Data Security

L1 · Initial

AI data repositories (training data, vector stores, RAG sources) rely on general-purpose data security controls. No differentiation between AI and other data assets. Data provenance for training and fine-tuning for self-trained models not tracked. Vector databases deployed with default access controls. No validation of data sources used for grounding or retrieval.

L2 · Repeatable

AI-specific data risks identified (poisoning, RAG leakage). Training and fine-tuning data sources documented for critical models. Basic access controls on vector databases. Basic validation (source approval, format checks) for external sources used in RAG. Sensitive data handling requirements documented for AI contexts but inconsistently applied.

L3 · Defined

AI data security requirements documented by data type (training, fine-tuning, RAG, embeddings). Data ingestion restricted to approved, documented sources with format validation before use. Vector databases use tenant or application isolation to prevent cross-context access. Sensitive data filtering applied to training and grounding sources (e.g. PII scanning). Initial data versioning for critical AI systems.

L4 · Capable

Data sources and transformations documented and auditable for production AI systems. Automated data validation integrated into AI pipelines where applicable. Permission-aware retrieval implemented for RAG tool calls (query results filtered based on user access to source data). Comprehensive data versioning with ability to trace data changes over time.

L5 · Efficient

Data lineage integrated with model inventory for end-to-end traceability (for training). Permission-aware retrieval standard across RAG deployments. Scanning for detection of potential data poisoning or adversarial patterns in training/RAG. AI data security controls updated proactively as new attack vectors emerge.