Data Privacy and Information Protection

The system operator SHALL maintain a documented AI input data policy specifying how customer, user, and third-party data is used for training, fine-tuning, inference, and caching — including opt-in/opt-out mechanisms and retention schedules.

The system operator SHALL maintain a documented AI output data policy specifying ownership of generated outputs, usage rights, downstream restrictions, and deletion procedures.

The system SHALL classify all data it processes according to a documented sensitivity taxonomy (e.g., public, internal, confidential, restricted) and apply handling controls proportional to classification level.

The system operator SHALL maintain a documented data flow map identifying all data ingress, processing, storage, and egress points — including third-party services, caching layers, and telemetry pipelines.

The system SHALL limit data collection and retention to the minimum necessary for the authorized task, enforced by configurable data access scoping tied to user roles and operational context.

The system SHALL support user consent management including opt-out mechanisms for data usage, data portability requests, and verifiable deletion of user-specific data.

The system SHALL enforce documented data retention schedules with automated purging and SHALL demonstrate verifiable deletion of expired data including from caches, logs, and backup systems.

The system SHALL provide clear disclosure to end users when they are interacting with an AI system rather than a human, except where the AI nature is already obvious from context.

The system SHALL implement automated detection and prevention of personally identifiable information in outputs, logs, telemetry, cached data, and error messages, with configurable sensitivity thresholds.

In shared or multi-customer environments, the system SHALL enforce strict isolation preventing any customer's data from appearing in another customer's inputs, outputs, context, or model state.

The system SHALL prevent data from prior sessions, conversations, or user contexts from leaking into subsequent interactions with different users or in different operational contexts.

Operational logs, telemetry streams, and monitoring data SHALL be sanitized to prevent exposure of sensitive input data, PII, or confidential business information.

The system SHALL enforce security controls on all caching mechanisms, temporary storage, and intermediate processing buffers to prevent data exposure through side-channel or timing attacks.

The system SHALL implement safeguards to prevent leakage of intellectual property, trade secrets, and confidential business information through outputs, logs, telemetry, or error messages.

The system SHALL implement controls to prevent outputs from reproducing copyrighted works, generating trademark-infringing content, or violating third-party intellectual property rights.

The system operator SHALL maintain documentation of training data sources, licensing status, consent mechanisms, and provenance chain for all data used in model training and fine-tuning.

The system SHALL implement controls to detect and mitigate training data contamination, poisoning, and integrity violations that could compromise data privacy protections.

The system SHALL resist adversarial attempts to extract training data, system prompts, or confidential information through prompt injection, context manipulation, or repeated querying patterns.

The system operator SHALL document data processing locations, data residency compliance, subprocessor relationships, and cross-border data transfer mechanisms.

The system SHALL enforce data residency requirements and cross-border transfer restrictions as defined in the system's data processing documentation and applicable regulatory requirements.

The system SHALL support data subject rights including access, rectification, erasure, portability, and restriction of processing as required by applicable privacy regulations.

The system operator SHALL maintain a documented Data Privacy Impact Assessment (DPIA) identifying privacy risks, mitigation measures, and residual risk acceptance for the autonomous system.

The system operator SHALL maintain vendor due diligence documentation for upstream model and data providers covering their data handling, PII controls, training data sourcing, and subprocessor relationships.

The system SHALL maintain data breach detection capabilities and the operator SHALL maintain documented notification procedures for data incidents affecting personal or confidential information.