Dear Editors, Please consider our manuscript, “Bounded Neural Authority: A Neuro-Symbolic Architecture for Detection and Response in Security Operations,” for publication as a Regular Paper in Neurosymbolic Artificial Intelligence. The paper addresses a practical question that we believe has received less attention than the development of new neuro-symbolic models themselves. How should neural and symbolic capabilities be introduced into an existing production security operations environment without allowing learned models to assume inappropriate authority over consequential decisions? We propose NS-D&R, a neuro-symbolic architecture for detection and response built around the principle of bounded neural authority. Neural components are used where their strengths are most useful, including representation, similarity, anomaly detection, generalisation, and hypothesis generation. Symbolic and deterministic mechanisms retain authority over validation, policy enforcement, scoring constraints, alerting, and other production decisions. The architecture is designed to augment rather than replace existing detection pipelines, allowing organisations to introduce learned capabilities while retaining explicit controls, auditability, human oversight, and deterministic fallback. The manuscript develops this architecture across the detection and response lifecycle, including enrichment, risk calibration, graph persistence, clustering, response support, governance, and evaluation. It also provides formal constraints for bounded confidence calibration and neural relationship proposals, together with an adoption roadmap intended to make the architecture applicable to existing security operations environments. We believe the manuscript is a good fit for Neurosymbolic Artificial Intelligence because its central concern is not simply the application of machine learning to cybersecurity. Rather, it examines the division of responsibility between neural and symbolic systems and how that division can be made explicit in an operational architecture. The cybersecurity setting provides a particularly useful environment in which to examine this problem because model outputs can ultimately influence decisions with significant operational consequences. This manuscript is original, has not been published previously, and is not currently under consideration for publication elsewhere. Both authors have reviewed and approved the manuscript and agree with its submission to the journal. There are no competing interests or sources of funding to declare, and no research data were generated or analysed as part of this work. The work did not involve human participants or animals and therefore did not require ethics approval or participant consent. The use of generative AI during manuscript preparation has been disclosed in the accompanying title page in accordance with the journal and publisher requirements. The authors remain responsible for the architecture, arguments, source verification, technical content, and final manuscript. Vishal Thakur is the corresponding author and can be reached at vischalthakur@gmail.com. Thank you for considering our manuscript. We hope that it will contribute to the discussion around how neuro-symbolic systems can move from individual techniques and experimental models toward controlled deployment in operational environments. Kind regards, Vishal Thakur Principal Architect, PR3TACK.org Mukesh Singh Research Fellow, PR3TACK.org