Self-Auditing Deep Learning Pipelines for Automated Compliance Validation with Explainability, Traceability, and Regulatory Assurance

Abstract

Deep learning is increasingly deployed in regulated domains such as healthcare, finance, insurance, and public services, where AI systems must satisfy requirements for privacy, security, fairness, transparency, and accountability. However, compliance assurance in most AI/ML pipelines remains manual, intermittent, and difficult to reproduce, creating gaps when data, features, and model versions change rapidly through continuous retraining and deployment. This paper proposes a self-auditing deep learning pipeline that automates compliance validation across the full ML lifecycle data ingestion, preprocessing, feature engineering, training, evaluation, deployment, and post-deployment monitoring while generating regulator-ready evidence by design. The approach integrates policy-as-code controls to encode governance rules as executable checks, continuous audit hooks to capture tamper-evident logs of datasets, code, configurations, and approvals, and end-to-end lineage to link inputs, transformations, model artifacts, and decisions into a traceability graph. To address transparency expectations, the architecture includes an explainability-driven validation layer that produces standardized explanation artifacts and reason codes, monitors explanation stability across model updates, and flags potential reliance on sensitive attributes. A continuous risk-scoring mechanism aggregates signals from privacy, security, data quality, drift, bias, and explainability to detect violations early and trigger remediation or release blocking. Overall, the proposed framework improves repeatability, reduces human error, and strengthens audit readiness by making compliance measurable, continuous, and reconstructable for every model version