Abstract: The burgeoning field of Brain–Computer Interfaces (BCIs) and the emerging concept of Brain-to-Brain Interfaces (BBIs) necessitate robust, low-latency, and highly secure neural data transmission architectures. The inherent sensitivity of neural data, coupled with the stringent real-time requirements of interactive BCI systems, presents formidable challenges to current communication paradigms. Existing neural communication methods often lack standardized protocols, exhibit vulnerabilities to cyber threats, and struggle with scalability for multi-user or global applications. This paper introduces the Neuroba Neural Transmission Security Architecture (NNTSA), a novel conceptual framework designed to address these critical issues. NNTSA integrates advanced neural data encoding, efficient compression and packetization, cutting-edge secure encryption (including post-quantum cryptographic considerations), and optimized low-latency transmission mechanisms, complemented by robust integrity verification and reconstruction layers. The proposed architecture aims to safeguard the privacy and integrity of neural information while ensuring the ultra-low latency essential for seamless brain-to-device and brain-to-brain interactions. Key contributions include a modular system design, mathematical formulations for latency optimization and encryption, and a comprehensive security analysis. While NNTSA offers a significant theoretical advancement, its practical implementation faces challenges related to hardware limitations, network latency trade-offs, and the evolving landscape of privacy regulations. Future work will explore quantum neural communication networks and AI-driven adaptive security systems to further enhance the capabilities of the Neuroba NCTS Framework.