Compliance monitoring in modern enterprises requires simultaneous adherence to multiple regulatory frameworks (ISO 27001, GDPR, SOC 2, HIPAA, PCI-DSS), yet existing Security Information and Event Management (SIEM) systems lack cross-framework mapping capabilities, explainability, and real-time processing. We present an explainable AI-powered compliance audit system that addresses these limitations by employing a novel architecture that combines knowledge graphs, transformer-based natural language models, and cryptographically signed audit trails. Our system provides real-time monitoring across five compliance frameworks, generates multi-audience explanations (technical, legal, executive), and maintains complete traceability of decisions. Evaluation on 100,000+ events from the LANL Unified Host and Network Dataset demonstrates 89% accuracy in compliance evaluation, 87% accuracy in control mapping, and end-to-end latency of 387ms (p50) on dedicated hardware (Intel Core i7-11700K, 32GB RAM). The system detected 1,247 compliance violations during a 30-day deployment, with 91.5% precision and an 8.5% false-positive rate. Our cross-framework knowledge graph reveals that 34% of applicable controls would be missed without unified mapping. We note that the LANL dataset evaluates the pipeline’s event-processing and rule-evaluation mechanics on authentication telemetry; validation on data-rich environments containing PII, PHI, or payment-card data remains necessary to confirm framework-specific detection accuracy. This work enables proactive compliance management and demonstrates a practical architecture for multi-regulatory compliance monitoring with built-in explainability.