IAM-17Cloud & AI Related

Knowledge Access Control - Need to Know

Specification

Define policy and procedure for "need to know" access to knowledge, information and data within the organization and in the context of the AI system to be applied when regulating access to resources.

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, Supply Chain

Evaluation

Validation/Red Teaming, Evaluation, 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

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.

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. Define the methods of data and information classification, Tools such as a matrix can be 
helpful.

2. Define and implement the method of assigning and tracking information ownership/stewardship.

3. Implement approval procedures for accessing that information (either permanent access to 
or time-limited access).

4. Implement data lineage tools to track the collection, transformation and use of data in MLops 
to better assign classification to models.

5. Regularly review permissions to data, and have data owners certify access configurations. 

6. Ensure tools accessible to Agent-based AI systems are classified and tagged based on the sensitivity of data 
they have access to.

7. Implement access control mechanisms in agent-based AI systems to ensure access to information is derived from a users credential and limited to their roles need-to-know.

8. Implement principle of least privilege to avoid the violation of "need-to-know" principle.

Auditing guidelines

1. Verify platform-level RBAC or ABAC policies are configured to enforce knowledge access limitations.

2. Check tenant-level isolation controls on data lake, object storage, and model registry.

3. Confirm logging and monitoring of access attempts to AI knowledge resources like datasets and features.

4. Validate cloud-native services (e.g., Vertex AI, SageMaker) restrict access to metadata and pipeline artifacts.

5. Ensure policies align with CSP customer’s "least privilege" configuration templates for AI workloads.

Standards mappings

ISO 42001No Gap

42001 B.3.2 - AI roles and responsibilities
27001 A.8.3 - Information access restriction

Addendum

N/A

EU AI ActPartial Gap

Article 9
Article 11
Article 15
Article 16
Article 29

Addendum

The AICM IAM-17 control is partially covered in the EU AI Act, with strong alignment on risk, security, and incident management, but only partial coverage regarding quality management and post-market monitoring.

NIST AI 600-1Full Gap

No Mapping

Addendum

No (explicit/implicit) reference to the requirement set by the AICM control is made in the NIST AI 600-1 standard.

BSI AIC4No Gap

C4 DM-01
C4 DM-02
C5 PSS-08

Addendum

N/A

AI-CAIQ questions (1)

IAM-17.1

Are policies and procedures defined for "need to know" access to knowledge, information and data within the organization and in the context of the AI system to be applied when regulating access to resources?