacity and Resource Planning

Specification

Plan and monitor the availability, quality, and adequate capacity of resources in order to deliver the required system performance as determined by the business.

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 storage, Resource provisioning

Development

Training

Evaluation

Re-evaluation, Evaluation

Deployment

Orchestration, AI Services supply chain, AI applications

Delivery

Operations, Continuous monitoring, Continuous improvement

Retirement

Not applicable

Ownership / SSRM

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

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.

Implementation guidelines

[All Actors]
1. Define Resource Planning Framework.

2. Establish a structured approach for monitoring and managing infrastructure capacity.

3. Align with best practices (ISO 27001, NIST SP 800-53, ITIL Capacity Management).

4. Capacity Forecasting and Performance Monitoring.

5. Implement predictive analytics to anticipate resource needs.

6. Define KPIs for utilization and performance benchmarks.

7. Scalability and Resiliency Planning.

8. Define strategies for scaling resources based on workload demands.

9. Implement automated scaling policies for cloud and virtualized environments.

10. Incident Response and Contingency Planning.

11. Define procedures for handling resource exhaustion and unexpected demand surges.

Auditing guidelines

1. Examine the Cloud Service Provider's business requirements for system performance are available. 

2. Verify capacity plans, performance forecasts, and scaling procedures are review and approve by senior management or governance authorities.

3. Verify performance metrics regularly, proactively identify potential capacity constraints, and verify compliance with agreed-upon service levels. 

4. Verify performance planning procedures regularly review, at least annually and align with changing business demands, system performance metrics, emerging technologies, and evolving threats.

Standards mappings

ISO 42001No Gap

ISO/IEC 42001:2023 - B.4.2

Addendum

N/A

EU AI ActFull Gap

No Mapping

Addendum

Full control would have to be added because the EU AI Act does not address these concerns. Add, "Plan and monitor the availability, quality, and adequate capacity of resources in order to deliver the required system performance as determined by the business."

NIST AI 600-1Full Gap

No Mapping

Addendum

NIST AI 600-1 is missing the operational, performance, and resource planning focus required by I&S-02.

BSI AIC4No Gap

C4 RE-01
C4 BC-03
C4 PF-01
C5 OPS-01
C5 OPS-02

Addendum

N/A

AI-CAIQ questions (1)

I&S-02.1

Are availability, quality and the adequate capacity of resources, being planned and monitored in order to deliver the required system performance as determined by the business?