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

翻译：现代组织日益面临由人类行为而非技术故障驱动的网络安全事件。为此，我们提出了一种概念性安全框架，该框架集成了一种混合卷积神经网络-长短期记忆（CNN-LSTM）模型，用于分析生物特征与环境数据，以做出情境感知的安全决策。CNN从传感器数据中提取空间模式，而LSTM则捕捉与人为错误易感性相关的时间动态。该模型达到了84%的准确率，证明了其能够可靠地检测导致人本网络风险升高的条件。通过实现持续监控和自适应防护，该框架支持主动干预，从而降低人为驱动网络事件发生的可能性。