Developing a Lightweight On-Device LLM Agent for Real-Time Privacy Monitoring in Resource-Conscious POS Systems

Small and medium-sized businesses (SMBs) rely on point-of-sale (POS) systems to process transactions and protect customer data but often lack the resources for robust privacy monitoring, increasing the risk of compliance failures and data breaches. This study presents a resource-efficient framework that uses lightweight Large Language Model (LLM) agent for real-time, on-device privacy monitoring, reducing cloud dependency and operational costs. The framework combines modular components, optimized anomaly-detection algorithms, and storage-aware preprocessing to minimize computational overhead while maintaining compliance readiness. Benchmarking on commodity POS hardware (8 GB RAM, 2.4 GHz CPU) shows a 120 MB memory footprint, 1.2-second latency per 1,000 transactions, and linear scalability up to 10,000 records. These results demonstrate the feasibility of near-real-time monitoring under strict resource constraints. Framework aligns with the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR), offering SMBs a practical path to improved data protection and customer trust. Future work will explore real-world deployments, adaptive compliance layers, and hybrid anomaly-detection strategies.