Intelligent Data Contamination Prevention Framework For AI Training Data Poisoning Detection And Mitigation

Abstract

The integrity of training data stands as a foundational requirement for trustworthy artificial intelligence systems deployed at enterprise scale. As organizations integrate machine learning into high-stakes decision processes — spanning fraud detection, clinical decision support, and autonomous systems — the attack surface has shifted from model architecture to data supply chains. This paper presents the Intelligent Data Contamination Prevention (IDCP) Framework, a multi-layer defensive architecture designed to detect and mitigate data poisoning attacks across enterprise AI training pipelines. The threat landscape addressed includes clean-label attacks [5], backdoor and trojan injection [7][4], gradient-matching poisoning [12], federated learning vulnerabilities [15], and concealed natural language processing (NLP) poisoning [6]. The IDCP Framework operates across three coordinated layers: a Data Ingestion and Provenance Layer that enforces cryptographic lineage tracking, a Statistical Anomaly Detection Layer that applies gradient-space analysis, spectral signatures, and ensemble voting, and a Behavioral Validation Layer that uses activation clustering, perturbation-based consistency testing, and reverse-engineering scans of model checkpoints. The framework contribution is architectural: an integrated, pipeline-agnostic design that synthesizes published detection methods into a unified enterprise defense. The framework aligns with NIST AI 100-2 [10] and incorporates automated Datasheets for Datasets [21] generation, providing a standards-compliant governance path for regulated industries.