Drift Detection in AI Systems
These documents examine how AI systems and the organizations around them can remain operational while losing alignment with real-world conditions, user intent, and intended outcomes. They extend drift detection beyond technical metrics to include changes in behavior, meaning, workflows, governance, and feedback.
Detecting Silent Model Drift in LLM Systems (PDF)
Explains how large language models degrade without triggering metric failures, producing outputs that remain fluent but lose alignment with intent, context, and usefulness.
Drift Audit Checklist (AI Systems) (PDF)
A practical checklist for identifying drift across data, performance, behavioral, semantic, and system layers in production AI systems.
Model Drift Detection Framework (PDF)
A structured framework for detecting and evaluating model drift across statistical, behavioral, and semantic layers, including methods for monitoring and mitigation.
AI Governance Readiness Checklist: Drift Detection & Semantic Fidelity (PDF)
A practical governance checklist for evaluating whether organizations can detect and correct AI drift after deployment, including purpose, monitoring, semantic fidelity, feedback, observability, and workflow readiness.
Institutional Drift Detection Framework (PDF)
A diagnostic framework for identifying drift across input, metric, process, behavioral, and system layers, with an audit checklist for detecting misalignment before it becomes visible failure.