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Brain-computer interfaces (BCIs) are a means of communicating between the computer and the human brain. They are being used in healthcare, assistive devices, and human-machine interaction. While there are many benefits to using a BCI, there are also some serious concerns about security and privacy. Brain data is very sensitive. Once it is exposed, it can neither be altered nor replaced. Because of this, it is very important that neural data is protected. Most existing BCI security systems employ machine learning or static rules. These methods are able to detect pattern, but they often work like a black box. They are difficult to understand, very easy to mix up with attacks, and have little power in enforcing privacy rules. This restricts their reliability in real world BCI systems. This review examines potential neuro-symbolic AI agent use to enhance cybersecurity and privacy in BCIs. Neuro-symbolic AI is a combination of neural networks and symbolic rules and logic. This enables systems to learn from data while also adhering to strict security and privacy rules. The paper summarizes for common security and privacy threats in BCI systems and compares different AI approaches. It also explains how neuro-symbolic agents can help to support safer, more transparent, and more trustworthy BCI systems. In addition, the review covers privacy-preserving techniques, such as federated learning and differential privacy. Ethical and legal concerns concerning the protection of brain data are also presented. Open challenges and future research directions are discussed at the end of the paper.