Legal Challenges to Personal Information Security in the Big Data Era and AI Regulatory Approaches

In the era of big data, personal information security faces systemic challenges triggered by technological iteration, necessitating the reconstruction of a collaborative governance paradigm between law and technology. This paper reveals that personal information processing exhibits new characteristics: non-intrusive data harvesting, precise prediction, and large-scale abuse. Against this backdrop, the traditional legal framework, centred on "informed consent," encounters structural dilemmas such as subject abstraction, ambiguous liability, and regulatory lag. To address this, this paper proposes an AI-driven regulatory technology implementation pathway: establishing preemptive automated assessment mechanisms for data collection legitimacy using deep neural networks; deploying sensor networks for real-time anomaly detection in data streams under Federated Learning Frameworks; and introducing cross-chain innovative contract technology to enable cross-domain, chain-traversing provenance analysis and evidence collection for infringement activities. This system transforms legal rules into programmable regulatory nodes, forming a dynamic, closed-loop intelligent oversight mechanism with end-to-end traceability. It provides a technology-enabled institutional safeguard to balance the security of data flow with innovation development.