<p>Digital information platforms driven by artificial intelligence face the dual risks of privacy leakage and network attacks in the process of processing sensitive data, mainly due to the high complexity of AI (Artificial Intelligence) models, the high-frequency dynamic characteristics of data interaction, and the lag of traditional security mechanisms in responding to new threats. In response to this challenge, this paper proposes a set of systematic privacy protection and network security reinforcement strategies with full life cycle characteristics, and constructs a three-layer collaborative protection architecture with “artificial intelligence as the leader and security mechanism as the support”. In the basic security layer, relying on the behavioral analysis capabilities of artificial intelligence, the access control engine is linked to dynamically configure access policies to build an end-to-end dynamic access control system. In the privacy protection layer, a differential privacy mechanism with dynamic noise control is embedded in AI model training to suppress model inversion and membership inference attacks. In the network defense layer, an AI intrusion detection system based on deep learning is constructed, which integrates time series modeling and anomaly reconstruction methods, while ensuring the traceability of attack paths and the credibility of detection results through blockchain mechanisms. The above three-layer protection system is supplemented by multi-layer encryption, a data exchange mechanism constrained by smart contracts, and regular vulnerability scanning and risk modeling. Experimental results show that when facing the command injection attack, the accuracy of the above model can achieve 94.1%. Experimental results also show that the overall reinforcement strategy composed of AI active defense, differential privacy perturbation, and blockchain trust mechanism can construct an evolvable, auditable and trustworthy intelligent security system for digital information platform. The whole narrative style of this study follows from the problem formulation to systematic design, experimental evaluation and discussion, which guarantee that readers can benefit from following the narrative from quantitative framework to experimental verification.</p>