Risks, Controls & Evidence

Evidence can be correct at the source and still become unreliable after parsing, normalization, chunking, retrieval and context assembly. The Evidence Preservation Chain tracks whether identity, provenance, scope and meaning survive the entire AI pipeline.

Not all supported answers are equally close to their evidence. Evidence Distance is a practical way to describe how many transformations, summaries, assumptions and inferential steps separate an AI claim from directly inspectable evidence.

AI governance defines who can approve, operate, change and audit AI systems across models, providers, data, permissions, risk, evaluation and the full lifecycle.

ADR vs NFR explained: learn how system quality requirements drive architecture decisions, how ADRs record trade-offs, and why validation stays separate.

Generative AI is more than a model. Learn how models, retrieval, tools, context, runtimes and applications fit together in production AI systems.