Inaccurate, incomplete, outdated, or biased grounding and training data lead to unreliable outputs, while data and concept drift erodes predictive power over time and amplifies historical errors at scale.
AI/ML / Multi Agent Refarch / Threats / DEV
Poor-quality, drifting, and bias-amplifying data
CCC.MARefArc.TH22
Related Capabilities
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CP12 | Authoritative knowledge source bases | Internal and external repositories of structured data, unstructured documents, and graph-based representations that provide authoritative information for grounding. |
| CCC.MARefArc.CP13 | Vector-based semantic retrieval | Vector databases providing semantic search and grounding so agents can find relevant information from large text corpora. |
| CCC.MARefArc.CP14 | Approved-model registry and lifecycle | Catalog of approved models with metadata, version information, configuration parameters, and usage constraints, ensuring agents access only models meeting organizational, regulatory, and security standards. |
Related Controls
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CN04 | Data Quality and Classification | Assess the quality of, and assign classification and sensitivity labels to, all data used for grounding, training, and fine-tuning, and enforce handling rules derived from those labels throughout the Knowledge and LLM layers. |
| CCC.MARefArc.CN17 | AI System Observability | Instrument every layer to emit logs, traces, metrics, and events to the Observability Layer so that behaviour, drift, availability, and data handling are continuously visible and auditable. |
| CCC.MARefArc.CN21 | Automated Evaluation Using LLM-as-a-Judge | Use automated model-based evaluation in the Evaluation Layer to assess output quality, grounding, bias, and policy compliance at scale. |
External Mappings
| Framework | ID | Remarks |
|---|---|---|
| air-vec | AIR-OP-019-01 | |
| air-vec | AIR-OP-019-02 | |
| air-vec | AIR-OP-019-03 |