Black-box foundation models produce outputs without traceable rationale, leaving the firm unable to justify AI-driven decisions to regulators, stakeholders, or customers and allowing latent errors or biases to go undetected; observability and human oversight are the principal mitigating surfaces.
AI/ML / Multi Agent Refarch / Threats / DEV
Lack of explainability and traceable rationale
CCC.MARefArc.TH24
Related Capabilities
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CP23 | Cross-layer telemetry collection | Captures logs, traces, metrics, and events emitted by every layer to support debugging, auditability, distributed tracing, and operational monitoring across the request lifecycle. |
| CCC.MARefArc.CP25 | Signal correlation | Correlates signals across logs, traces, metrics, and events into a unified view, connecting symptoms to root causes across cross-layer dependencies. |
| CCC.MARefArc.CP21 | Human supervision and oversight | Mechanisms for human reviewers to inspect, approve, correct, or override agent outputs, supporting human-in-the-loop and human-over-the-loop workflows for sensitive or high-impact tasks. |
Related Controls
| ID | Title | Description |
|---|---|---|
| CCC.MARefArc.CN20 | Citations and Source Traceability for AI-Generated Information | Attach citations and source traceability to AI-generated information so that outputs can be verified against retrieved sources and decisions can be explained. |
External Mappings
| Framework | ID | Remarks |
|---|---|---|
| air-vec | AIR-OP-017 |