Artificial Intelligence-based Financial Big Data Information Security and Local Risk Prevention and Control

As a new product of artificial intelligence, big data is widely used in daily life. Due to its appearance, people’s lives are more convenient and efficient, but at the same time, there are certain security risks, namely the leakage of private information, especially the financial information problem brought about by financial informatization has a more serious leakage problem. In order to effectively reduce the problems caused by the leakage of financial information privacy, this paper attempted to establish a model of related protection measures for financial big data information security by establishing a three-dimensional encrypted information model of big data or by using differential privacy method and using their own. The three-dimensional encrypted information model of big data overcame the defect that financial information is easy to be broken, while the differential privacy model overcame the defect of inaccurate protection of financial information, both of which can play a better protective role in different applications. The experimental results showed that in the process of accessing financial data information, with the increase of access frequency, the number of sensitive locations changes from 40 to 46. This also meat that a non-sensitive position becomes a sensitive position, which blurs the original sensitive position and achieves the effect of protecting the real sensitive position.