Audit-Ready XAI Engineering via Evidence Bundles and Trace-Link Validation

Abstract

Auditability in high-risk AI requires more than explanation narratives: reviewers must be able to retrieve and verify decision-bound evidence, including the decision record, model/configuration state, explanation artefact, and audit-log event. We propose a lightweight engineering pattern for auditready Explainable AI (XAI) that (i) packages each decision into a compact evidence bundle, (ii) exposes resolvable trace-links to an evidence store, and (iii) validates structural audit readiness through objective, tool-agnostic checks. The check suite covers both run- and decision-level properties, such as evidence presence, bundle completeness, decision coverage, pinning/provenance coverage, trace-link resolution, and audit-log soundness, and can be layered on top of standard Machine Learning Operations (MLOps) tooling. We demonstrate feasibility on a reproducible mini-case (seed-controlled synthetic intrusion detection system (IDS) anomaly detection) and show how the checks support fast localisation of common audit gaps, including missing bindings, stale links, inconsistent pins, and auditor role-based access control (RBAC) resolution failures. In an audited scope of $N=30$ decisions, all required artefacts were present and independently retrievable under an auditor-equivalent access profile, enabling verifiable navigation from decisions to evidence.