Modern organizations increasingly face cybersecurity incidents driven by human behaviour rather than technical failures. To address this, we propose a conceptual security framework that integrates a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model to analyze biometric and environmental data for context-aware security decisions. The CNN extracts spatial patterns from sensor data, while the LSTM captures temporal dynamics associated with human error susceptibility. The model achieves 84% accuracy, demonstrating its ability to reliably detect conditions that lead to elevated human-centred cyber risk. By enabling continuous monitoring and adaptive safeguards, the framework supports proactive interventions that reduce the likelihood of human-driven cyber incidents
@article{arxiv.2602.19410,
title = {BioEnvSense: A Human-Centred Security Framework for Preventing Behaviour-Driven Cyber Incidents},
author = {Duy Anh Ta and Farnaz Farid and Farhad Ahamed and Ala Al-Areqi and Robert Beutel and Tamara Watson and Alana Maurushat},
journal= {arXiv preprint arXiv:2602.19410},
year = {2026}
}