AIUC-1 Standard

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A. Data & Privacy

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A005. Prevent cross-customer data exposure

A005

Prevent cross-customer data exposure

Implement safeguards to prevent cross-customer data exposure when combining customer data from multiple sources

Keywords

Cross-Customer Data

Model Training

Data Rights

Application

Mandatory

Frequency

Every 12 months

Type

Preventative

Crosswalks

MEASURE 2.10: Privacy risk assessment

LLM02:25 - Sensitive Information Disclosure

LLM05:25 - Improper Output Handling

LLM08:25 - Vector and Embedding Weaknesses

I&S-06: Segmentation and Segregation

DSP-22: Privacy Enhancing Technologies

UEM-08: Storage Encryption

Control activities

Typical evidence

Establishing explicit consent and disclosure for combined data usage. For example, informing customers when their data will be combined with competitor data, disclosing data anonymization and abstraction policies, providing opt-out mechanisms.

A005.1 Documentation: Consent for combined data usage

Typically demonstrated by Data Processing Agreement or Terms of Service

Category

Data Processing AgreementTerms of Service

Universal

Implementing customer data isolation controls. For example, enforcing strict logical and physical separation of customer data, applying tenant-specific encryption, validating data flow boundaries in shared infrastructure, establishing technical barriers between customer datasets during training.

A005.2 Config: Customer data isolation controls

Screenshot showing app_IDs in database schema, screenshot showing that namespace by appID is used in vector store for RAG or that logical isolation is implemented in an equivalent way, or screenshot of authz check in code verifying appIDs match before returning objects.

Category

Technical Implementation

Engineering Code

Universal

Implementing specific privacy-enhancing technologies (PETs) to reduce competitive exposure.

A005.3 Config: Privacy-enhancing controls

May include tokenization, hashing, or anonymization techniques (robust to prevent re-identification or reversal) making data algorithmic-usable but not human-readable, differential privacy implementation obfuscating individual contributions, federated learning configuration avoiding centralized raw data, or data masking/pseudonymization protecting customer identities.

Category

Technical Implementation

Engineering Code

Universal

Organizations can submit alternative evidence demonstrating how they meet the requirement.

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Head of Trust for SecurityPal and former CISO of MongoDB