Research on a collaborative model for artificial intelligence data security based on blockchain trust mechanisms

The rapid advancement of artificial intelligence (AI) and intelligent algorithms, particularly in data processing, decision support, and automated control, has become a core driving force behind modern technological progress. However, with the widespread adoption of these technologies, data security and privacy protection issues have gradually emerged, especially within industrial internet of things (IIoT) and cloud computing environments. To address these challenges, blockchain technology, with its decentralized, transparent, and immutable characteristics, has increasingly become a vital means of ensuring data security. This study explores a blockchain-based collaborative AI data security model, proposing a multilayered security architecture that integrates blockchain trust mechanisms with AI-driven optimization. By designing a system that combines node behavior evaluation, data encryption, and smart contracts, it enhances the security, reliability, and transparency of data transmission. The research employs an experimental platform and multiple task datasets for validation. Test results demonstrate that the model achieves significant performance improvements in high-concurrency, high-load scenarios, effectively reducing data tampering, enhancing trust evaluation accuracy, and optimizing collaborative efficiency. This study offers new insights for blockchain-AI integration applications and holds significant implications for advancing intelligent data management and security protection in the future.