The application relates to the technical field of
computer security, in particular to a zero-trust
security system and control method based on bidirectional
authentication of subjects and data, which comprises a decentralized
identity management module, which is used for generating decentralized digital identities for access subjects and data assets, and bidirectionally verifying the identities of the access subjects and the data assets; an AI dynamic trust evaluation module, which is used for continuously collecting multidimensional real-
time data streams, and generating a dynamically updated comprehensive
trust score through a
deep learning model; a dynamic strategy generation and execution module, which is used for generating real-time
access control strategies based on the comprehensive
trust score and data asset attributes, and executing the real-time
access control strategies; and a closed-loop feedback optimization module, which is used for dynamically optimizing parameters of the
deep learning model through a
reinforcement learning algorithm according to
strategy execution effect data. Through bidirectional decentralized
authentication of subjects and data, AI dynamic trust evaluation and
reinforcement learning closed-
loop optimization, the application realizes
safer, smarter and more flexible fine-grained
access control.