Securing AI pipelines against data poisoning: a practical guide for technical teams

This guide explains how to secure AI pipelines against data poisoning by mapping trust boundaries, enforcing ingestion and provenance controls (allowlisting, schema validation, provenance metadata), applying dataset versioning and signed artefacts, quarantining suspicious inputs, monitoring drift and label quality, and using approval gates and canary evaluations for safe model promotion; it targets practical, low-friction measures for SMEs and recommends operational runbooks for triage, rollback, and governance improvements.